Perceptual organization and the representation of natural form
Artificial Intelligence
Using perceptual inference networks to manage vision processes
Computer Vision and Image Understanding
Representing 3-D objects in range images using geons
Computer Vision and Image Understanding
Physics-based visual understanding
Computer Vision and Image Understanding - Special issue on physics-based modeling and reasoning in computer vision
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Relevance feedback techniques in image retrieval
Principles of visual information retrieval
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
A Goal Oriented Attention Guidance Model
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Selective visual attention enables learning and recognition of multiple objects in cluttered scenes
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
Automatic description of complex buildings from multiple images
Computer Vision and Image Understanding
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Human vision system can understand images quickly and accurately, but it is impossible to design a generic computer vision system to challenge this task at present. The most important reason is that computer vision community is lack of effective collaborations with visual psychologists, because current object recognition systems use only a small subset of visual cognition theory. We argue that it is possible to put forward a generic solution for image object recognition if the whole vision cognition theory of different schools and different levels can be systematically integrated into an inherent computing framework from the perspective of computer science. In this paper, we construct a generic object recognition solution, which absorbs the pith of main schools of vision cognition theory. Some examples illustrate the feasibility and validity of this solution.