5. Conference Proceedings

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CONFERENCE PRESENTATIONS AND PUBLICATIONS (301)

2016 (13)

  1. H.C. Li and C.-I Chang, “Real-time hyperspectral anomaly detection via band-interleaved by line,” Remotely Sensed Data Compression, Communications, and Processing XII, part of SPIE Commercial + Scientific Sensing and Imaging, 17-21 April, 2016.
  2. H.C. Li and C.-I Chang, “Geometric convex cone volume analysis,” Remotely Sensed Data Compression, Communications, and Processing XII, part of SPIE Commercial + Scientific Sensing and Imaging, 17-21 April, 2016.
  3. B. Xue, L. Wang, H.C. Li and C.-I Chang, “Lesion detection in magnetic resonance brain images by hyperspectral imaging algorithms,” Remotely Sensed Data Compression, Communications, and Processing XII, part of SPIE Commercial + Scientific Sensing and Imaging, 17-21 April, 2016.
  4. L.C. Lee, C. Gao and C.-I Chang, “Hyperspectral analysis approach to prioritizing vital sign signals for Medical Data,” Remotely Sensed Data Compression, Communications, and Processing XII, part of SPIE Commercial + Scientific Sensing and Imaging, 17-21 April, 2016.
  5. C. Gao, L.C. Lee and C.-I Chang, “Progressive anomaly detection in medical data using vital sign signals,” Remotely Sensed Data Compression, Communications, and Processing XII, part of SPIE Commercial + Scientific Sensing and Imaging, 17-21 April, 2016.
  6. Y. Li and C.-I Chang, “Progressive band processing of fast iterative pixel purity index,” Remotely Sensed Data Compression, Communications, and Processing XII, part of SPIE Commercial + Scientific Sensing and Imaging, 17-21 April, 2016.
  7. B. Lampe and C.-I Chang, “Hyperspectral band selection using compressive sensing,” Remotely Sensed Data Compression, Communications, and Processing XII, part of SPIE Commercial + Scientific Sensing and Imaging, 17-21 April, 2016.
  8. A. Bekit and C.-I Chang, “Unsupervised hyperspectral unmixing using compressive sensing,” Remotely Sensed Data Compression, Communications, and Processing XII, part of SPIE Commercial + Scientific Sensing and Imaging, 17-21 April, 2016.
  9. H.C. Li, C.-I Chang and L. Wang, “Constrained multiple band selection for hyperspectral imagery,” 2016 IEEE Geoscience and Remote Sensing Symposium, Beijing, China, July 10-15, 2016.
  10. L. Wang and C.-I Chang, “Multiple band selection for anomaly detection in hyperspectral imagery,” 2016 IEEE Geoscience and Remote Sensing Symposium, Beijing, China, July 10-15, 2016.
  11. H.C. Li and C.-I Chang, “Geometric simplex growing algorithm for finding endmembers in hyperspectral imagery,” 2016 IEEE Geoscience and Remote Sensing Symposium, Beijing, China, July 10-15, 2016.
  12. L.C. Lee and C.-I Chang, “An information theoretical approach to multiple band selection for hyperspectral imagery,” 2016 IEEE Geoscience and Remote Sensing Symposium, Beijing, China, July 10-15, 2016.
  13. H.M. Chen, J.W. Chai, C.C.C. Chen, C. Song, P.C. Chung and C.-I Chang, “Semi-automatic hyperspectral magnetic resonance image classification of brain issues and white matter lesions,” Computer Vision and Graphics Image Processing (CVGIP), Keelung, Taiwan, August 15-17, 2016.

2015 (17)

  1. C.C. Wu, Y.-H. Liao, W.-S. Lo, H.-Y. Guo, C. Lin, C.-H. Wen, H.-M. Chen, Y.-C. Ouyang, C.-I Chang, “Band weighting spectral measurement for detection of pesticide residues using hyperspectral remote sensing,” International Geoscience and Remote Sensing Symposium 2015 (IGARSS 2015), Milan, Italy, July 26-31, 2015.
  2. H.-C. Li and C.-I Chang, “An orthogonal projection approach to simplex growing algorithm for finding endmembers in hyperspectral imagery,” 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, (WHISPERS), Tokyo, Japan, 2-5 June, 2015.
  3. H.-C. Li and C.-I Chang, “Linear spectral unmixing using least squares error, orthogonal projection and simplex volume for hyperspectral Images,” 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, (WHISPERS), Tokyo, Japan, 2-5 June, 2015.
  4. L.-C. Lee, D. Paylor and C.-I Chang, “Anomaly discrimination and classification for hyperspectral imagery,” 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, (WHISPERS),Tokyo,Japan, 2-5 June, 2015.
  5. C. Gao, S.-Y. Chen, H.M. Chen, C.C. Wu, C.H. Wen and C.-I Chang, “Fully abundance-constrained endmember finding for hyperspectral images,” 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, (WHISPERS), Tokyo, Japan, 2-5 June, 2015.
  6. Y. Li, C. Gao and C.-I Chang, “Progressive band processing of automatic target generation process,” 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, (WHISPERS),Tokyo,Japan, 2-5 June, 2015.
  7. S.-Y. Chen, Y.-C. Ouyang, C. Lin, H.-M. Chen, C. Gao and C.-I Chang, “Progressive endmember finding by fully constrained least squares method,” 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, (WHISPERS), Tokyo, Japan, 2-5 June, 2015.
  8. C.-I Chang, Y. Li and C.C. Wu, “Band detection in hyperspectral imagery by pixel purity index,” 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, (WHISPERS),Tokyo,Japan, 2-5 June, 2015.
  9. S.-Y. Chen, Y.-H. Liao, W.-S. Lo, H.-Y. Guo, T.-M. Chou, C.-H. Wen, C. Lin, H.-M. Chen, Y.-C. Ouyang, C.-C. Wu and Chein-I Chang, “Pesticide residue detection by hyperspectral imaging sensors,” 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, (WHISPERS),Tokyo,Japan, 2-5 June, 2015.
  10. C. Gao, Y. Li and C.-I Chang, “Finding endmember classes in hyperspectral imagery,” Satellite Data Compression, Communication and Processing XI (ST127), SPIE International Symposium on SPIE Sensing Technology + Applications, Proc. SPIE, no. 9501, pp. 95010M-1-95010M-11, Baltimore, MD, 20-24 April, 2015.
  11. C.-I Chang, L.-C. Lee and D. Paylor, “Virtual dimensionality analysis for hyperspectral imagery,” Satellite Data Compression, Communication and Processing XI (ST127), SPIE International Symposium on SPIE Sensing Technology + Applications, Proc. SPIE, no. 9501, pp. 95010R-1-95010R-11, Baltimore, MD, 20-24 April, 2015.
  12. H.-C. Li, M. Song and C.-I Chang, “Simplex volume analysis for finding endmembers in hyperspectral imagery,” Satellite Data Compression, Communication and Processing XI (ST127), SPIE International Symposium on SPIE Sensing Technology + Applications, Proc. SPIE, no. 9501, pp. 950107-1-950107-8, Baltimore, MD, 20-24 April, 2015.
  13. Y. Li, H.C. Li, C. Gao, M. Song and C.-I Chang, “Progressive band processing of pixel purity index for finding endmembers in hyperspectral imagery,” Satellite Data Compression, Communication and Processing XI (ST127), SPIE International Symposium on SPIE Sensing Technology + Applications, Proc. SPIE, no. 9501, pp. 95010U-1-95010U-10, Baltimore, MD, 20-24 April, 2015.
  14. H.C. Li, Y. Li, C. Gao, M. Song and C.-I Chang, “Progressive band processing of orthogonal subspace projection,” Satellite Data Compression, Communication and Processing XI (ST127), SPIE International Symposium on SPIE Sensing Technology + Applications, Proc. SPIE, no. 9501, pp. 95010F-1-95010F-8, Baltimore, MD, 20-24 April, 2015.
  15. M. Song, H.C. Li, C. Gao and C.-I Chang, “Orthogonal projection based fully constrained spectral unmixing,” SPIE International Symposium on SPIE Sensing Technology + Applications, Proc. SPIE, no. 9501, pp. 95010G-1-95010G-5, Baltimore, MD, 20-24 April, 2015.
  16. C. Gao, Y. Li, H.-C. Li, C.-I Chang, P. Hu and C. Mackenzie, “Hyperspectral vital sign signal analysis for medical data,” Satellite Data Compression, Communication and Processing XI (ST127), SPIE International Symposium on SPIE Sensing Technology + Applications, Proc. SPIE, no. 9501, pp. 950110-1-950110-7, Baltimore, MD, 20-24 April, 2015.
  17. Y.-H. Liao, W.-S. Lo, H.-Y. Guo,C.-H. Kao,T.-M. Chou,J.-J. Chen,C.-H. Wen, C. Lin, H.-M. Chen, Y.-C. Ouyang, C.-C. Wu, S.-Y. Chen and C.-I Chang, “Pesticide residue quantification analysis by hyperspectral imaging sensors,” Satellite Data Compression, Communication and Processing XI (ST127), SPIE International Symposium on SPIE Sensing Technology + Applications, Proc. SPIE, no. 9501, pp. 95010B-1-95010B-7, Baltimore, MD, 20-24 April, 2015.

