Detection, feature extraction and pose estimation of
people in images and video is made challenging by the variability of
human appearance, the complexity of natural scenes and the high
dimensionality of articulated body models and also the important
field in Image, Signal and Vision Computing in recent years. In this
paper, four types of people in 2D dimension image will be tested and
proposed. The system will extract the size and the advantage of them
(such as: tall fat, short fat, tall thin and short thin) from image. Fat
and thin, according to their result from the human body that has been
extract from image, will be obtained. Also the system extract every
size of human body such as length, width and shown them in output.
[1] D.M. Gavrila, "The Visual Analysis of Human Movement: A
Survey,"Computer Vision and Image Understanding, vol. 73, no. 1, pp.
82-98, 1999.
[2] 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.
[3] KADYROV, M. PETROU: "The Trace Transform and Its Applications",
IEEE Transactions on Pattern Analysis and Machine Intelligence,
Vol.23, No.8, pp. 811-828, August 2001
[4] N. FEDOTOV, L. SHULGA: "New Theory of Pattern Recognition on
the Basis of Stochastic Geometry", in Proc. WSCG 2000, the 8-th
International Conference in Central Europe on Computer Graphics,
Visualisation and Digital Media 2000, Plzen, Czech Republic, Feb.
2000.
[5] G. WOLBERG, S. ZOKAI: "Robust Image Registration using Log-polar
Transform", wwwcs. engr.ccny.edu/~wolberg/pub/icip00.pdf (last visit
20.7.2003).
[6] S. Ju, M. Black and Y. Yacoob. Cardboard people: A parameterized
model of articulated motion. Int. Conf. on Automatic Face and Gesture
Recognition, pp. 38-44, 1996.
[7] Y. Wu, G. Hua and T. Yu, Tracking articulated body by dynamic
Markov network, ICCV, pp. 1094-1101, 2003.
[8] C. Sminchisescu and B. Triggs. Covariance scaled sampling for
monocular 3D body tracking, CVPR, vol. 1 pp. 447-454, 2001.
[9] H. Sidenbladh, M. Black and D. Fleet. Stochastic tracking of 3D human
figures using 2D image motion, ECCV, vol. 2, pp. 702-718, 2000.
[10] J. MacCormick and M. Isard. Partitioned sampling, articulated objects,
and interface-quality hand tracking. ECCV (2), pp. 3-19, 2000.
[11] M. Burl, M. Weber and P. Perona . A probabilistic approach to object
recognition using local photometry and global geometry, ECCV, pp.
628-641, 1998.
[12] S. Ioffe and D. Forsyth. Probabilistic methods for finding people, IJCV
43(1):45-68, 2001.
[13] D. Ramanan and D. Forsyth. Finding and tracking people from the
bottom up, CVPR, Vol. II, pp. 467-716, 2003.
[14] S. Yu, R. Gross, and J. Shi. Object segmentation by graph partitioning
Concurrent object recognition and segmentation by graph partitioning,
Advances in Neural Info. Proc. Sys. 15, pp. 1407-1414, 2003.
[15] P. Felzenszwalb and D. Huttenlocher. Efficient matching of pictorial
structures, CVPR, Vol. 2, pp. 66-73, 2000.
[16] M. Jordan, T. Sejnowski and T. Poggio. Graphical models: Foundations
of neural computation, MIT Press, 2001.
[17] Hochberg, Julian E. andVirginiaBrooks, "Pictorial recognition as an
unlearned ability: A study of one child-s performance," American
Journal of Psychology, 75 (1962), 624-628.
[18] Barnard, Stephen T., "Interpreting perspective images," Artificial
Intelligence, 21 (1983), 435-462.
[19] Lowe, David G., and Thomas O. Binford, "The recovery of threedimensional
structure from image curves," IEEE Trans. on Pattern
Analysis and Machine Intelligence, 7, 3 (May 1985), 320-326.
[20] M. Oren, C. Papageorgiour, P. Sinha, E. Osuma, and T. Poggio."
Pedestrian detection using wavelet templates". In Proc. Comp. Vis. and
Pattern Rec., pages 193-199. IEEE, 1997.
[21] D. Gavrila. Pedestrian detection from a moving vehicle. In Proc.
European Conf. Comp. Vis., pages 37-49, 2000.
[22] P. Viola, M. Jones, and D. Snow. "Detecting pedestrians using patterns
of motion and appearance". In Proc. Int. Conf. Comp. Vis., pages 734-
741, 2003.
