Automatic recognition of silhouttes from images acquired in uncontrolled lighting conditions
Keywords:
Image denoising, Image enhancement, Image processing, Non-controlled illumination, Silhouettes
Abstract
The image acquisition process is not always realized in a controled environment, therefore it is affected by diverses variables and circumstances, being a necessity the involvement of protocols and measures to reduce the greater impacts. In the case of this investigation the objective is to obtain the silhouette of a person or group automatically whatever are the lighting conditions. A description of an experimental comparison of techniques designed to reduce the impact of lighting conditions during image acquisition to facilitate further analysis of the information contained therein.
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References
[1] Bouhamidi A An iterative method for bayesian gaussmarkov image restoration. Applied Mathematical Modelling 33 (2009) 361-372.
[2] Makaremi Iman Ahmadi Majid Blur invariants: a novel representation in the wavelet domain. Pattern recognition 43 (2010) 3950-3957.
[3] Sun Shao-jie et al Blind image deconvolution for single motion blurred image. The journal of China universities of Posts and Telecommunications. June 2010 17(3) 104-109
[4] Candes Emmanuel Compressed sensing makes every pixel counts. Whats happening in the mathematical sciences. (2011)
[5] E Ceseracciu et al, “Markless analysis of frontal crawl swimming,”
[6] Caglioti Vincenzo Giusti alessandro. Recovering ball motion from a single motion-blurred image. Computer vision and image understanding 113 (2009) 590-597
[7] Storring M et al., “Tracking regions of human skin through illumination changes,” Pattern recognitions letters 24 pp. 1715-1723, (2003)
[8] Shen C et al., ”Moving object tracking under varying illuminations conditions,” Pattern recognition letters 27 pp. 1632-1643, (2006),,
[9] Ranz J et al, “Algoritmos genéticos aplicados a la segmentación de imágenes con iluminación no controlada,” presentado en las jornadas de Automática, Tarragona, 2008 Universidad Complutense de Madrid 1-6
[10] Nayak A S Chaudhuri, “Automatic illumination correction for scene enhacement and object tracking,” Image and vision computing 24 (2006) pp. 949-959
[11] Hampson F JC Pesquet, ”Motion estimation in the presence of illumination variations,” Signal processing: Image communication 16 (2000) 373-381
[12] Rav-acha A Peleg Schmuel. Two motion blurred images are better than one. Pattern recognition letters 26 (2005) 311-317
[13] Dong Y, Desouza GN adaptive learning of multi-subspace for foreground detection under illumination changes. Computer vision and image understanding 115 (2011),,
[14] Tafazzoli Faezeh, Safabakhsh Reza, Model-based human gait recognition using leg arm movements. Engineering applications of artificial intelligence (2010)
[15] González, R. Woods. Digital Image Processing using Matlab. Upper Saddle River: Pearson - Prentice Hall, 2004, pp. 954
[16] Pajares Martínez, Gonzalo et al., Visión Por Computador: Imágenes Digitales Y Aplicaciones. México : Alfaomega : Ra-Ma, 2008
[17] Oliveira Joao et al., Adaptive total variation image deblurring. A majorization -minimization approach. Signal Processing 89 (2009) 1683-1693
[18] Stauffer Chris Grimson WEL Adaptive background mixture models for real time tracking. The artificial Inteligence Laboratory MIT, Conference on Computer Vision and Pattern Recognition (1998)
[19] Moeslund Thomas et al., A survey of advances in vision based human motion capture and analysis Computer Vision and Image Understanding 104 (2006) 90-126
[20] Abril Mauricio, Valencia Mauricio, et al., Sistema de visión artificial para el registro de densidad peatonal en tiempo real Scientia et technica, agosto año/vol XIII No. 035 (2007) Universidad Tecnológica de Pereira pp. 79-83,,
