[1]
K. A. Panetta, E. J. Wharton and S. S. Agaian, Human Visual System-Based Image Enhancement and Logarithmic Contrast Measure, IEEE Trans. Systems, Man, and Cybernetics, 38(1), pp.174-188, (2008).
DOI: 10.1109/tsmcb.2007.909440
Google Scholar
[2]
L. Tao, M Seow and V. K. Asari, Nonlinear Image Enhancement to Improve Face Detection in Complex Lighting Environment, International Journal of Computational Intelligence Research, 2(3), pp.327-336, (2006).
DOI: 10.5019/j.ijcir.2006.70
Google Scholar
[3]
D. Geronimo, A. M. Lo, and A. D. Sappa, Survey of Pedestrian Detection for Advanced Driver Assistance Systems, IEEE Trans Pattern Anal. Machine Intell, 32(7), pp.1239-58, (2010).
DOI: 10.1109/tpami.2009.122
Google Scholar
[4]
F. Lamberti, B. Montrucchio and A. Sanna, CMBFHE: a Novel Contrast Enhancement Technique based on Cascaded Multistep Binomial Filtering Histogram Equalization, IEEE Trans Consumer Electronics, 52(3), pp.966-974, (2006).
DOI: 10.1109/tce.2006.1706495
Google Scholar
[5]
K. Panetta, E. Wharton and S. Agaian, Human Visual System-Based Image Enhancement and Logarithmic Contrast Measure, IEEE Trans. Syst Man Cybern B Cybern, 38(1), pp.174-88, (2008).
DOI: 10.1109/tsmcb.2007.909440
Google Scholar
[6]
Jobson DJ, Rahman Z and W. GA, Properties and performance of a Center/Surround Retinex, IEEE Trans. Image Process, 6(3), pp.451-62, (1997).
DOI: 10.1109/83.557356
Google Scholar
[7]
K. L. Chung and W. J. Yang, Efficient edge-preserving algorithm for color contrast enhancement with application to color image segmentation, Journal of Visual Communication and Image Representation, 19(5), pp.299-310, (2008).
DOI: 10.1016/j.jvcir.2008.02.002
Google Scholar
[8]
H. Yoon and Y. Han, Image contrast enhancement based subhistogram equalization technique without over-equalization noise, World Academy of Science, Engineering and Technology, 50, pp.176-182, (2009).
Google Scholar
[9]
A. K. Jai, Fundamentals of Digital Image Processing, Prentice-Hall, (1989).
Google Scholar
[10]
Mohan, C. Papageorgiou, and T. Poggio, Example-based object detection in images by components, IEEE Trans. Pattern Analysis and Machine Intelligence, 23(4), pp.349-361, (2001).
DOI: 10.1109/34.917571
Google Scholar
[11]
D. Gavrila and V. Philomin, Real-time object detection for smart vehicles, In Proc. 7th ICCV, pages 87-93, (1999).
Google Scholar
[12]
P. Felzenszwalb, Learning Models for Object Recognition, In Proc. Conf. Computer Vision and Pattern Recognition, pages 56-62, (2001).
Google Scholar
[13]
N. Dalal and B. Triggs, Histograms of oriented gradients for human detection, In Proc. IEEE Comput. Soc. Conf. Comput. Vision and Pattern Recognition, (2005).
DOI: 10.1109/cvpr.2005.177
Google Scholar
[14]
S. Munder and D. Gavrila, An experimental study on pedestrian classification, IEEE Trans. Pattern Analysis and Machine Intelligence, 28(11), 1863-1868, (2006).
DOI: 10.1109/tpami.2006.217
Google Scholar
[15]
D. Comaniciu and P. Meer, Mean shift: A robust approach toward feature space analysis, IEEE Trans. Pattern Anal. Machine Intell. Vol 24(5), pp.603-619, (2002).
DOI: 10.1109/34.1000236
Google Scholar
[16]
P. Viola and M. Jones. Rapid object detection using a boosted cascade of simple features, IEEE Computer Vision and Pattern Recognition, pp.511-518, (2001).
DOI: 10.1109/cvpr.2001.990517
Google Scholar
[17]
Leibe, A. Leonardis, B. Schiele. Robust Object Detection With Interleaved Categorization and Segmentation, International journal of computer vision, 77(1), pp.259-289.
DOI: 10.1007/s11263-007-0095-3
Google Scholar