2014 (13)

  1. S.Y. Chen, Y.C. Ouyang and C.-I Chang, “Recursive unsupervised fully constrained least squares methods,” 2014 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Quebec Canada, July 13-18, 2014.
  2. Y. Wang, C.H. Zhao and C.-I Chang, “Anomaly detection using sliding causal windows,” 2014 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Quebec Canada, July 13-18, 2014.
  3. L. Zhao, S.Y. Chen, M. Fan and C.-I Chang, “Endmember-specified virtual dimensionality in hyperspectral imagery,” 2014 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Quebec Canada, July 13-18, 2014.
  4. M. Song, H.C. Li, C.-I Chang and Y. Li, “Gram-Schmidt orthogonal vector projection for hyperspectral unmixing,” 2014 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 2934-2937, Quebec Canada, July 13-18, 2014.
  5. C. Gao and C.-I Chang, “Recursive automatic target generation process for unsupervised hyperspectral target detection,” 2014 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Quebec Canada, July 13-18, 2014.
  6. H.C. Li, M. Song and C.-I Chang, “Finding analytical solutions to abundance fully-constrained linear spectral mixture analysis,” 2014 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Quebec Canada, July 13-18, 2014.
  7. M. Song, Y. Li, C.-I Chang and L. Zhang, “Recursive Orthogonal Vector Projection Algorithm for Linear Spectral Unmixing,” IEEE GRSS WHISPERS 2014 conference (Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing), Lausanne, Switzerland, June 24-27, 2014.
  8. C. Gao, S.Y. Chen and C.-I Chang, “Fisher’s ratio-based criterion for finding endmembers in hyperspectral imagery,” Satellite Data Compression, Communication and Processing X (ST146), SPIE International Symposium on SPIE Sensing Technology + Applications, Baltimore, MD, 5-9 May 2014.
  9. Y. Li, S.Y. Chen, C. Gao and C.-I Chang, “Endmember variability resolved by pixel purity index in hyperspectral imagery,” Satellite Data Compression, Communication and Processing X (ST146), SPIE International Symposium on SPIE Sensing Technology + Applications, Baltimore, MD, 5-9 May 2014.
  10. Y. Wang, S.Y. Chen, C. Liu and C.-I Chang, “Background suppression issues in anomaly detection for hyperspectral imagery,” Satellite Data Compression, Communication and Processing X (ST146), SPIE International Symposium on SPIE Sensing Technology + Applications, Baltimore, MD, 5-9 May 2014.
  11. R.C. Schultz, M. Hobbs, and C.-I Chang, “Progressive band processing of simplex growing algorithm for finding endmembers in hyperspectral imagery,” Satellite Data Compression, Communication and Processing X (ST146), SPIE International Symposium on SPIE Sensing Technology + Applications, Baltimore, MD, 5-9 May 2014.
  12. S.Y. Chen, D. Paylor and C.-I Chang, “Anomaly discrimination in hyperspectral imagery,” Satellite Data Compression, Communication and Processing X (ST146), SPIE International Symposium on SPIE Sensing Technology + Applications, Baltimore, MD, 5-9 May 2014.
  13. D. Paylor and C.-I Chang, “A theory of least squares target-specified virtual dimensionality in hyperspectral imagery,” Satellite Data Compression, Communication and Processing X (ST146), SPIE International Symposium on SPIE Sensing Technology + Applications, Baltimore, MD, 5-9 May 2014.

2013 (5)

  1. D. Paylor and C.-I Chang, “Second-order statistics-specified virtual dimensionality,” SPIE Conference on Algorithms and Technologies for Multispectral, Hyperspectral and Ultraspectral Imagery XIX (DS122), 29 April-3 May 2013, Baltimore, MD 2103.
  2. Y. Wang, R. Schultz, S.Y. Chen, C. Liu and C.-I Chang, “Progressive constrained energy minimization for subpixel detection,” SPIE Conference on Algorithms and Technologies for Multispectral, Hyperspectral and Ultraspectral Imagery XIX, 29 April-3 May 2013, Baltimore, MD 2013.
  3. C.C. Wu, G.S. Huang, K.H. Liu and C.-I Chang, “Real-time progressive band processing of modified fully abundance-constrained spectral unmixing,” IEEE International Geoscience and Remote Sensing Symposium, 21-26 July, Melbourne, Australia, 2013.
  4. S.Y. Chen, D. Paylor and C.-I Chang, “Anomaly-specified virtual dimensionality,” SPIE Conference on Satellite Data Compression, Communication and Processing IX (OP 405), San Diego, CA, August 25-29, 2013.
  5. R. Schultz, S.Y. Chen, Y. Wang, C. Liu and C.-I Chang, “Progressive band processing of anomaly detection,” SPIE Conference on Satellite Data Compression, Communication and Processing IX (OP 405), San Diego, CA, August 25-29, 2013.

2012 (10)

  1. C.C. Wu, K.H. Liu and C.-I Chang, “Real-time progressive band processing of linear spectral unmixing,” Proceedings of Conference High-Performance Computing in Remote Sensing, SPIE 8539, Edinburgh, United Kingdom, 24-27 September, 2012.
  2. C.-I Chang, “Progressive hyperspectral imaging,” Proceedings of Conference High-Performance Computing in Remote Sensing, SPIE 8539, Edinburgh United Kingdom, 24-27 September, 2012.
  3. Y. Wang, S.Y. Chen, C.C. Wu, C. Liu and C.-I Chang, “Real-time causal processing of anomaly detection,” Proceedings of Conference High-Performance Computing in Remote Sensing, SPIE 8539, Edinburgh United Kingdom, 24-27 September, 2012.
  4. E. Wong and C.-I Chang, “Modified full abundance-constrained spectral unmixing,” Proceedings of Conference High-Performance Computing in Remote Sensing, SPIE 8539, Edinburgh United Kingdom, 24-27 September, 2012.
  5. C.-I Chang, “A unified theory for virtual dimensionality of hyperspectral imagery,” Proceedings of Conference High-Performance Computing in Remote Sensing, SPIE 8539, Edinburgh United Kingdom, 24-27 September, 2012.
  6. C.-I  Chang, F.-M. P. Hu, S.-Y.  Chen,  C.  Mackenzie, L.  Stansbury, J.  DuBose and T. Scalea, ”Utility of 3-dimensional ROC in using vital signs signals for blood transfusion,” 25th Computer Vision, Graphic, Image Processing (CVGIP), Nan-Tou, Taiwan, 12-14 August, 2012.
  7. C.-I  Chang and E. Wong, “2 dimensional Tanimoto index for continuous decision made classification,” 25th Computer Vision, Graphic, Image Processing (CVGIP), Nan-Tou, Taiwan, 12-14 August, 2012.
  8. S.Y. Chen, Y.C, Ouyang and C.-I Chang, “Weighted radial basis function kernels-based support vector machines for multispectral Image classification,” IEEE International Geoscience and Remote Sensing Symposium, 22-27 July, Munich, Germany, 2012.
  9. C.C. Wu and C.-I Chang, “Iterative pixel purity index,” 4th IEEE GRSS Workshop on Hyperspectral Image and Signal Processing - Evolution in Remote Sensing (WHISPERS), 12-14 June, Shanghai, China, 2012.
  10. S.Y Chen, C. Lin, Y.C. Ouyang and C.-I Chang, “Unsupervised multispectral image classification,” 4th IEEE GRSS Workshop on Hyperspectral Image and Signal Processing - Evolution in Remote Sensing (WHISPERS), 12-14 June, Shanghai, China, 2012.

2011 (7)

  1. C.Y Yu, Y.C. Ouyang, T.W. Yu and C.-I Chang, “An AIHT based contrast-limited adaptive histogram equalization (CLAHE) algorithm for image contrast enhancement,” 24thIPPR Conference on Computer Vision, Graphic, and Image Processing 2011, Chia-Yi, Taiwan, August 21-23 2011.
  2. H.M. Chen, B.H. Lin, S.Y. Chen, Y.C. Ouyang, J.W. Chai, C.C.C. Chen, C.W. Yang, T.S. Tsai, S.K. Lee and C.-I Chang, “Weighted radial basis function kernels for support vector machines classification of magnetic resonance brain images,” 24th IPPR Conference on Computer Vision, Graphic, and Image Processing 2011, Chia-Yi, Taiwan, August 21-23 2011.
  3. W. Xiong, C.C. Wu and C.-I Chang, “Field programmable Gate Array Design of Implementing Simplex Growing Algorithm for Hyperspectral Endmember Extraction,” Satellite Data Compression, Communications, and Processing VII, SPIE Optical Engineering + Applications, San Diego, 21-25 August 2011.
  4. K. Liu, E. Wong and C.-I Chang, “Kernel-based weighted abundance constrained linear Spectral mixture analysis,” SPIE Conference on Algorithms and Technologies for Multispectral, Hyperspectral and Ultraspectral Imagery XVII, 25 - 29 April 2011, Orlando, Flroida, 2011.
  5. H. Safavi, K. Liu and C.-I Chang, “Dynamic dimensionality reduction for hyperspectral imagery,” SPIE Conference on Algorithms and Technologies for Multispectral, Hyperspectral and Ultraspectral Imagery XVII, 25 - 29 April 2011, Orlando, Florida, 2011.
  6. K. Liu and C.-I Chang, “Dynamic band selection for hyperspectral imagery,” International Geoscience and Remote Sensing Symposium, 24-29 July, Vancouver, Canada, 2011.
  7. S.-Y. Chen, Y.C. Ouyang and C.-I Chang, “Iterative support vector machine for hyperspectral image classification,” International Geoscience and Remote Sensing Symposium, 24-29 July, Vancouver, Canada, 2011.