[21] Carfagni Monica et al., Real time machine visión system for monitoring the textile raising process Computers in Industry 56 (2005) 831-842
[22] Lu Ye, Li Ze-Nian Automatic object extraction and reconstruction in active video Pattern Recognition 41 (2008) 1159-1172
[23] Rebelo F, Ferreira C, Correia Da Silva K Sintese de padroes motores de membro superior optimizados para aplicaçoes em design ergonómico VI Congreso Nacional de mecánica aplicada e Computacional 10 pág. Disponible online http://home.fmh.utl. pt/~cferreira/ pdf/aveiro2000.pdf en noviembre 2009
[24] Abi M H et al., “An automated method for analysis of gait data to aid clinical interpretation,” presented at Biomedical Engineering (MECBME), 2011 1st. Middle East Conference pp. 119-121
[25] Lam T et al, ”Gait flow image: a silhouette based gait representation for human identification,” Pattern recognition 44 pp. 973-987, (2011)
[26] Tzevanidis K A Argyros, “Unsupervised learning of background modeling parameters in multicamera Systems,” Computer vision and Image understanding 115 (2011) 105-116
[27] Bae, J M Tomizuka, “Gait phase analysis based on a hidden markov model,” Mechatronics (2011)
[28] Agrawal amit Raskar Ramesh Optimal single image capture for motion deblurring pp. 1-8, Conference on Computer Vision and Pattern Recognition (2009)
[29] Mann Steve comparametric equations with practical applications in quantigraphic image processing. IEEE transactions on image processing 9 (8) (2000) 1389-1406
[30] Cai jian Feng et al., Blind motion deblurring using multiple images. Journal of computacional physics 228 (2009) 5057-5071
[31] Sanin Andres et al. Shadow detection: a survey and comparative evaluation of recent methods. Pattern recognition 45 (2012) 1684-1695
[32] Ginesu Giaime et al A multi factor approach for image quality assessment based on a human visual system model. Signal Processing Image communication 21 (2006) 316-333
[33] Zhang xin. Jing xili. Image denoising in contourlet domain based on a normal inverse gaussian prior. Digital signal processing 20 (2010) 1439-1446
[34] Pankjakshan P kumar V Detail preserving image information restoration guided by SVM based noise mapping. Digital Signal Processing 17 (2007) 561-577Journal of biomechanics 44 (2011) 2236- 2242
[2] Makaremi Iman Ahmadi Majid Blur invariants: a novel representation in the wavelet domain. Pattern recognition 43 (2010) 3950-3957.
[3] Sun Shao-jie et al Blind image deconvolution for single motion blurred image. The journal of China universities of Posts and Telecommunications. June 2010 17(3) 104-109
[4] Candes Emmanuel Compressed sensing makes every pixel counts. Whats happening in the mathematical sciences. (2011)
[5] E Ceseracciu et al, “Markless analysis of frontal crawl swimming,”
[6] Caglioti Vincenzo Giusti alessandro. Recovering ball motion from a single motion-blurred image. Computer vision and image understanding 113 (2009) 590-597
[7] Storring M et al., “Tracking regions of human skin through illumination changes,” Pattern recognitions letters 24 pp. 1715-1723, (2003)
[8] Shen C et al., ”Moving object tracking under varying illuminations conditions,” Pattern recognition letters 27 pp. 1632-1643, (2006),,
[9] Ranz J et al, “Algoritmos genéticos aplicados a la segmentación de imágenes con iluminación no controlada,” presentado en las jornadas de Automática, Tarragona, 2008 Universidad Complutense de Madrid 1-6
[10] Nayak A S Chaudhuri, “Automatic illumination correction for scene enhacement and object tracking,” Image and vision computing 24 (2006) pp. 949-959
[11] Hampson F JC Pesquet, ”Motion estimation in the presence of illumination variations,” Signal processing: Image communication 16 (2000) 373-381
[12] Rav-acha A Peleg Schmuel. Two motion blurred images are better than one. Pattern recognition letters 26 (2005) 311-317
