TY - GEN
T1 - Consistent object representation method for computer vision applications
AU - Sohn, Kwanghoon
AU - Kim, Jung H.
AU - Alexander, Winser E.
PY - 1994
Y1 - 1994
N2 - The human visual system uses two-dimensional (2D) boundary information to recognize objects since the shape of the boundary usually contains the pertinent information about an object. Thus, representing a boundary concisely and consistently is necessary for object recognition. In this paper, we propose a consistent object representation method using mean field annealing (MFA) technique for computer vision applications. Since a curvature function computed on a preprocessed smooth boundary, which is obtained by the MFA approach is consistent, we can consistently detect corner points in this curvature function space. Furthermore, the MFA approach preserves the sharpness of corner points very well. Thus, we can detect corner points easier and better with this method than with other existing methods. Ideal corner points rarely exist for a real boundary. They are often rounded due to the smoothing effect of the preprocessing. In addition, a human recognizes both sharp corner points and slightly rounded segments as corner points. Thus, we use `corner sharpness,' which is qualitatively similar to a human's capability of detecting corner points, to increase the robustness of the proposed algorithm.
AB - The human visual system uses two-dimensional (2D) boundary information to recognize objects since the shape of the boundary usually contains the pertinent information about an object. Thus, representing a boundary concisely and consistently is necessary for object recognition. In this paper, we propose a consistent object representation method using mean field annealing (MFA) technique for computer vision applications. Since a curvature function computed on a preprocessed smooth boundary, which is obtained by the MFA approach is consistent, we can consistently detect corner points in this curvature function space. Furthermore, the MFA approach preserves the sharpness of corner points very well. Thus, we can detect corner points easier and better with this method than with other existing methods. Ideal corner points rarely exist for a real boundary. They are often rounded due to the smoothing effect of the preprocessing. In addition, a human recognizes both sharp corner points and slightly rounded segments as corner points. Thus, we use `corner sharpness,' which is qualitatively similar to a human's capability of detecting corner points, to increase the robustness of the proposed algorithm.
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M3 - Conference contribution
AN - SCOPUS:0028748651
SN - 0819416886
T3 - Proceedings of SPIE - The International Society for Optical Engineering
SP - 163
EP - 171
BT - Proceedings of SPIE - The International Society for Optical Engineering
PB - Society of Photo-Optical Instrumentation Engineers
T2 - Intelligent Robots and Computer Vision XIII: Algorithms and Computer Vision
Y2 - 31 October 1994 through 2 November 1994
ER -