[23] Lipton, H. Fujiyoshi, and R. Patil. "Moving target classification and
tracking from real-time video". In Proc. Wkshp. Applications of Comp.
Vis., 1998.
[24] Frome, D. Huber, R. Kolluri, T. Bulow, and J. Malik. "Recognizing
objects in range data using regional point descriptors". In Proc. of the
Europ. Conf. on Computer Vision (ECCV), 2004.
[25] Ajmal S. Mian, Mohammed Bennamoun, and Robyn A. Owens.
"Matching tensors for automatic correspondence and registration". In
Proc. of the Europ. Conf. on Computer Vision (ECCV), 2004.
[26] Z. Wu, Y. Wang, and G. Pan. "3D face recognition using local shape
map". In Proc. of IEEE Intern. Conf. on Image Processing, pages 2003-
2006, 2004.
[27] ] X. Li and I. Guskov. "Multiscale features for approximate alignment of
point-based surfaces". In Symp. on Geometry Processing, pages 217-
226, 2005.
[28] R. Osada, T. Funkhouser, B. Chazelle, and D. Dobkin. "Matching 3D
models with shape distributions. In Shape Modeling Internationa"l,
pages 154- 166, 2001.
[29] D.G. Lowe: "Distinctive image features from scale-invariant keypoints"
accepted paper, Int. J. of Comp. Vision, 2004.
[30] M. Weber: "Unsupervised Learning of Models for Object Recognition",
Ph.D thesis, Department of Computation and Neural Systems, California
Institute of Technology, Pasadena, CA, 2000
[31] L. Fei-Fei, R. Fergus and P. Perona: "A bayesian approach to
unsupervised one-shot learning of object categories", Proc. Int. Conf. on
Comp. Vision, Nice, France, 2003.
[32] S. Ioffe and d.a. Forsyth.2001."Probabilistic Methods for Finding
People," International Journal of Computer Vision 43(1), 45-68, 2001.
[33] Gonzales R.C.and Woods R.E, (1992)."Digital Image Processing", USA,
Addison-wesley.
[1] D.M. Gavrila, "The Visual Analysis of Human Movement: A
Survey,"Computer Vision and Image Understanding, vol. 73, no. 1, pp.
82-98, 1999.
[2] 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.
[3] KADYROV, M. PETROU: "The Trace Transform and Its Applications",
IEEE Transactions on Pattern Analysis and Machine Intelligence,
Vol.23, No.8, pp. 811-828, August 2001
[4] N. FEDOTOV, L. SHULGA: "New Theory of Pattern Recognition on
the Basis of Stochastic Geometry", in Proc. WSCG 2000, the 8-th
International Conference in Central Europe on Computer Graphics,
Visualisation and Digital Media 2000, Plzen, Czech Republic, Feb.
2000.
[5] G. WOLBERG, S. ZOKAI: "Robust Image Registration using Log-polar
Transform", wwwcs. engr.ccny.edu/~wolberg/pub/icip00.pdf (last visit
20.7.2003).
[6] S. Ju, M. Black and Y. Yacoob. Cardboard people: A parameterized
model of articulated motion. Int. Conf. on Automatic Face and Gesture
Recognition, pp. 38-44, 1996.
[7] Y. Wu, G. Hua and T. Yu, Tracking articulated body by dynamic
Markov network, ICCV, pp. 1094-1101, 2003.
[8] C. Sminchisescu and B. Triggs. Covariance scaled sampling for
monocular 3D body tracking, CVPR, vol. 1 pp. 447-454, 2001.
[9] H. Sidenbladh, M. Black and D. Fleet. Stochastic tracking of 3D human
figures using 2D image motion, ECCV, vol. 2, pp. 702-718, 2000.
[10] J. MacCormick and M. Isard. Partitioned sampling, articulated objects,
and interface-quality hand tracking. ECCV (2), pp. 3-19, 2000.
[11] M. Burl, M. Weber and P. Perona . A probabilistic approach to object
recognition using local photometry and global geometry, ECCV, pp.
628-641, 1998.
[12] S. Ioffe and D. Forsyth. Probabilistic methods for finding people, IJCV
43(1):45-68, 2001.
[13] D. Ramanan and D. Forsyth. Finding and tracking people from the
bottom up, CVPR, Vol. II, pp. 467-716, 2003.
[14] S. Yu, R. Gross, and J. Shi. Object segmentation by graph partitioning
Concurrent object recognition and segmentation by graph partitioning,
Advances in Neural Info. Proc. Sys. 15, pp. 1407-1414, 2003.