[13] Dong Y, Desouza GN adaptive learning of multi-subspace for foreground detection under illumination changes. Computer vision and image understanding 115 (2011),,
[14] Tafazzoli Faezeh, Safabakhsh Reza, Model-based human gait recognition using leg arm movements. Engineering applications of artificial intelligence (2010)
[15] González, R. Woods. Digital Image Processing using Matlab. Upper Saddle River: Pearson - Prentice Hall, 2004, pp. 954
[16] Pajares Martínez, Gonzalo et al., Visión Por Computador: Imágenes Digitales Y Aplicaciones. México : Alfaomega : Ra-Ma, 2008
[17] Oliveira Joao et al., Adaptive total variation image deblurring. A majorization -minimization approach. Signal Processing 89 (2009) 1683-1693
[18] Stauffer Chris Grimson WEL Adaptive background mixture models for real time tracking. The artificial Inteligence Laboratory MIT, Conference on Computer Vision and Pattern Recognition (1998)
[19] Moeslund Thomas et al., A survey of advances in vision based human motion capture and analysis Computer Vision and Image Understanding 104 (2006) 90-126
[20] Abril Mauricio, Valencia Mauricio, et al., Sistema de visión artificial para el registro de densidad peatonal en tiempo real Scientia et technica, agosto año/vol XIII No. 035 (2007) Universidad Tecnológica de Pereira pp. 79-83,,
[21] Carfagni Monica et al., Real time machine visión system for monitoring the textile raising process Computers in Industry 56 (2005) 831-842
[22] Lu Ye, Li Ze-Nian Automatic object extraction and reconstruction in active video Pattern Recognition 41 (2008) 1159-1172
[23] Rebelo F, Ferreira C, Correia Da Silva K Sintese de padroes motores de membro superior optimizados para aplicaçoes em design ergonómico VI Congreso Nacional de mecánica aplicada e Computacional 10 pág. Disponible online http://home.fmh.utl. pt/~cferreira/ pdf/aveiro2000.pdf en noviembre 2009
[24] Abi M H et al., “An automated method for analysis of gait data to aid clinical interpretation,” presented at Biomedical Engineering (MECBME), 2011 1st. Middle East Conference pp. 119-121
[25] Lam T et al, ”Gait flow image: a silhouette based gait representation for human identification,” Pattern recognition 44 pp. 973-987, (2011)
[26] Tzevanidis K A Argyros, “Unsupervised learning of background modeling parameters in multicamera Systems,” Computer vision and Image understanding 115 (2011) 105-116
[27] Bae, J M Tomizuka, “Gait phase analysis based on a hidden markov model,” Mechatronics (2011)
[28] Agrawal amit Raskar Ramesh Optimal single image capture for motion deblurring pp. 1-8, Conference on Computer Vision and Pattern Recognition (2009)
[29] Mann Steve comparametric equations with practical applications in quantigraphic image processing. IEEE transactions on image processing 9 (8) (2000) 1389-1406
[30] Cai jian Feng et al., Blind motion deblurring using multiple images. Journal of computacional physics 228 (2009) 5057-5071
[31] Sanin Andres et al. Shadow detection: a survey and comparative evaluation of recent methods. Pattern recognition 45 (2012) 1684-1695
[32] Ginesu Giaime et al A multi factor approach for image quality assessment based on a human visual system model. Signal Processing Image communication 21 (2006) 316-333
[33] Zhang xin. Jing xili. Image denoising in contourlet domain based on a normal inverse gaussian prior. Digital signal processing 20 (2010) 1439-1446
[34] Pankjakshan P kumar V Detail preserving image information restoration guided by SVM based noise mapping. Digital Signal Processing 17 (2007) 561-577Journal of biomechanics 44 (2011) 2236- 2242
Published
2014-11-26
How to Cite
Sanabria Sarmiento, J., & Archila Díaz, J. (2014). Automatic recognition of silhouttes from images acquired in uncontrolled lighting conditions. ITECKNE, 9(1), 107-114. https://doi.org/https://doi.org/10.15332/iteckne.v9i1.2751
Issue
Section
Research and Innovation Articles
