On active contour models and balloons
CVGIP: Image Understanding
A fast algorithm for active contours and curvature estimation
CVGIP: Image Understanding
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
Object Matching Using Deformable Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual Image Retrieval by Elastic Matching of User Sketches
IEEE Transactions on Pattern Analysis and Machine Intelligence
Toward Improved Ranking Metrics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gradient Vector Flow: A New External Force for Snakes
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Challenges of Image and Video Retrieval
CIVR '02 Proceedings of the International Conference on Image and Video Retrieval
Evaluation of key frame-based retrieval techniques for video
Computer Vision and Image Understanding - Special isssue on video retrieval and summarization
Content-based multimedia information retrieval: State of the art and challenges
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Recognition of object categories using affine kernels
Proceedings of the international conference on Multimedia information retrieval
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Many visual matching algorithms can be described in terms of the features and the inter-feature distance or metric. The most commonly used metric is the sum of squared differences (SSD), which is valid from a maximum likelihood perspective when the real noise distribution is Gaussian. However, we have found experimentally that the Gaussian noise distribution assumption is often invalid. This implies that other metrics, which have distributions closer to the real noise distribution, should be used. In this paper we considered a shape matching application. We implemented two algorithms from the research literature and for each algorithm we compared the efficacy of the SSD metric, the SAD (sum of the absolute differences) metric, and the Cauchy metric. Furthermore, in the case where sufficient training data is available, we discussed and experimentally tested a metric based directly on the real noise distribution, which we denoted the maximum likelihood metric.