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Feature's Extraction of Human Body Composition in Images by Segmentation Method

Authors: Mousa Mojarrad, Mashallah Abbasi Dezfouli, Amir Masoud Rahmani


Detection and recognition of the Human Body Composition and extraction their measures (width and length of human body) in images are a major issue in detecting objects and the important field in Image, Signal and Vision Computing in recent years. Finding people and extraction their features in Images are particularly important problem of object recognition, because people can have high variability in the appearance. This variability may be due to the configuration of a person (e.g., standing vs. sitting vs. jogging), the pose (e.g. frontal vs. lateral view), clothing, and variations in illumination. In this study, first, Human Body is being recognized in image then the measures of Human Body extract from the image.

Keywords: Segmentation, classification, Feature Extraction, canny edge detection, Analysis of Image Processing, Human Body Recognition

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[1] Brett Allen1. And Brian Curless. And Zoran Popovi. And Aaron Hertzmann.2006." Learning a correlated model of identity and posedependent body shape variation for real-time synthesis," Eurographics/ ACM SIGGRAPH Symposium on Computer Animation 2006.
[2] G. Mori and J. Malik." Estimating Human Body Configurations using Shape Context Matching". ECCV 2002, pp 666-680.
[3] C. Barron, I. A. Kakadiaris." Estimating anthropometry and pose from a single image", CVPR 2000, vol.1, pp. 669-676.
[4] C.J. Taylor. "Reconstruction of articulated objects from point correspondences in a single uncalibrated image". CVIU 80(3): 349-63, December 2000.
[5] Mikel D. Rodriguez and Mubarak Shah.2007."Detecting and Segmenting Humans in Crowded Scenes,"ACM September 23-28, 2007.
[6] S. Ioffe and d.a. Forsyth.2001."Probabilistic Methods for Finding People," International Journal of Computer Vision 43(1), 45-68, 2001.
[7] Micilotta, E. Ong, and R. Bowden." Detection and Tracking of Humans by Probabilistic Body Part Assembly". BMVC, 2005.
[8] K. Mikolajczyk, C. Schmid, and A. Zisserman. "Human detection based on a probabilistic assembly of robust part detectors". ECCV, 1:69{81}, 2004.
[9] T. Roberts, S. McKenna, and I. Ricketts." Human poses estimation using learnt probabilistic region similarities and partial configurations". ECCV, 4:291{303}, 2004.
[10] R. Ronfard, C. Schmid, and B. Triggs." Learning to parse pictures of people". ECCV, 4:700{714}, 2002.
[11] Oren, M., Papageorgiou, C., Sinha, P., and Osuna, E."Pedestrian detection using wavelet templates". In IEEE Conf.on Computer Vision and Pattern Recognition, pp. 193-199 .1997.
[12] Niyogi, S.A.and Adelson, E.H.1995. "Analyzing and recognizing walking figures in xyt.Media lab Vision and modeling" tr-223, MIT, Cambridge, MA.
[13] Liu, F. and Picard, R.W.1996."Detecting and segmenting periodic motion. Media lab Vision and modeling" tr-400, MIT, Cambridge, MA.
[14] R. Cutler and L. S. Davis. (2000)."Robust real - time periodic motion detection". Analysis and applicatons.IEEE .Pattern Analysis and Machine Intelligence, 22(8):781-769.
[15] Poggio, T. and Sung, K,-K. 1995." Finding human faces with a Gaussian mixture distribution-based face model". In Asia Conf. On Computer Vision, pp. 435-440.
[16] H.A.Rowley, S. Baluja, and T. Kanade. (1998)."Rotation invariant neural network-based face detection". In IEEE Conf.onComputer Vision and Pattern Recognition, pp. 38-44.
[17] Faugeras, O.D.and Hebert, M.1986." The representation, recognition, and locating of 3-D objects". International Journal of Robotics Research, 5(3):27-52.
[18] Thompson.D.W.and Mundy .J.L. (1987). "Three- dimensional model matching form an unconstrained viewpoint", In IEEE Int. Conf.on robotics and automation, Raleigh, NC, pp. 208-220.
[19] Gonzales R.C.and Woods R.E, (1992)."Digital Image Processing", USA, Addison-wesley.