Toward a Symbolic Representation of Intensity Changes in Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Smoothness of a Vector Field-Application to Optical Flow
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Flow Segmentation and Estimation by Constraint Line Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Direct Analytical Methods for Solving Poisson Equations in Computer Vision Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Boundary Finding with Parametrically Deformable Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical background modeling for non-stationary camera
Pattern Recognition Letters
Parallel Trellis Based Stereo Matching Using Constraints
BMVC '00 Proceedings of the First IEEE International Workshop on Biologically Motivated Computer Vision
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 2
An optimal scale for edge detection
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 2
A Solution of the Dichromatic Model for Multispectral Photometric Invariance
International Journal of Computer Vision
Multi objective optimization based fast motion detector
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part I
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
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While in classical optics the problem is to determine the images of physical objects, vision is confronted with the inverse problem of recovering three-dimensional shape from the light distribution in the image. Most processes of early vision such as stereomatching, computation of motion and all the ``structure from" processes can be regarded as solutions to inverse problems. This common characteristic of early vision can be formalized---{\it most early vision problems are ``ill-posed problems" in the sense of Hadamard}. We will show that a mathematical theory developed for regularizing ill-posed problems leads in a natural way to the solution of early vision problems in terms of variational principles of a certain class. This is a new theoretical framework for some of the variational solutions already obtained in the analysis of early vision processes. It also shows how several other problems in early vision can be approached and solved.