2010 (11)

  1. H.-M. Chen, S.-Y. Chen, J. W. Chai, C. C.-C. Chen, C.-C. Wu, Y.-C. Ouyang, C.-T Tsai, C.-W. Yang, S.-K. Lee, C.-I Chang, “Techniques for automatic magentic resonance image classification,” 4th Int. Conf. Genetic and Evolutional Computing, Shenzen, China, December 13-15, 2010.
  2. Y.J. Chiou, J. W. Chai, C. C.-C. Chen, S.-Y. Chen, H.-M. Chen, Y.-C. Ouyang, W.-C. Su,  C.-T Tsai, C.-W. Yang, S.-K. Lee, C.-I Chang, “Volume-based magentic resonance brain image classifcation,” 4th Int. Conf. Genetic and Evolutional Computing, Shenzen, China, December 13-15, 2010.
  3. H.-M. Chen, S.-Y. Chen, J. W. Chai, C. C.-C. Chen, Y.-C. Ouyang, C. T. Tsai, C.-W. Yang, S.-K. Lee, C.-I Chang, “An iterative Fisher’s linear discrimanant analysis coupled with support vector machine to enhance classification performance,” Computer Vision, Graphic, Image Processing (CVGIP), Kaushiung, Taiwan, August 15-17, 2010.
  4. H.-M. Chen, S.-Y. Chen, J. W. Chai, C. C.-C. Chen, Y.-C. Ouyang, C.-W. Yang, S.-K. Lee, C.-I Chang, “Hierarchical multi-class support vector machines,” Computer Vision, Graphic, Image Processing (CVGIP), Kaushiung, Taiwan, August 15-17, 2010.
  5. W. Xiong and C.-I Chang, “Maximum orthogonal subspace projection approach to estimating the number of spectral signal sources in hyperspectral imagery,” SPIE, vol. 7810, SPIE Conference on Satellite Data Compression, Communication and Processing VI, San Diego, CA, August 2-5, 2010.
  6. C.-I Chang and W. Xiong, “High-order statistics Harsanyi-Farrand-Chang method for estimation of virtual dimensionality,” SPIE, vol. 7810, SPIE Conference on Satellite Data Compression, Communication and Processing VI, San Diego, CA, August 2-5, 2010.
  7. W. Xiong, C.T. Tsai, C.W. Yang and C.-I Chang, “Convex cone-based endmember extraction for hyperspectral imagery,” SPIE, vol. 7812, SPIE Conference on Imaging Spectrometry XV, San Diego, CA, August 2-5, 2010.
  8. W. Xiong, C.-I Chang and C.-T. Tsai, “Estimation of virtual dimensionality in hyperspectral imagery by linear spectral mixture analysis,” IEEE International Geoscience and Remote Sensing Symposium, Honolulu; Hawaii, July 25-30, 2010.
  9. S. Chen, C. Lin, Y.C. Ouyang and C.-I Chang, “A new application of pixel purity index to unsupervised multispectral image classification,” IEEE International Geoscience and Remote Sensing Symposium, Honolulu; Hawaii, July 25-30, 2010.
  10. W. Xiong, C.-I Chang and K. Kalpakis, “Fast algorithms to implement N-FINDR for hyperspecftral endmember extarction,” Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI, SPIE Defense and Security Symposium in Orlando, Florida on April 5-9, 2010.
  11. Y.K Wong, E. Wong, C.-I Chang, J.W. Chai, C.C.C. Chen, K.W. Chang, “Remote sensing image detection; a new tool for evaluation the tumor thickness in tongue SCC,”American Association for Cancer Research (ACCR) 101st Annual Meeting, April 17-21, Wahsington DC, 2010.

2009 (14)

  1. H. Safavi and C.-I Chang, “Mixed projection pursuit-based dimensionality reduction,” Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV, SPIE Defense and Security Symposium in Orlando, Florida on April 13-17, 2009.
  2. C.C. Wu and C.-I Chang, “Causal pixel purity index,” Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV, SPIE Defense and Security Symposium in Orlando, Florida on April 13-17, 2009.
  3. C.-I Chang, C.C Wu and Y.L. Chang, “Real-time simplex growing algorithms,” IEEE International Geoscience and Remote Sensing Symposium, Cape Town, South Africa, 2009.
  4. C.C. Wu and C.-I Chang, “Soft-decision hyperspectral measures for target discrimination and classification,” SPIE Conference on Imaging Spectrometry XIV (OP 506), August 2-6, San Diego, 2009.
  5. K. Fisher and C.-I Chang, “Progressive band selection,” SPIE Conference on Imaging Spectrometry XIV (OP 506), August 2-6, San Diego, CA, 2009.
  6. H. Safavi and C.-I Chang, “Progressive dimensionality reduction for hyperspectral imagery,” SPIE Conference on Satellite Data Compression, Communication and Processing V (OP 504), August 2-6, San Diego, CA, 2009.
  7. C.-I Chang and C.C. Wu, *“*Design and analysis of real-time endmember extraction algorithms for hyperspectral imagery,” SPIE Conference on Satellite Data Compression, Communication and Processing V (OP 504), August 2-6, San Diego, CA, 2009.
  8. C.-I Chang, “Hyperspectral information compression,” SPIE Conference on Satellite Data Compression, Communication and Processing V (OP 504), August 2-6, San Diego, CA, 2009.
  9. K. Liu, E. Wong, C.-I Chang and Y. Du, “Kernel-based linear spectral mixture analysis for hyperspectral image classification,” 1st IEEE GRSS Workshop on Hyperspectral Image and Signal Processing - Evolution in Remote Sensing, 26-28 August, Grenoble, France, 2009.
  10. X. Jiao, Y. Du and C.-I Chang, ”Component Analysis-Based Unsupervised Linear Spectral Mixture Analysis for Hyperspectral Imagery,” 1st IEEE GRSS Workshop on Hyperspectral Image and Signal Processing - Evolution in Remote Sensing, 26-28 August, Grenoble, France, 2009.
  11. X. Jiao, Y. Du and C.-I Chang, “Orthogonal subspace projection approach to finding signal sourcesin hyperspectral imagery,” Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI, SPIE Defense and Security Symposium in Orlando, Florida on April 5-9, 2009.
  12. S-Y. Chen, H.M. Chen, Y.C. Chiu, J. W. Chai, C. C.-C. Chen, Y.-C. Ouyang, C.-W. Yang, S.-K. Lee and C.-I Chang, “A hyperspectral imaging approach to unsupervised magnetic resonance brian tossue classification,” 22th Computer Vision, Graphic and Image Processing (CVGIP), Chi-Tou, Taichung, Aug. 23-25, 2009.
  13. H-C. Lee, H.M. Chen, Y.C. Chiu, J. W. Chai, C. C.-C. Chen, Y.-C. Ouyang, C.-W. Yang, S.-K. Lee and C.-I Chang, “Texture analysis for linear spectral unmixing of brain MR image classification,” 22th Computer Vision, Graphic and Image Processing (CVGIP), Chi-Tou, Taichung, Aug. 23-25, 2009.
  14. Y-C. Chiu, H.-M. Chen, J. W. Chai, C. C.-C. Chen, Y.-C. Ouyang, W.-C. Su, C.-W. Yang, S.-K. Lee and C.-I Chang, “Unsupervised magnetic resonance image classification using independent vector analysis,” 22th Computer Vision, Graphic and Image Processing (CVGIP), Chi-Tou, Taichung, Aug. 23-25, 2009.

2008 (18)

  1. B. Ramakrishna, G. Saiprasad*,* N. M. Safdar, K. M. Siddiqui, W. Kim, W. Liu,  C. -I. Chang, E. L. Siegel, "Automated discovery of meniscal tears on MR Imaging: a novel, high-performance, computer-aided detection application for radiologists," SPIE Medical Imaging, San Diego, CA 2008
  2. Y.-C. Chang, H. Ren, C.-I Chang and B. Rand, “How to design synthetic images to validate and evaluate hyperspectral imaging algorithms,” SPIE Conference on Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIV, March 16-20, Orlando, Florida, 2008.
  3. E.L. Wong and C.-I Chang, “Linear spectral unmixing approaches to magnetic resonance image analysis,” SPIE Conference on Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIV, March 16-20, Orlando, Florida, 2008.
  4. X. Jiao and C.-I Chang, “Kernel-based constrained energy minimization (KCEM),” SPIE Conference on Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIV, March 16-20, Orlando, Florida, 2008.
  5. K. Liu and C.-I Chang, “Exploration of component analysis in multi/hyperspectral image processing,” SPIE Conference on Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIV, March 16-20, Orlando, Florida, 2008.
  6. S. Chu, H. Ren and C.-I Chang, “High-order statistics-based approaches to endmember extraction for hyperspectral imagery,” SPIE Conference on Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIV, March 16-20, Orlando, Florida, 2008.
  7. H. Safavi and C.-I Chang, “Projection pursuit-based dimensionality reduction,” SPIE Conference on Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIV, March 16-20, Orlando, Florida, 2008.
  8. S. Chakravarty and Chang, “Band selection for hyperspectral signature coding,” International Symposium Spectral Sensing Research (ISSSR), June 23-27, Steven Institute of Technology,  N.J., 2008.
  9. C.-I Chang, “Hyperspectral imaging: an emerging technique in remote sensing,” International Symposium Spectral Sensing Research (ISSSR), June 23-27, Steven Institute of Technology,  N.J., 2008.
  10. C.-I Chang, “Unsupervised linear hyperspectral unmixing,” International Symposium Spectral Sensing Research (ISSSR), June 23-27, Steven Institute of Technology, N.J., 2008
  11. C.-I Chang, “Hyperspectral imaging: an emerging technique in remote sensing,” International Symposium Spectral Sensing Research (ISSSR), June 23-27, Steven Institute of Technology,  N.J., 2008.
  12. C.-I Chang, “Three dimensional receiver operating characteristic (3D ROC) analysis for hyperspectral signal detection and estimation,” ISSSR, June 23-27, N.J., 2008.
  13. W. Liu and C.-I Chang, “Multiple-window anomaly detection for hyperspectral imagery,” IEEE International Geoscience and Remote Sensing Symposium, July 6-11, Boston, MA, 2008.
  14. S. Chakravarty and C.-I Chang, “Block truncation signature coding for hyperspectral image analysis,” SPIE Conference on Imaging Spectrometry XIII, August 10-14, San Diego, 2008.
  15. C.C. Wu, S. Chu and C.-I Chang, “Sequential N-FINDR algorithm,” SPIE Conference on Imaging Spectrometry XIII, August 10-14, San Diego, 2008.
  16. X. Jiao and C.-I Chang, “Unsupervised hyperspectral target analysis,” SPIE Conference on Imaging Spectrometry XIII, August 10-14, San Diego, 2008.
  17. G. Saiprasad, B. Ramakrishna, O. N. Ilahi, N. M. Safdar, K. M. Siddiqui, G. Bochicchio, E. Siegel, C. -I. Chang, “A computer aided detection application for automatic detection of splenic volume: a gradient vector flow (GVF) snake approach,” Radiological Society of North America (RSNA), 94th Scientific Assembly and Annual Meeting, Nov 30 – Dec 5, Chicago, Illinois, 2008. (presented)
  18. G. Saiprasad*,* B. Ramakrishna, A. Sharma, T. Pan, N. M. Safdar, K. M. Siddiqui, “Orchestrating a workflow for integrating multiple remote CAD algorithms over a grid,”Radiological Society of North America (RSNA), 94th Scientific Assembly and Annual Meeting, Nov 30 – Dec 5, Chicago, Illinois, 2008. (presented)

2007 (10)

