IEICE TRANSACTIONS on Information
A Neurocomputational Approach to the Correspondence Problem in Computer Vision
Summary :
A problem which often arises in computer vision is that of matching corresponding points of images. In the case of object recognition, for example, the computer compares new images to templates from a library of known objects. A common way to perform this comparison is to extract feature points from the images and compare these points with the template points. Another common example is the case of motion detection, where feature points of a video image are compared to those of the previous frame. Note that in both of these example, the point correspondence is complicated by the fact that the point sets are not only randomly ordered but have also been distorted by an unknown transformation and having quite different coordinates. In the case of object recognition, there exists a transformation from the object being viewed, to its projection onto the camera's imaging plane, while in the motion detection case, this transformation represents the motion (translation and rotation) of the ofject. If the parameters of the transformation are completely unknow, then all n! permutations must be compared (n : number of feature points). For each permutation, the ensuing transformation is computed using the least-squared projection method. The exponentially large computation required for this is prohibitive. A neural computational method is propopsed to solve these combinatorial problems. This method obtains the best correspondence matching and also finds the associated transform parameters. The method was applied to two dimensional point correspondence and three-to-two dimensional correspondence. Finally, this connectionist approach extends readily to a Boltzmann machine implementation. This implementation is desirable when the transformation is unknown, as it is less sensitive to local minima regardless of initial conditions.
- Publication
- IEICE TRANSACTIONS on Information Vol.E77-D No.4 pp.507-515
- Publication Date
- 1994/04/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- Special Section PAPER (Special Issue on Neurocomputing)
- Category
- Image Processing
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Hiroshi SAKO, Hadar Itzhak AVI-ITZHAK, "A Neurocomputational Approach to the Correspondence Problem in Computer Vision" in IEICE TRANSACTIONS on Information,
vol. E77-D, no. 4, pp. 507-515, April 1994, doi: .
Abstract: A problem which often arises in computer vision is that of matching corresponding points of images. In the case of object recognition, for example, the computer compares new images to templates from a library of known objects. A common way to perform this comparison is to extract feature points from the images and compare these points with the template points. Another common example is the case of motion detection, where feature points of a video image are compared to those of the previous frame. Note that in both of these example, the point correspondence is complicated by the fact that the point sets are not only randomly ordered but have also been distorted by an unknown transformation and having quite different coordinates. In the case of object recognition, there exists a transformation from the object being viewed, to its projection onto the camera's imaging plane, while in the motion detection case, this transformation represents the motion (translation and rotation) of the ofject. If the parameters of the transformation are completely unknow, then all n! permutations must be compared (n : number of feature points). For each permutation, the ensuing transformation is computed using the least-squared projection method. The exponentially large computation required for this is prohibitive. A neural computational method is propopsed to solve these combinatorial problems. This method obtains the best correspondence matching and also finds the associated transform parameters. The method was applied to two dimensional point correspondence and three-to-two dimensional correspondence. Finally, this connectionist approach extends readily to a Boltzmann machine implementation. This implementation is desirable when the transformation is unknown, as it is less sensitive to local minima regardless of initial conditions.
URL: https://global.ieice.org/en_transactions/information/10.1587/e77-d_4_507/_p
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@ARTICLE{e77-d_4_507,
author={Hiroshi SAKO, Hadar Itzhak AVI-ITZHAK, },
journal={IEICE TRANSACTIONS on Information},
title={A Neurocomputational Approach to the Correspondence Problem in Computer Vision},
year={1994},
volume={E77-D},
number={4},
pages={507-515},
abstract={A problem which often arises in computer vision is that of matching corresponding points of images. In the case of object recognition, for example, the computer compares new images to templates from a library of known objects. A common way to perform this comparison is to extract feature points from the images and compare these points with the template points. Another common example is the case of motion detection, where feature points of a video image are compared to those of the previous frame. Note that in both of these example, the point correspondence is complicated by the fact that the point sets are not only randomly ordered but have also been distorted by an unknown transformation and having quite different coordinates. In the case of object recognition, there exists a transformation from the object being viewed, to its projection onto the camera's imaging plane, while in the motion detection case, this transformation represents the motion (translation and rotation) of the ofject. If the parameters of the transformation are completely unknow, then all n! permutations must be compared (n : number of feature points). For each permutation, the ensuing transformation is computed using the least-squared projection method. The exponentially large computation required for this is prohibitive. A neural computational method is propopsed to solve these combinatorial problems. This method obtains the best correspondence matching and also finds the associated transform parameters. The method was applied to two dimensional point correspondence and three-to-two dimensional correspondence. Finally, this connectionist approach extends readily to a Boltzmann machine implementation. This implementation is desirable when the transformation is unknown, as it is less sensitive to local minima regardless of initial conditions.},
keywords={},
doi={},
ISSN={},
month={April},}
Copy
TY - JOUR
TI - A Neurocomputational Approach to the Correspondence Problem in Computer Vision
T2 - IEICE TRANSACTIONS on Information
SP - 507
EP - 515
AU - Hiroshi SAKO
AU - Hadar Itzhak AVI-ITZHAK
PY - 1994
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E77-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 1994
AB - A problem which often arises in computer vision is that of matching corresponding points of images. In the case of object recognition, for example, the computer compares new images to templates from a library of known objects. A common way to perform this comparison is to extract feature points from the images and compare these points with the template points. Another common example is the case of motion detection, where feature points of a video image are compared to those of the previous frame. Note that in both of these example, the point correspondence is complicated by the fact that the point sets are not only randomly ordered but have also been distorted by an unknown transformation and having quite different coordinates. In the case of object recognition, there exists a transformation from the object being viewed, to its projection onto the camera's imaging plane, while in the motion detection case, this transformation represents the motion (translation and rotation) of the ofject. If the parameters of the transformation are completely unknow, then all n! permutations must be compared (n : number of feature points). For each permutation, the ensuing transformation is computed using the least-squared projection method. The exponentially large computation required for this is prohibitive. A neural computational method is propopsed to solve these combinatorial problems. This method obtains the best correspondence matching and also finds the associated transform parameters. The method was applied to two dimensional point correspondence and three-to-two dimensional correspondence. Finally, this connectionist approach extends readily to a Boltzmann machine implementation. This implementation is desirable when the transformation is unknown, as it is less sensitive to local minima regardless of initial conditions.
ER -
English
