Model Driven Segmentation of Articulating Humans in Laplacian Eigenspace

• Published in: IEEE transactions on pattern analysis and machine intelligence Vol. 30; no. 10; pp. 1771 - 1785
• Main Authors: Sundaresan, A., Chellappa, R.
• Format: Journal Article
• Language: English
• Published: Los Alamitos, CA IEEE 01.10.2008; IEEE Computer Society; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Application software; Applied sciences; Articulated; Artificial Intelligence; Biological system modeling; Cameras; Computer science; control theory; systems; Computer Simulation; Exact sciences and technology; Graph-theoretic methods; Graphs; Human; Human body; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Image Processing and Computer Vision; Image segmentation; Imaging, Three-Dimensional - methods; Joints; Joints - anatomy & histology; Laplace equations; Limbs; Models, Anatomic; Motion estimation; partitioning; Pattern Recognition; Pattern Recognition, Automated - methods; Pattern recognition. Digital image processing. Computational geometry; Region growing; Reproducibility of Results; Segmentation; Sensitivity and Specificity; Splines; Streaming media; Studies; Surveillance; Three dimensional; Whole Body Imaging - methods; partitioning; Pattern Recognition; Segmentation; Graph-theoretic methods; Region growing; Image Processing and Computer Vision; Image processing; Threshold detection; Top down method; Modeling; Spline approximation; Multidimensional analysis; Voxel; region growing; Pattern analysis; Body; Computer vision; Head; Probabilistic approach; Chain length; graph-theoretic methods; Model driven architecture; Graph theory; Pattern recognition; Object recognition; Laplacian; Bottom up method; Tridimensional image; Graph method; Graphic method; Artificial intelligence

We propose a general approach using Laplacian Eigenmaps and a graphical model of the human body to segment 3D voxel data of humans into different articulated chains. In the bottom-up stage, the voxels are transformed into a high-dimensional (6D or less) Laplacian Eigenspace (LE) of the voxel neighbo...