  1. B. Ramakrishna, W. Liu, Nabile Safdar, Khan Siddiqui, Woojin Kim, Krishna Juluru, C. Chang, E. L. Siegel, “Automatic CAD of meniscal tears on MR Imaging  a morphology-based  approach,” CA, February 2007.
  2. W. Liu, C-C. Wu and C.-I Chang, “An orthognal subspace projection-based estimation of virtual dimesnionality for hyperspectral data exploitation,” Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIII, SPIE Defense and Security Symposium, Orlando, Florida, April 9-13, 2007.
  3. C-C. Wu, W. Liu, H. Ren and C.-I Chang, “A comparative study and analysis between vertex component analysis and orthogonal subspace projection for endmember extarction,” Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIII, SPIE Defense and Security Symposium, Orlando, Florida, April 9-13, 2007.
  4. B. Ramakrishna, W. Liu, K. M. Siddiqui, K.Juluru, N. M. Safdar, C. Chang, E. L. Siegel, “An Automatic tool for assessment of tumor viability and tumor burden in liver tissue on MRI scans,” Computer Assisted Radiology and Surgery [P0 462], Berlin, Germany 2007.
  5. C-C. Wu and C.-I Chang, “Does an endmember set really yield maximum simplex volume?,” 2007 International Geoscience and Remote Sensing Symposium, Barcelona, Spain, 2007.
  6. W. Liu and C.-I Chang, “Variants of principal components analysis,” 2007 International Geoscience and Remote Sensing Symposium, Barcelona, Spain, 2007.
  7. S. Chu, C.C. Wu and C.-I Chang, “Statistics-based endmember extraction algorithms for hyperspectral imagery,” SPIE Conf. Imaging Spectrometry XII, SPIE Symposium on Optics & Photonics, San Diego, CA, 26-30 August, 2007.
  8. X. Jiao and C.-I Chang, “Unsuperviused hyperspectral image classification,” SPIE Conf. Imaging Spectrometry XII, SPIE Symposium on Optics & Photonics, San Diego, CA, 26-30 August, 2007.
  9. B. Ramakrishna, W. Liu, K. M. Siddiqui, K.Juluru, N. M. Safdar, C. Chang, E. L. Siegel, “Demonstration of a Novel Computer-aided Detection Application for Evaluation of Meniscal Tears,” Radiological Society of North America (RSNA), Nov. 25-30, 2007.
  10. B. Ramakrishna, W. Liu, K. M. Siddiqui, K.Juluru, N. M. Safdar, C. Chang, E. L. Siegel, “Comparison of a novel computer-aided detection tool in identifying meniscal tears with radiologist interpretations,” Radiological Society of North America (RSNA), Nov. 25-30 2007.

2006 (17)

  1. S. Wang and C.-I Chang, “Variable-size variable band selection for spectral feature characterization in hyperspectral data,”Optics East, Chemical and Biological Sensors for Industrial and Environmental Monitoring II, vol. 6378, Boston, MA, Oct. 23-26, 2006.
  2. C.-C. Wu and C.-I Chang, “Exploration of methods for estimation of number of endmembers in hyperspectral imagery,”Optics East, Chemical and Biological Sensors for Industrial and Environmental Monitoring II, vol. 6378, Boston, MA, Oct. 23-26, 2006.
  3. C.C. Wu and C.-I Chang, “Automatic algorithms for endmember extraction,” SPIE Conf. Imaging Spectrometry XI, SPIE Symposium on Optics & Photonics, vol. 6302, 13-17 August 2006, San Diego, CA.
  4. S. Wang and C.-I Chang, “Band prioritization for hyperspectral imagery,” SPIE Conf. Imaging Spectrometry XI, SPIE Symposium on Optics & Photonics, vol. 6302, 13-17 August, San Diego, CA, 2006.
  5. S. Wang, C.-I Chang, J.L. Jensen and J.O. Jensen, “Kalman filter-based approaches to hyperspectral signature similarity and discrimination,” SPIE Conf. Imaging Spectrometry XI, SPIE Symposium on Optics & Photonics, vol. 6302, 13-17 August, San Diego, CA, 2006.
  6. S. Chakravarty and C.-I Chang, “Spectral derative feature coding for hypespectral signature analysis,” SPIE Conf. Imaging Spectrometry XI, SPIE Symposium on Optics & Photonics, vol. 6302, 13-17 August, San Diego, CA, 2006.
  7. B. Ramakrishna, C.-I Chang, B. Trout and J. Henqemihle, “Chesapeake bay water monitoring using satellite imagery,” International Symposium Spectral Sensing Research(ISSSR), pp. 66-72, May 29-June 2, Maine, 2006.
  8. C.-I Chang, M. Hsueh, F. Chaudhry, W. Liu,  C.-C. Wu, G. Solya and A. Plaza, “A pyramid-based block of skewers for pixel purity index for endmember extarction in hyperspectral imagery,” 2006 International Symposium Spectral Sensing Research (ISSSR), May 29-June 2, pp. 355-368, Maine, 2006.
  9. C.-I Chang, “Exploration of virtual dimensionality in hyperspectral image analysis,” SPIE Conf. 6233, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII, SPIE Defense and Security Symposium, Orlando, Florida, April 17-21, 2006.
  10. W. Liu and C.-I Chang, “Sample spectral correlation-based measures for subpixels and mixed pixels in real hyperspectral imagery,” SPIE Conf. 6233, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII, SPIE Defense and Security Symposium, Orlando, Florida, April 17-21, 2006.
  11. S. Wang and C.-I Chang, “Linearly constrained band selection for hyperspectral imagery,” SPIE Conf. 6233, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII, SPIE Defense and Security Symposium, Orlando, Florida, April 17-21, 2006.
  12. J. Wang and C.-I Chang, “Applications of independent component Analysis (ICA) to abundance quantification for hyperspectral imagery,” SPIE Conf. 6233, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII, SPIE Defense and Security Symposium, Orlando, Florida, April 17-21, 2006.
  13. S. Chakravarty and C.-I Chang, “Spectral feature probabilistic coding for hyperspectral signatures,” SPIE Conf. 6233, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII, SPIE Defense and Security Symposium, Orlando, Florida, April 17-21, 2006.
  14. S.M. Guo, Y.A. Pan, Y.C. Liao, C.Y.  Hsu, J.S. Tsai, C.-I Chang, “A key frame selection-based facial expression recognition system,” IEEE 2006 International Conference on Innovative Computing, Information and Control, Aug. 31-Sep. 1, 2006, Beijing, China.
  15. H.-M. Chen, C.-C. Chen, Y.-C. Ouyang, J. W. Chai, C. C.-C. Chen, C.-W. Yang, S.-K. Lee, C.-I Chang, “Independent component analysis in conjunction with support vector machine for magnetic resonance image analysis”, 19th IPPR Conference on Computer Vision, Graphics and Image Processing, August 13-15, Taiwan, 2006.
  16. M.-L. Chang, C.P. Chuang, C.C. Wu, Y.W. Chang, G.C. Hsu,, S.-K. Lee and C.-I Chang, “A versatile mammography system with its applications”, 19th IPPR Conference on Computer Vision, Graphics and Image Processing, August 13-15, Taiwan, 2006.
  17. P.S. Liao, S.M. Guo., Z.E. Tsai and C.-I Chang, “Feature Selection Strategy for Mass Detection in Mammograms,” 19th IPPR Conference on Computer Vision, Graphics and Image Processing, August 13-15, Taiwan, 2006.

2005 (15)

  1. J. Wang and C.-I Chang, “An over-complete independent component analysis (ICA) approach to magentic resonance image analysis,” 27th Annual International Conference of IEEE Engineering in Medicine and Biology Society (EMBS), September 1-4, 2005, Shanghai, China.
  2. S. Wang, C.-I Chang and S. Yang, “3D ROC analysis for medical diagnosis evaluation,” 27th Annual International Conference of IEEE Engineering in Medicine and Biology Society (EMBS), Spetember 1-4, 2005, Shianghai,  China.  
  3. J. Wang and C.-I Chang, “Mixed (PCA,ICA) spectral/spatial compression for hyperspectral imagery,” OpticsEast, Chemical and Biological Standoff Detection III (SA103), Boston, MA, Oct. 23-26, 2005.
  4. W. Liu, C.-I Chang, S. Wang, J. Jensen, J. Jensen, H. Hnapp, R. Daniel and R. Yin, “3D ROC analysis for detection software used in water monitoring,” OpticsEast, Chemical and Biological Standoff Detection III (SA103), Boston, MA, Oct. 23-26, 2005.
  5. L. Wu, J. Wang, M. Hsueh, B. Ramakrishna, J. Liu, Qufei Wu, C. Wu, C.-C. Liu, M. Cao, C.-I Chang, J. Jensen, J. Jensen, H. Hnapp, R. Daniel and R. Yin, “An embedded system for hand held assy used in water monitor,” OpticsEast, Chemical and Biological Standoff Detection III (SA103), Boston, MA, Oct. 23-26, 2005.
  6. J. Wang and C.-I Chang, “Dimensionality reduction by independent component analysis for hyperspectral image analysis,” IEEE International Geoscience and Remote Sensing Symposium, Seoul, Korea, July 25-29, 2005.
  7. C. Wu and C.-I Chang, “A new simplex growing algorithm for endmember extraction,” IEEE International Geoscience and Remote Sensing Symposium, Seoul, Korea, July 25-29, 2005.
  8. S. Wang and C.-I Chang, “A new application of wavelet analysis to hyperspectral signature characterization,” IEEE International Geoscience and Remote Sensing Symposium, Seoul, Korea, July 25-29, 2005.
  9. G. Solyar, A. Plaza and C.-I Chang, “Endmember generation by projection pursuit,” Conference on Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI, SPIE Symposium on Defense and Security, SPIE Vol. 5806, Orlando, Florida, 28 March-1 April, 2005.
  10. B. Ramakrishna, J. Wang, A. Plaza and C.-I Chang, “Spectral/spatial hyperspectral image compression in conjunction with virtual dimensionality,” Conference on Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI, SPIE Symposium on Defense and Security, SPIE Vol. 5806, Orlando, Florida, 28 March-1 April, 2005.
  11. B. Ji and C.-I Chang, “Weighted least squares error approaches to abundance-constrained linear spectral mixture analysis,” Conference on Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI, SPIE Symposium on Defense and Security, SPIE Vol. 5806, Orlando, Florida, 28 March-1 April, 2005.
  12. A. Plaza and C.-I Chang, “Fast implementation of pixel purity index algorithm,” Conference on Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI, SPIE Symposium on Defense and Security, SPIE Vol. 5806, Orlando, Florida, 28 March-1 April, 2005.
  13. A. Plaza and C.-I Chang, “An improved N-FINDR algorithm in implementation,” Conference on Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI, SPIE Symposium on Defense and Security, SPIE Vol. 5806, Orlando, Florida, 28 March-1 April, 2005.
  14. J. Plaza, A. Plaza and C.-I Chang, “On the generation of training samples for neural network-based mixed pixel classification,” Conference on Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI, SPIE Symposium on Defense and Security, SPIE Vol. 5806, Orlando, Florida, 28 March-1 April, 2005.
  15. F. Chaudhry, S. Chakravarty, A. Plaza and C.-I Chang, “Design of fast algorithms for pixel purity index for endmember extraction in hyperspectral imagery,” 2005 American Society for Photogrammetry & Remote Sensing, (ASPRS) Annual Conference, March 7-11, Baltimore, MD 2005.

