The Computation of Visible-Surface Representations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Matching Two Perspective Views
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Images to Surfaces: A Computational Study of the Human Early Visual System
From Images to Surfaces: A Computational Study of the Human Early Visual System
Computer and Robot Vision
Obstacle avoidance and navigation in the real world by a seeing robot rover
Obstacle avoidance and navigation in the real world by a seeing robot rover
IEEE Transactions on Pattern Analysis and Machine Intelligence
Toward Improved Ranking Metrics
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new image rectification algorithm
Pattern Recognition Letters
Object Recognition for Video Retrieval
CIVR '02 Proceedings of the International Conference on Image and Video Retrieval
Stereovision matching through support vector machines
Pattern Recognition Letters
Computer Vision and Image Understanding - Special isssue on video retrieval and summarization
Image domain formalization for content-based image retrieval
Proceedings of the 2005 ACM symposium on Applied computing
Fuzzy Cognitive Maps for stereovision matching
Pattern Recognition
Interactive feedback for video tracking using a hybrid maximum likelihood similarity measure
HCI'07 Proceedings of the 2007 IEEE international conference on Human-computer interaction
Visual information retrieval: future directions and grand challenges
VISUAL'07 Proceedings of the 9th international conference on Advances in visual information systems
Fuzzy multi-criteria decision making in stereovision matching for fish-eye lenses in forest analysis
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Expert Systems with Applications: An International Journal
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We present a novel stereo matching algorithm which integrates learning, feature selection, and surface reconstruction. First, a new instance based learning (IBL) algorithm is used to generate an approximation to the optimal feature set for matching. In addition, the importance of two separate kinds of knowledge, image dependent knowledge and image independent knowledge, is discussed. Second, we develop an adaptive method for refining the feature set. This adaptive method analyzes the feature error to locate areas of the image that would lead to false matches. Then these areas are used to guide the search through feature space towards maximizing the class separation distance between the correct match and the false matches. Third, we introduce a self-diagnostic method for determining when apriori knowledge is necessary for finding the correct match. If the a priori knowledge is necessary then we use a surface reconstruction model to discriminate between match possibilities. Our algorithm is comprehensively tested against fixed feature set algorithms and against a traditional pyramid algorithm. Finally, we present and discuss extensive empirical results of our algorithm based on a large set of real images.