Science Journal of Circuits, Systems and Signal Processing
Volume 2, Issue 5, October 2013, Pages: 100-105
Received: Aug. 12, 2013;
Published: Sep. 10, 2013
Views 2263 Downloads 180
Mousa Mojarrad, Department of Computer, Firoozabad Branch, Islamic Azad University, Firoozabad, Iran
Sedigheh Kargar, Islamic Azad University, Bushehr Science and Research Branch , Bushehr, Iran
The main parameters of the human body can identify and estimate images easier. In this research, various images of people (short, long, lean and obese) were examined and their main features were extracted from the images. 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 images. 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 the output.
Measuring the Main Parameters of the Human Body in Images by Canny Edge Detector, Science Journal of Circuits, Systems and Signal Processing.
Vol. 2, No. 5,
2013, pp. 100-105.
Gavrila D M (1999) the Visual Analysis of Human Movement: A Survey, Compute Vis and Image Underst73:82-98.
Allen B, Curless B. Popovi Z , Hertzmann A (2006) Learning a correlated model of identity and pose dependent body shape variation for real-time synthesis, Eurographics/ ACM SIGGRAPH Symposium on Computer Animation .
Kadyrov M P (2001) The Trace Transform and It's Applications", IEEE Trans on Pat Anal and Mach Intell 23: 811-828.
Fedotov N, L. Shulga (2000) 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, Plze, Czech Republic, Feb. 2000.
Wolberg G, Zokai S (2003) Robust Image Registration using Log-polar Transform", wwwcs. engr.ccny.edu/~wolberg/pub/icip00.pdf (last visit20.7.2003).
Ju S, Black M, Yacoob Y (1996) Cardboard people: A parameterized model of articulated motion. I\/. /nt. Conf. on Automi Face and Gesture Recog: 38–44.
Wu Y, Hua G, T Yu (2003) Tracking articulated body by dynamic Markov network, ICCV, 1094–1101.
Sminchisescu C. Triggs B (2001) Covariance scaled sampling for monocular 3D body tracking, CVPR 1:447–454.
Sidenbladh H, Black M, Fleet D (2000) Stochastic tracking of 3D human figures using 2D image motion, ECCV 2: 702–718.
MacCormick J, Isard M (2000) Partitioned sampling, articulated objects, and interface-quality hand tracking. ECCV 2: 3–19.
Burl M, Weber M, Perona P (1998) A probabilistic approach to object recognition using local photometry and global geometry, ECCV: 628–641.
Ioffe S, Forsyth D (2001) Probabilistic methods for finding people, IJCV 43(1):45–68.
Ramanan D, Forsyth D (2003) Finding and tracking people from the bottom up CVPR II: 467–716.
Yu S, Gross R Shi, J (2003)Object segmentation by graph partitioning Concurrent object recognition and segmentation by graph partitioning, Advanc in Neur Info. Proc. Sys 15: 1407–1414.
Felzenszwalb P, Huttenlocher D (2000) Efficient matching of pictorial structures, CVPR 2: 66–73.
Jordan M , Sejnowski T , T. Poggio(2001)Graphical models: Foundations of neural computation, MIT Press .
Hochberg, Julian E. Brooks V(1962) Pictorial recognition as an unlearned ability: A study of one child’s performance American Journal of Psychology, 75 : 624-628.
Barnard, S T (1983) Interpreting perspective images, Artificial Intelligence 21 : 435-462.
Lowe D G, Binford T O (1985) the recovery of threedimensional structure from image curves, IEEE Trans. on Pattern Analysis and Machine Intelligence 7(3) 320-326.
Oren M, Papageorgiour C, Sinha P, Osuma E, Poggio T(1997) Pedestrian detection using wavelet templates. In Proc. Comp. Vis. and Pattern Rec., 193–199.
Gavrila D (2000) Pedestrian detection from a moving vehicle. In Proc. European Conf. Comp. Vis. 37–49.
Viola P, Jones M, and Snow D(2003) Detecting pedestrians using patterns of motion and appearance". In Proc. Int. Conf. Comp. Vis.734–741.
Fujiyoshi L H, Patil R (1998) Moving target classification and tracking from real-time video". In Proc. Wkshp. Applications of Comp. Vis.
Frome D. Huber R. Kolluri T B, Malik J(2004) Recognizing objects in range data using regional point descriptors, In Proc. of the Europ. Conf. on Computer Vision (ECCV).
Ajmal S. Mian, Mohammed Bennamoun, Robyn A(2004) Owens. "Matching tensors for automatic correspondence and registration, In Proc. of the Europ. Conf. on Computer Vision (ECCV).
Wu Z, Wang Y , Pan G(2003) 3D face recognition using local shape Map, In Proc. of IEEE Intern. Conf. on Image Processing, 2003– 2006.
Li X, Guskov I (2005) Multiscale features for approximate alignment of point-based surfaces". In Symp. on Geometry Processing, 217–226.
Osada R, Funkhouser T, Chazelle B, Dobkin D (2001)Matching 3D models with shape distributions. In Shape Modeling Internationa, 154– 166.
Lowe D G(2004) "Distinctive image features from scale-invariant keypoints, Int. J. of Comp. Vision, 2004.
Weber M (2000) Unsupervised Learning of Models for Object Recognition, Ph.D thesis, Department of Computation and Neural Systems, California Institute of Technology, Pasadena, CA.
Fei-Fei L, Fergus R, Perona P (2003) A bayesian approach to unsupervised one-shot learning of object categories. Proc. Int. Conf. on Comp. Vision, Nice, France.
Ioffe S, Forsyth D A (2001) Probabilistic Methods for Finding People, Internat J of Comput Vis 43(1): 45–68.
Gonzales R C, Woods R E (1992) Digital Image Processing", USA, Addison-wesley.