2004 (11)

  1. M. Hsueh, A. Plaza, J. Wang, S. Wang, W. Liu, C.-I Chang, J. L. Jensen and J. O. Jensen, “Morphological algorithms for processing tickets by hand held assay,” OpticsEast, Chemical and Biological Standoff Detection II (OE120), Vol. 5584, Philadelphia, PA, Oct. 25-28, 2004.
  2. S. Wang, C.-I Chang, J. L. Jensen, and J. O. Jensen, “Spectral abundance fraction estimation of materials using Kalman filters,” OpticsEast, Chemical and Biological Standoff Detection II (OE120), Vol. 5584, Philadelphia, PA, Oct. 25-28, 2004.
  3. C.-I Chang, W. Liu and C.-C. Chang, “Discrmination and identification for subpixel targets in hyperspectral imagery,” IEEE International Conference on Image Processing, Singapore, Oct. 24-27, 2004.
  4. Y. Chen and C.-I Chang, “A new application of texture unit coding to mass classification for mammograms,”IEEE International Conference on Image Processing, Singapore, Oct. 24-27,  2004.
  5. M. Hsueh and C.-I Chang, “Adaptive causal anomaly detection for hyperspectral imagery,” IEEE International Geoscience and Remote Sensing Symposium, Alaska, September 20-24, 2004.
  6. W. Liu and C.-I Chang, “A nested spatial window-based approach to target detection for hyperspectral imagery,” IEEE International Geoscience and Remote Sensing Symposium, Alaska, September 20-24, 2004.
  7. J. Wang and C.-I Chang, “A uniform projection-based unsupervised detection and classification for hyperspectral imagery,” IEEE International Geoscience and Remote Sensing Symposium, Alaska, September 20-24, 2004.
  8. J. Wang, C.-I Chang, C.-C. Chang and C. Lin, “Binary coding for remotely sensed imagery,” 49th Annual Meeting, SPIE International Symposium on Optical Science and Techology, Imaging Spectrometry X (AM105), Denver, CO, pp. 107-114, August 2-4, 2004.
  9. S. Yang, J. Wang, C.-I Chang, J.L. Jensen and J.O. Jensen, “Unsupervised image classification for remotely sensed imagery,” 49th Annual Meeting, SPIE International Symposium on Optical Science and Technology, Imaging Spectrometry X (AM105), Denver, CO, pp. 354-365, August 2-4, 2004.
  10. B. Ji, C.-I Chang, J.O. Jensen and J.L. Jensen, “Unsupervised constrained linear Fisher’s discriminant analysis for hyperspectral image classification,” 49th Annual Meeting,SPIE International Symposium on Optical Science and Techology, Imaging Spectrometry IX (AM105), Denver, CO, pp. 344-353, August 2-4, 2004.
  11. X. Zhang, R. Xu, Kwan and C.-I Chang, “Target detection with texture feature coding method and supprting vector machine,” ICASSP, Montreal, CA, May 17-21, 2004.

2003 (15)

  1. Y. Du, C.-I Chang and P. Thouin, “An unsupervised approach to color thresholding,” 2003 ICASSP, April 1-5, pp. III-373-III-376, Hong Kong, 2003.
  2. C.-I Chang, H. Ren, F. D’Amico and J.O. Jensen, “Subpixel target size estimation for remotely sensed imagery,” SPIE AeroSense, Conf. on Algorithms and Technologies for Multispectral, Hyperspectral and Ultraspectral Imagery IX, Orlando, Florida, pp. 398-407, April, 2003.
  3. Y. Du, C.-I Chang, H. Ren, F. D’Amico and J.O. Jensen, “A new hyperspectral discrimination measure for spectral similarity,” SPIE AeroSense, Conf. on Algorithms and Technologies for Multispectral, Hyperspectral and Ultraspectral Imagery IX, Orlando, Florida, pp. 430-439, April 2003.
  4. S.K. Lee, S.-C. Yang, P.-C. Chung, C.-S. Lo, C.-W. Yang, S.-M. Guo, P.-S. Liao and C.-I Chang, “Computer-aided diagnosis in mammography,” 13th International Congress on the Ultrasonic Examination of Breast, Kyoto, Japan, April 5-9, 2003.
  5. P.-S. Liao, S.-M. Guo, Y.C. Liao, S.C. Yang, S.K. Lee, C.-S. Lo, P.-C. Chung, C.-W. Yang and C.-I Chang, “Computer-aided classification of masses using a texture feature coding method,” 13th International Congress on the Ultrasonic Examination of Breast, Kyoto, Japan, April 5-9, 2003.
  6. S.-M. Guo, P.-S. Liao, Y.C. Liao, S.C. Yang, C.-S. Lo, P.-C. Chung, C.-W. Yang, S.K. Lee and C.-I Chang, “A texture spectrum approach to mass detection,” Annual Meeting on Chinese Radiaology Association, Taichung, Taiwan, March 27-29, 2003.
  7. Z. Sun, C.-I Chang, H. Ren, F. D’Amico and J.O. Jensen, “A least-squares approach to fully constrained linear spectral mixture analysis using linear inequality constraints,” 48thAnnual Meeting, SPIE International Symposium on Optical science and Techology, Imaging Spectrometry IX (AM110), San Diego, CA,  August 3-8, 2003.
  8. C.-I Chang, H. Ren, M. Hsueh, F. D’Amico and J.O. Jensen, “A revisit to target-constrained interference-minimized filter,” 48th Annual Meeting, SPIE International Symposium on Optical science and Techology, Imaging Spectrometry IX (AM110), San Diego, CA, August 3-8, 2003.
  9. C.-I Chang, J. Wang, F. D’Amico and J.O. Jensen, “Multistage pluse code modulation for progressive spectral signature coding,” Chemical and Biological Standoff Detection-Optical Technologies for Industrial and Environmental Sensing Symposia - Photonics West 2003, pp. 252-261, 27-31 October, 2003.
  10. J. Wang and C.-I Chang, “FPGA design for constrained energy minimization,” Chemical and Biological Standoff Detection-Optical Technologies for Industrial and Environmental Sensing Symposia - Photonics West 2003, pp. 262-273, 27-31 October, 2003.
  11. Q. Du and C.-I Chang, “Segmented PCA-based compression for hyperspectral image analysis,” Chemical and Biological Standoff Detection-Optical Technologies for Industrial and Environmental Sensing Symposia - Photonics West 2003, pp. 274-281, 27-31 October, 2003.
  12. H. Ren, C. Lin and C.-I Chang, “Subpixel land-cover detection and classification for hyperspectral imagery,” Chemical and Biological Standoff Detection-Optical Technologies for Industrial and Environmental Sensing Symposia - Photonics West 2003, pp. 282-287, 27-31 October, 2003.
  13. C.-I Chang, “How to effectivly utilize information to design hyperspectral target detection and classification algorithms,” Workshop in honor of Professor David Landgrebe on Advances in Techniques for Analysis of Remotely Sensed Data, NASA Goddard Visitor Center, Washington DC, Oct. 27-28, 2003.
  14. H. Ren, Q. Du, C.-I Chang and J.O. Jensen, “Comparison between constrained energy minimization based approaches for hyperspectral imagery,” Workshop in honor of Professor David Landgrebe on Advances in Techniques for Analysis of Remotely Sensed Data, NASA Goddard Visitor Center in Washington DC on Oct. 27-28, 2003.
  15. C. Lin and C.-I Chang, “Study on the relationship between the forest canopy closure and hyperspectral signatures,” 24th Asian Conference on Remote Sensing (ACRS) and 2003 International Symposium on Remote Sensing, Busan, Korea, 3-7 November, 2003.

2002 (8)

  1. Y. Du, P.D. Thouin and C.-I Chang, "A multistage predictive coding approach to unsupervised text detection in video images," IS&T/SPIE’s 14th Int. Symp. on Electronic Imaging: Science and Technology, SPIE, Vol. 4670, Document Recognition and Retrieval IX, San Jose, CA,  Jan 20-25, 2002.
  2. J. Wang, Y. Du, C.-I Chang and P. Thouin, "Relative entropy-based methods for image thresholding," International Symposium. Circuit and Systems (ISCAS) 2002, 26-29 May, Scottsdale, Arizona, 2002.
  3. C.-I Chang, "Relationship among orthogonal subspace projection, constrained energy minimization and RX-algorithm," SPIE Conf. on Algorithms and Technologies for Multispectral, Hyperspectral and Ultraspectral Imagery VIII, SPIE 4725, Orlando, Florida, 1-5 April, 2002.
  4. K. Guilfoyle, M.L.G. Althouse, C.-I Chang, "Further results on linear and nonlinear mixture models for analyzing hyperspectral imagery," SPIE Conf. on Algorithms and Technologies for Multispectral, Hyperspectral and Ultraspectral Imagery VIII, SPIE 4725, Orlando, Florida, 1-5 April, 2002.
  5. S.S. Chiang and C.-I Chang, “Automatic thresholding abundance fractional images for mixed pixel classification,” 2002 International Geoscience and Remote Sensing Symp., 24-28, June, Toronto, Canada, 2002.
  6. J. Wang and C.-I Chang, “Unsupervised Kalman filter approach to signature estimation for remotely sensed imagery,” 2002 International Geoscience and Remote Sensing Symp., 24-28, June, Toronto, Canada, 2002.
  7. Y. Du, P. Thouin and C.-I Chang, "Thresholding of color video images,” International Conf. on Pattern Recognition, Quabec, Canada, August 2002.
  8. Q. Du, H. Ren and C.-I Chang, “A study between orthogonal subspace projection and likelihood ratio test in hyperspectral image classification,” 2002 International Geoscience and Remote Sensing Symp., 24-28, June, Toronto, Canada, 2002.

