Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of SPIE - The International Society for Optical Engineering |
| Publisher | Society of Photo-Optical Instrumentation Engineers |
| Pages | 163-171 |
| Number of pages | 9 |
| Volume | 2353 |
| ISBN (Print) | 0819416886 |
| Publication status | Published - 1994 Dec 1 |
| Event | Intelligent Robots and Computer Vision XIII: Algorithms and Computer Vision - Boston, MA, USA Duration: 1994 Oct 31 → 1994 Nov 2 |
Other
| Other | Intelligent Robots and Computer Vision XIII: Algorithms and Computer Vision |
|---|---|
| City | Boston, MA, USA |
| Period | 94/10/31 → 94/11/2 |
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All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering
- Condensed Matter Physics
Cite this
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Consistent object representation method for computer vision applications. / Sohn, Kwanghoon; Kim, Jung H.; Alexander, Winser E.
Proceedings of SPIE - The International Society for Optical Engineering. Vol. 2353 Society of Photo-Optical Instrumentation Engineers, 1994. p. 163-171.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Consistent object representation method for computer vision applications
AU - Sohn, Kwanghoon
AU - Kim, Jung H.
AU - Alexander, Winser E.
PY - 1994/12/1
Y1 - 1994/12/1
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
SN - 0819416886
VL - 2353
SP - 163
EP - 171
BT - Proceedings of SPIE - The International Society for Optical Engineering
PB - Society of Photo-Optical Instrumentation Engineers
ER -