[15] P. Felzenszwalb and D. Huttenlocher. Efficient matching of pictorial
structures, CVPR, Vol. 2, pp. 66-73, 2000.
[16] M. Jordan, T. Sejnowski and T. Poggio. Graphical models: Foundations
of neural computation, MIT Press, 2001.
[17] Hochberg, Julian E. andVirginiaBrooks, "Pictorial recognition as an
unlearned ability: A study of one child-s performance," American
Journal of Psychology, 75 (1962), 624-628.
[18] Barnard, Stephen T., "Interpreting perspective images," Artificial
Intelligence, 21 (1983), 435-462.
[19] Lowe, David G., and Thomas O. Binford, "The recovery of threedimensional
structure from image curves," IEEE Trans. on Pattern
Analysis and Machine Intelligence, 7, 3 (May 1985), 320-326.
[20] M. Oren, C. Papageorgiour, P. Sinha, E. Osuma, and T. Poggio."
Pedestrian detection using wavelet templates". In Proc. Comp. Vis. and
Pattern Rec., pages 193-199. IEEE, 1997.
[21] D. Gavrila. Pedestrian detection from a moving vehicle. In Proc.
European Conf. Comp. Vis., pages 37-49, 2000.
[22] P. Viola, M. Jones, and D. Snow. "Detecting pedestrians using patterns
of motion and appearance". In Proc. Int. Conf. Comp. Vis., pages 734-
741, 2003.
[23] Lipton, H. Fujiyoshi, and R. Patil. "Moving target classification and
tracking from real-time video". In Proc. Wkshp. Applications of Comp.
Vis., 1998.
[24] Frome, D. Huber, R. Kolluri, T. Bulow, and J. Malik. "Recognizing
objects in range data using regional point descriptors". In Proc. of the
Europ. Conf. on Computer Vision (ECCV), 2004.
[25] Ajmal S. Mian, Mohammed Bennamoun, and Robyn A. Owens.
"Matching tensors for automatic correspondence and registration". In
Proc. of the Europ. Conf. on Computer Vision (ECCV), 2004.
[26] Z. Wu, Y. Wang, and G. Pan. "3D face recognition using local shape
map". In Proc. of IEEE Intern. Conf. on Image Processing, pages 2003-
2006, 2004.
[27] ] X. Li and I. Guskov. "Multiscale features for approximate alignment of
point-based surfaces". In Symp. on Geometry Processing, pages 217-
226, 2005.
[28] R. Osada, T. Funkhouser, B. Chazelle, and D. Dobkin. "Matching 3D
models with shape distributions. In Shape Modeling Internationa"l,
pages 154- 166, 2001.
[29] D.G. Lowe: "Distinctive image features from scale-invariant keypoints"
accepted paper, Int. J. of Comp. Vision, 2004.
[30] M. Weber: "Unsupervised Learning of Models for Object Recognition",
Ph.D thesis, Department of Computation and Neural Systems, California
Institute of Technology, Pasadena, CA, 2000
[31] L. Fei-Fei, R. Fergus and P. Perona: "A bayesian approach to
unsupervised one-shot learning of object categories", Proc. Int. Conf. on
Comp. Vision, Nice, France, 2003.
[32] S. Ioffe and d.a. Forsyth.2001."Probabilistic Methods for Finding
People," International Journal of Computer Vision 43(1), 45-68, 2001.
[33] Gonzales R.C.and Woods R.E, (1992)."Digital Image Processing", USA,
Addison-wesley.
@article{"International Journal of Information, Control and Computer Sciences:52495", author = "Mousa Mojarrad and Amir Masoud Rahmani and Mehrab Mohebi", title = "Detection and Pose Estimation of People in Images", abstract = "Detection, feature extraction and pose estimation of
people in images and video is made challenging by the variability of
human appearance, the complexity of natural scenes and the high
dimensionality of articulated body models and also the important
field in Image, Signal and Vision Computing in recent years. In this
paper, four types of people in 2D dimension image will be tested and
proposed. The system will extract the size and the advantage of them
(such as: tall fat, short fat, tall thin and short thin) from image. Fat
and thin, according to their result from the human body that has been
extract from image, will be obtained. Also the system extract every
size of human body such as length, width and shown them in output.", keywords = "Analysis of Image Processing, Canny Edge
Detection, Human Body Recognition, Measurement, Pose
Estimation, 2D Human Dimension.", volume = "3", number = "1", pages = "45-6", }