2001 (11)

  1. P. Thouin, Y. Du and C.-I Chang, "Low resolution expansion of gray scale text images using Gibbs-Markov random field model," 2001 Symposium on Document Image Understanding Technology, pp. 41-47, April 23-25, 2001.
  2. C.-I Chang, S.-S. Chiang and I.W. Ginsberg, "Anomaly detection in hyperspectral imagery," SPIE Conf. on Geo-Spatial Image and Data Exploitation II, Orlando, Florida, pp. 43-50, 20-24 April, 2001.
  3. C.-I Chang, Q. Du, S.-S. Chiang, D. Heinz and I.W. Ginsberg, "Unsupervised target subpixel detection in hyperspectral imagery," SPIE Conf. on Algorithms for Multispectral, Hyperspectral and Ultraspectral Imagery VII, Orlando, Florida, pp. 370-379, 20-24 April, 2001.
  4. Q. Du and C.-I Chang, "Interference subspace projection approach to subpixel target detection," SPIE Conf. on Algorithms for Multispectral, Hyperspectral and Ultraspectral Imagery VII, Orlando, Florida, pp. 570-577, 20-24 April, 2001.
  5. Y. Du and C.-I Chang, "Low resolution expansion of color text image using HSI approach," 5th World Multiconference on Systems, Cybernetics and Informatics (SCI 2001) and 7th International Conference on Information Systems Analysis and Synthesis (ISAS 2001), Orlando, Florida, pp. 295-300, July 22-25, 2001.
  6. S.-C. Yang, P.C. Chung, C.-I Chang, S.K. Lee, Y.N. Chung, C.W. Yang, M.C. Lu and C.S. Lo "An automated system for detection and segmentation of masses in digital mammgrams," 5th World Multiconference on Systemics, Cybernetics and Informatics, Orlando, Florida, USA, July 22-25, 2001.
  7. C.-I Chang, H. Ren, Q. Du, S-S. Chiang and A. Ifarraguerri, "An ROC analysis for subpixel detection," IEEE 2001 International Geoscience and Remote Sensing Symp., Sydney, Australia, July 24-28, 2001.
  8. S-S. Chiang and C.-I Chang, "Discrimination measures for target classification," IEEE 2001 International Geoscience and Remote Sensing Symp., Sydney, Australia, July 24-28, 2001.
  9. D. Heinz and C.-I Chang, "Real time implementation of an unsupervised constrained spectral unmixing algorithm," IEEE 2001 International Geoscience and Remote Sensing Symp., Sydney, Australia, July 24-28, 2001.
  10. Q. Du and C.-I Chang, "Hidden Markov model approaches to hyperspectral image classification," IEEE 2001 International Geoscience and Remote Sensing Symp., Sydney, Australia, July 24-28, 2001.
  11. C. Lin and C.-I Chang, "Dependence of land use and spectral information in the aspect of band selection of seashore and urban hyperspectral data," 5th International Airborne Remote Sensing Conf., San Francisco, CA, 17-20 September, 2001.

2000 (8)

  1. C.M. Wang, P.C. Chung, C.-I Chang, C.-W. Yang, and C.C. Chen, "An orthogonal subspace projection to MR image classification," Fourth Asian Conf. on Computer Vision, vol. 2, pp. 877-880, Taipei, Taiwan, Jan. 2000.
  2. C.-I Chang and D. Heinz, "Constrained subpixel target detection for hyperspectral imagery," SPIE Conf. on Signal and Data Processing of Small Targets 2000, Orlando, FL, pp. 35-45, April 2000.
  3. Q. Du and C.-I Chang, "A hidden Markov model-based spectral measure for hyperspectral image analysis," SPIE Conf. on Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VI, Orlando, FL, pp. April 2000.
  4. H. Ren and C.-I Chang, "A target-constrained interference-minimized filter for subpixel target detection and classification in hyperspectral imagery," IEEE 2000 International Geoscience and Remote Sensing Symp., Hawaii, USA, July 24-28, 2000.
  5. S.-S. Chiang, C.-I Chang and I.W. Ginsberg, "Unsupervised hyperspectral image analysis using independent components analysis," IEEE 2000 International Geoscience and Remote Sensing Symp., Hawaii, USA, July 24-28, 2000.
  6. D. Heinz and C.-I Chang, "Unsupervised fully constrained least squares linear mixture analysis for multispectral imagery," IEEE 2000 International Geoscience and Remote Sensing Symp., Hawaii, USA, July 24-28, 2000.
  7. Q. Du, C.-I Chang, D.C. Heinz, M. L.G. Althouse and I.W. Ginsberg, "Hyperspectral image compression for target detection and classification," IEEE 2000 International Geoscience and Remote Sensing Symp., Hawaii, USA, July 24-28, 2000.
  8. C. Lin, C.M. Wang and C.-I Chang, "Application of generalized constrained energy minimization approach on urban road detection," IEEE 2000 International Geoscience and Remote Sensing Symp., Hawaii, USA, July 24-28, 2000.

1999 (14)

  1. Q. Du and C.-I Chang, "A linear constrained Euclidean distance-based discriminant analysis for hyperspectral image classification," 1999 Conference on Information and System Science, Johns Hopkins University, Baltimore, MD, March 17-19, 1999.
  2. H. Ren and C.-I Chang, "A constrained least squares approach to hyperspectral image classification," 1999 Conference on Information Science and Systems, Johns Hopkins University, Baltimore, MD,  March 17-19, 1999.
  3. P. Thouin and C.-I Chang, "A method for restoration of low-resolution text images," SPIE Proc. 1999 Symposium on Documentation Image Understanding Technology, Annapolis, MD, pp. 143-148, April 14-16, 1999.
  4. Q. Du and C.-I Chang, "An interference rejection-based radial basis function neural network approach to hyperspectral image classification," International Joint Conference on Neural Network, Washington DC, pp. 2698-2703, July 1999.
  5. C.-I Chang, "Spectral information divergence for hyperspectral image analysis," IEEE 1999 International Geoscience and Remote Sensing Symp., Hamburg, Germany, pp. 509-511, 28 June-2 July, 1999.
  6. D.C. Heinz, C.-I Chang and M.L.G. Althouse, "Fully constrained least squares-based linear unmixing," IEEE 1999 International Geoscience and Remote Sensing Symp., Hamburg, Germany, pp. 1401-1403, 28 June-2 July, 1999.
  7. C.-I Chang and H. Ren , "Linearly constrained minimum variance beamforming for target detection and classification in hyperspectral imagery," IEEE 1999 International Geoscience and Remote Sensing Symp., Hamburg, Germany, pp. 1241-1243, 28 June-2 July,  1999.
  8. C.C. Chang, Mann-Li Chang, H. Ren and C.-I Chang, "Exploitation of remote sensing image processing in environmental studies and monitoring," Chinese Asian Academic Professional Conference, Washington, DC, July 2-5, pp. 9.13.1-9.13.4, 1999.
  9. C.M. Wang, P.C. Chung, C.-I Chang, C.-C. Chen and C.-S. Lo, "A constrained energy minimization approach to MR image classification," 12th IPPR Conf. on Computer Vision, Graphics and Image Processing, Taipei, Taiwan, pp. 83-86, August 1999.
  10. I. Seo, S. Kang, C.-I Chang and H. Ko, "An efficient bias estimation method in multisensor fusion for navigation by adaptive prototype selection in a bank of Kalman filters,"1999 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, Taipei, Taiwan, Aug. 15-19, pp. 279-284, 1999.
  11. C.-I Chang and Q. Du, "A noise subspace projection approach to determination of intrinsic dimensionality for hyperspectral imagery," EOS/SPIE Symposium on Remote Sensing, Conference on Image and Signal Processing for Remote Sensing V, SPIE vol. 3871, Florence, Italy, pp. 34-44, September 20-24, 1999.
  12. S.-S. Chiang and C.-I Chang, "Target subpixel detection for hyperspectral imagery using projection pursuit," EOS/SPIE Symposium on Remote Sensing, Conference on Image and Signal Processing for Remote Sensing V, SPIE vol. 3871, Florence, Italy, pp. 107-115, September 20-24, 1999.
  13. J.-M. Liu, C.M. Wang, Chieu, C.-I Chang, H. Ren and C.W. Yang, "A generalized constrained energy minimization approach to subpixel detection for multispectral imagery,"EOS/SPIE Symposium on Remote Sensing, Conference on Image and Signal Processing for Remote Sensing V, Florence, SPIE vol. 3871, Italy, pp. 125-135, September 20-24, 1999.
  14. K. Guilfoyle, M.L.G. Althouse and C.-I Chang, "Spectral mixing for spatially distinct and randomly distributed regions," Proc. Int. Sym. Special  Spectral Research (ISSSR), Las Vegas, NV, November 1-4, 1999.

