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5. Conference Proceedings

5. Conference Proceedings

Mar 12, 2018

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.