1998 (14)

  1. P. Thouin and C.-I Chang, "Constrained nonlinear restoration of JPEG compressed low-resolution text from gray-level images using a Gibbs-Markov random fields," IS&T/SPIE10th Annual Symposium, Electronic Imaging '98: Science & Technology, Documentation V, San Jose, pp. 220-229, Jan. 24-30, 1998.
  2. Q. Du and C.-I Chang, "Radial basis function neural networks approach to hyperspectral image classification," 1998 Conference on Information Science and Systems, Princeton University, Princeton, NJ, pp. 721-726, March 1998.
  3. C. Brumbley and C.-I Chang, "An iterative linear unmixing Kalman filtering approach to detection and estimation for signature abundance in remotely sensed imagery," 1998 Conference on Information Science and Systems, Princeton University, Princeton, NJ, pp. 269-274, March 1998.
  4. J. Zhang, C.-I Chang, S. Miller and K.A. Kang, "Multi-spectral image analysis for skin cancer study," 1998 Annual Mid-Atlantic Biochemical Engineering Consortium (MABEC) Meeting, Johns Hopkins University, Baltimore, March 20, 1998 (presentation).
  5. A. Ifarragaerri and C.-I Chang, "Projection pursuit analysis of hyperspectral scenes," SPIE, vol. 3370, pp. 51-59, April 1998.
  6. H. Ren and C.-I Chang, "A computer-aided detection and classification method for concealed targets in hyperspectral imagery," IEEE 1998 International Geoscience and Remote Sensing Symposium, Seattle, WA, pp. 1016-1018, July 5-10, 1998.
  7. C. Brumbley and C.-I Chang, "Unsupervised linear unmixing Kalman filtering approach to signature extraction and estimation for remotely sensed images," IEEE 1998 International Geoscience and Remote Sensing Symposium, Seattle, WA, pp. 1590-1592, July 5-10, 1998.
  8. C.-I Chang and Q. Du, "An interference rejection approach to noise adjusted principal components transform," IEEE 1998 International Geoscience and Remote Sensing Symposium, Seattle, WA, pp. 2059-2061, July 5-10, 1998.
  9. H. Ren and C.-I Chang, "A generalized orthogonal subspace projection approach to unsupervised multispectral image classification," SPIE Conference on Image and Signal Processing for Remote Sensing IV, vol. 3500, Spain, pp. 42-53, September 21-25, 1998.
  10. A. Ifarragaerri, H. Ren and C.-I Chang, "Target detection in hyperspectral images using projection pursuit with interference rejection," SPIE Conference on Image and Signal Processing for Remote Sensing IV, vol. 3500, Spain, pp. 54-62, September 21-25, 1998.
  11. P. Thouin and C.-I Chang, "Restoration of text images from 8x8 block DCT compression," SPIE Documentation Recognition, special edition, 1998.
  12. J. Zhang, C.-I Chang, S. Miller and K.A. Kang, "Multi-spectral image analysis for skin tumors using color and texture features," Abstract in 1998 Annual Biomedical Engineering Society Meeting, Cleveland, Ohio, Oct. 10-13, 1998, poster presentation.
  13. J. Zhang, C.-I Chang, S. Miller and K.A. Kang, "Multi-spectral image analysis for skin tumors," Abstract in  A Look Ahead II: Biomedical Research: A Life Sciences Symposium, UMBC, Baltimore, MD, Nov. 12, 1998, poster presentation.
  14. J. Zhang, C.-I Chang, S. Miller and K.A. Kang, "Principal components analysis for skin tumors," Abstract in 1998 Annual AIChE Meeting, Miami, Florida, Nov. 15-20, 1998, oral presentation.

1997 (11)

  1. C.-I Chang and C. Brumbley, "An orthogonalization target signature space projection approach to image classification in unknown background," 31st Conference on Information Sciences and Systems, The Johns Hopkins University, March, 1997, pp. 174-178.
  2. C. Brumbley and C.-I Chang, "A Kalman filtering approach to hyperspectral image classification and signature abundance estimation," 31st Conference on Information Sciences and Systems, The Johns Hopkins University, March, 1997, pp. 179-184.
  3. M. Cao, K.A. Kang, C.-I Chang, D. Bruley and S.J. Miller, "Skin cancer detection based on NIR image analysis," 16th Southern Biomedical Engineering Conf., Biloxi, Mississippi, April 4-6, 1997, pp. 431-436. (awarded for one of best students' paper)
  4. M.L.G. Althouse and C.-I Chang, "Use of IR spectroscopy for medical diagnostics," 1997 Annual National Heat Transfer Meeting, Aug. 10-12, Baltimore, MD, 1997.
  5. C.-S. Lo, S.-K. Lee, P.C. Chung and C.-I Chang, "An automatic computerized system for detection and segmentation of clustered microcalcifications on mammograms," Proc. the 46th Annual Meeting of Radiological Society of Republic of China, Mar. 27-28, 1997, p. F157.
  6. A. Ifarraguerri and C.-I Chang, "Hyperspectral image segmentation with convex cones," presented in Spectroradiometric Symposium,  San Diego, Nov. 2-7, 1997. (invited)
  7. H. Ren and C.-I Chang, "An unsupervised orthogonal subspace projection to target detection and classification in unknown environment," presented in Spectroradiometric Symposium,  San Diego, Nov. 2-7, 1997.  (invited)
  8. C.-I Chang, T.-L.E. Sun and M.L.G. Althouse, "An unsupervised interference rejection approach to target detection and classification for hyperspectral imagery," presented inSpectroradiometric Symposium,  San Diego, Nov. 2-7, 1997.  (invited).
  9. C.-I Chang, T.-L.E. Sun and M.L.G. Althouse, "Target classification by unsupervised interference rejection," Proc. Int. Sym. Special  Spectral Research (ISSSR), Dec. 14-17, San Diego, CA, 1997.
  10. A. Ifarragaerri and C.-I Chang, "Hyperspectral image segmentation with convex cone," Proc. Int. Sym.  Special Spectral  Research (ISSSR), Dec. 14-17, San Diego, CA, 1997.
  11. K.A. Kang, M Cao, J. Zhang, C.-I Chang and S.J. Miller, "Preliminary study on optical characterization of skin tumors by multispectral NIR images," AIChE's 1997 Annual Meeting, Nov. 16-21, Los Angeles,  CA, 1997. (presentation)

1996 (9)

  1. Y. Xu and C.-I Chang, "Implementation of a 3-D model for neocognitron," Proc. Int. Conf. Neural Networks,  Washington DC, June 3-6, pp. 794-799, 1996.
  2. J.-R. Tsai. P.C. Chung and C.-I Chang, "A sigmoidal radial basis function neural network for function approximation," Int. Conf. Neural Networks, Washington DC, June 3-6, 1996, pp. 496-501.
  3. S.-C. Lo, P.-C. Chung, B.-C. Hsu, C.-I Chang, S.K. Lee and B.-S. Liao, "An algorithm for detection and segmentation of clustered microcalcifications on mammograms," Proc. 2nd Medical Eng. Week of the World, 3rd Asian-Pacific Conf. on Medical and Biological Engineering, Taipei, Taiwan, ROC, p. 102, 1996.
  4. P.-S. Liao, B.C. Hsu, C.-S. Luo, P.-C. Chung, T.-S. Chen, S.-K. Lee, L. Cheng and  C.-I Chang, "Automatic detection of microcalcifications in digital mammograms," 18th Annual Int. Conf. IEEE Eng. in Med. and Bio. Society, Amsterdam, Netherlands, Oct. 31-Nov. 3, 1996, pp. 88-89.
  5. B.-C. Hsu, P.-C. Chung and C.-I Chang, "Automated system for detection and classification of microcalcifications in digital mammograms,"  Proc. CVGIP'96, Taiwan, ROC, pp. 127-134, 1996. (awarded for the best paper in the conference)
  6. C.-W. Yang, P.-C. Chung, B.-Chang Hsu and C.-I Chang, "A hierarchical neural model for shape recognition: shape cognitron," 1996 International Symposium on Multi-Technology Information Processing, Kaoshiung, Taiwan, ROC, pp. 195-200, 1996.
  7. C.-W. Yang, P.-C. Chung, C.-I Chang, J. Wang and M.L.G. Althouse, "Entropic and relative entropic thresholding," Joint Conf. 1996  International Computer Symposium, Dec. 19-21, Kaoshiung, Taiwan, ROC, pp. 82-89, 1996.
  8. C.-W. Yang, P.-C. Chung, C.-I Chang, S.-K. Lee and C.-H. Wen, "A hierarchical model for PACS," Joint Conf. 1996  International Computer Symposium, Dec. 19-21, Kaoshiung, Taiwan, ROC, pp. 195-202, 1996.
  9. C.-S. Lo, P.-C. Chung, C.-I Chang, and S.K. Lee, "A computerized system for detection and segmentation of clustered microcalcifications," Joint Conf. 1996 International Computer Symposium, Dec. 19-21, Kaoshiung, Taiwan, ROC, pp. 247-253, 1996.

1995 (5)

  1. M. Tu, C.-H. Chen and C.-I Chang, "A least squares orthogonal subspace projection approach to signature detection," Proc. Conference on Computer Applications, Nan Tou, Taiwan, R.O.C., pp. 120-125, April 21-22, 1995.
  2. C.-W. Yang, P.-C. Chung and C.-I Chang, "A pyramid approach to two-dimensional entropic thresholding," Proc. Conference on Computer Applications, Nan Tou, Taiwan, R.O.C., April 21-22, 1995, pp. 59-64.
  3. Y. Kim, J. Lee and C.-I Chang, "Tracking a maneuvering target with kinematic constraints using IMM method," Proc. IASTED (Int. Assoc. of Sci. and Tech. for Development),Int. Conf. Signal and Image Processing,  Las Vegas, Nevada,  Nov. 20-23, 1995.
  4. M.L.G. Althouse and C.-I Chang, "Image segmentation using local entropy methods," Proc. Int. Conf. Image Processing, Washington DC, Oct. 16-20, 1995, Vol. III, 61-65.
  5. M. Cao and C.-I Chang, "Image compression by neural network," Proc. International Symposium on Neural Networks, Hsinchu, Taiwan, ROC, Dec. 18-20, pp. D.3-19-D3-24, 1995.

1994 (8)

  1. C.-I Chang, J. Wang and M.L.G. Althouse, "Vapor cloud detection using relative entropy thresholding," Signal Processing, Sensor Fusion and Target Recognition III, Volume 2232, SPIE, Orlando, Florida, pp. 276-284, April, 1994.
  2. D. Komo, C.-I Chang and H. Ko, "Stock market index prediction using neural networks,"  Applications of Artificial Neural Networks V, Volume 2243, SPIE, Orlando, Florida, pp. 516-526, April 1994.
  3. C.-I Chang, Y. Cheng, J. Wang, M.L.G. Althouse and M.L. Chang, "Progressive edge extraction using multistage predictive coding," Proc. 1994 International Symposium on Speech, Image and Neural Networks,  Hong Kong, April 14-16, 1994, pp. 57-60.
  4. D. Komo, C.-I Chang and H. Ko, "Neural network technology for stock market index prediction," Proc. 1994 International Symposium on Speech, Image and Neural Networks,  Hong Kong, April 14-16, 1994, pp. 543-546.
  5. J.C. Harsanyi, W. Farrand and C.-I Chang, "Detection of subpixel spectral signatures in hyperspectral image sequences," Proceedings of American Congress on Surveying & Mapping (ACSM)/ American Society of Photogrammetry & Remote Sensing (ASPRS) Annual Converntion and Exposition, Baltimore, vol. 1, pp. 236-247, 1994.
  6. J.C. Harsanyi, W. Farrand, J. Hejl and C.-I Chang, "Automatic identification of spectral endmembers in hyperspectral image sequences," International Symposium on Spectral Sensing Research '94 (ISSSR), San Diego, July 10-15, pp. 267-277, 1994.
  7. M.L.G. Althouse, J. Wang, C.-I Chang and J. Harsanyi, "Target detection in multispectral images using relative entropy thresholding," International Symposium on Spectral Sensing Research '94 (ISSSR), San Diego, July 10-15, 1994.
  8. M.L.G. Althouse and C.-I Chang, "Chemical vapor detection and mapping with a multispectral FLIR," Proc. Instrumentation for Measurement and Imaging of Air  Emissions, SPIE, 1994.

1993 (7)

  1. J. Harsanyi, W. Farrand and C.-I Chang, "Determining the number and identity of spectral endmembers: an integrated approach using Neyman-Pearson eigen-thresholding and iterative constrained RMS error minimization," Proc. Ninth Thematic Conference on Geologic Remote Sensing, February, pp. , 1993.
  2. J. Harsanyi and C.-I Chang, "Hyperspectral image dimensionality reduction and pixel classification: an orthogonal subspace projection approach," Proc. 1993 Conf. on Information Sciences and Systems, Johns Hopkins University, Baltimore, MD, pp. 401-406, March 24-26, 1993.
  3. L. Wolfe and C.-I Chang, "N-level quantization of a source with an unknown parameter," Proc. 1993 Conf. on Information Sciences and Systems,  Johns Hopkins University, Baltimore, p. 355, March 24-26, 1993.
  4. J. Harsanyi and C.-I Chang, "Classification of hyperspectral image data: an orthogonal subspace projection approach," Proc. International Conf. on Signal Processing '93/Beijing, Beijing, China, pp. 874-877, Oct. 26-30, 1993.
  5. C.-J. Chang, C.-I Chang and M.-L. Chang, "Subband multistage predictive coding," Proc. International Conf. on Signal Processing '93/Beijing, Beijing, China, pp. 783-787, Oct. 26-30, 1993.
  6. R.H. Baran, H. Ko and C.-I Chang, "Signal detectability with neural network," Asia Pacific Conference on Communication, Taejon, Korea, pp. 6E.4.1-6.E.4.5, August 1993.
  7. C.-I Chang, J. Wang and M.L.G. Althouse, "Chemical vapor cloud detection using multistage entropic thresholding," Proc. Scientific Conference on Chemical Defense Research, ERDEC, Aberdeen Proving Ground, MD, November 16-19, 1993.

1992 (9)

  1. L.B. Wolfe and C.-I Chang, "A complete sufficient statistic for finite-state Markov processes with applications to source coding," Proceedings of 26th Annual Conference on Information Sciences and Systems, Princeton University, Princeton, N.J., pp. 878-883,  March 1992.
  2. K. Chen, M.L.G. Althouse and C.-I Chang, "A relative entropy approach to image thresholding," Proceedings of 26th Annual Conference on Information Sciences and Systems, Princeton University, Princeton, N.J., pp. 907-911,  March 1992
  3. M.L.G. Althouse, C.-I Chang and D. Smith, "Single frame multispectral thermal imagery,"  SPIE, vol. 1689, Orlando, Florida, April, 1992, pp. 20-24. 
  4. G.I. Goo, C.-I Chang and H.T. Goo, "A  novel approach to sonar target identification using back propagation neural network," Conference Proceedings SPIE, Orlando, Florida, pp. 738-749, April 24, 1992.
  5. M.L.G. Althouse and C.-I Chang, "Chemical vapor detection and mapping with a multi-spectral thermal imager, " Proc. 4th Int. Symp. Protection Against Chemical Warfare Agents, Stockholm, pp. 195-200, June 1992.
  6. Y. Cheng and C.-I Chang, "Multistage DPCM," Proc. IEEE Workshop on Visual Signal Processing and Communications, Raleigh, North Carolina,  pp. 188-193, Sep. 2-3, 1992.
  7. C.-I Chang, Y. Cheng, M.L.G. Althouse, L. Zhang and J. Wang, "Multistage image coding: a top-down gray-level triangle method," Proc. International Symposium on Spectral Sensing Research  (ISSSR), Kauai, Hawaii, pp. 497-511, Sep. 15-20, 1992. 
  8. M.L.G. Althouse and  C.-I Chang, "Chemical vapor detection with multispectral thermal imagery," Proc. International Symposium on Spectral Sensing Research  (ISSSR), Kauai, Hawaii, pp. 1088-1094., Sep. 15-20, 1992. 
  9. C.-I Chang and Y. Cheng and M.L.G. Althouse, "Chemical vapor detection using multistage predictive coding," Proc. Scientific Conference on Chemical Defense Research, CRDEC, Aberdeen Proving Ground, MD, pp. 909-915, Nov. 17-20, 1992. 

1991 (2)

  1. C.-I Chang and M.L.G. Althouse, "A systolic array algorithm and architecture of adaptive spatial filters for FLIR target detection," IEEE Workshop on Visual Signal Processing and Communications, Hsichu, Taiwan, pp. 110-115, June 6-7, 1991. 
  2. L.B. Wolfe and C.-I Chang, "Computation of rate-distortion function of parameterized first-order discrete binary Markov sources," 1991 International Symposium on Communications,  pp. 261-264,  December 10-13, 1991, Tainan, Taiwan.

1990 (7)

  1. C.-I Chang and L.D. Davisson, "An entropy-constrained quantization approach for a source characterized by random parameters," Abstracts of 1990 IEEE International Symposium on Information Theory, San Diego, CA, Jan. 14-19, 1990.
  2. C.-I Chang, "Rate-distortion function of nonergodic sources," 24th Annual 1990 Conf. on Inform. Science and Systems, Princeton University, NJ, pp. 344-348, Mar. 21-23, 1990.
  3. C.-F.T. Tang, C.-I Chang and Y.J. Chen, "On realizations of minimum variance  distortionless response beamformer by systolic arrays," 24th Annual 1990 Conf. on Information Science and Systems, Princeton University, NJ, 824-828, Mar. 21-23, 1990.
  4. J.J. Pan and  C.-I Chang, "Upward and downward continuation of gravity and magnetic data using linear programming," 1990  Society of Exploration Geophysicists 60th Annual Meeting, pp.  654-657, September 23-27, San Francisco, 1990.
  5. L. Wolfe and C.-I Chang, "Source matching problems revisited," Proc. International Conference on Signal Processing'90, Beijing, October 22-26, 1990, pp. 119-122.
  6. J.J. Pan and  C.-I Chang, "A simple filter design for destriping of LANDSAT MSS images," Proc. Int. Conf. on Signal Processing'90, Beijing, pp. 737-740, Oct. 22-26, 1990. 
  7. C.-F.T. Tang, C.-I Chang and Y.J. Chen, "A minimum variance  distortionless response beamformer with systolic array implementation," Proc. International Conference on Signal Processing'90, Beijing, October 22-26, 1990, pp. 1109-1112.

1989 (2)

  1. C.-I Chang, "An algorithm for calculating channel capacity," Proc. 1989 Conference on Information Sciences and Systems, Johns Hopkins University, Baltimore, MD, pp. 62-65, March 22-24, 1989.
  2. C.-I Chang and L.D. Davisson, "A counterpart of Remez's algorithms in statistical decision theory: Chang-Davisson's algorithms," Proc. IEEE 1989 International Conference on Acoustic, Speech and Signal  Processing, pp. 1345-1348, May 23-26, 1989, Glasgow,  Scotland, U.K.

1988 (3)

  1. C.-I Chang, S.C. Fan and L.D. Davisson, "A simple method of calculating channel capacity and finding minimax codes for source matching problems," Proc. 1988 Conf. on Inform. Sciences and Systems, Princeton University, Princeton, NJ, pp. 362-366, Mar. 1988.
  2. C.-I Chang, "A note on the computation of capacity of a discrete memoryless channel," Proc. 1988 Conf. on Inform. Sciences and Systems, Princeton University, Princeton, NJ, p. 367, Mar. 1988.
  3. C.-I Chang, L.C. Fan and L.D. Davisson, "Computation of the rate-distortion function of a source with uncertain statistics," Proc. IEEE 1988 International Conference on Communication  Systems, pp. 1180-1184, Oct. 31-Nov. 3, 1988, Singapore.

1987

  1. C.-I Chang and L.D. Davisson, "Universal source coding with partial prior information," Proc. 1987 Conf. on Inform. Sciences and Systems, The Johns Hopkins University, Baltimore, MD, pp. 245-249, March 1987.

1985

  1. C.-I Chang and L.D. Davisson, "On calculating the capacity of infinite-input finite-output channels," Abstracts in  Proc. IEEE 1985 International Symposium on Information Theory, p. 114, June 1985.

1983

  1. C.-I Chang and H.V. Poor, "A note on memory length and detection of Gaussian signals," Proc. 1983 Conf. on Inform. Sciences and Systems, The Johns Hopkins University, Baltimore, MD, pp. 539-543, Mar. 1983.

1981

  1. C.-I Chang and H.V. Poor, "On the performance of memoryless detection systems relative to systems with memory," Proc. 1981 Conf. on Inform. Sciences and Systems, The Johns Hopkins University, Baltimore, MD, pp. 140-146, March 1981.