Sampling of Images for Efficient Model-Based Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detection and Separation of Ring-Shaped Clusters Using Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modal Matching for Correspondence and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
End-point detection of the aerobic phase in a biological reactor using SOM and clustering algorithms
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence
Combining multiple clusterings using similarity graph
Pattern Recognition
The farthest point strategy for progressive image sampling
IEEE Transactions on Image Processing
An efficient and scalable family of algorithms for combining clusterings
Engineering Applications of Artificial Intelligence
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Arbitrary shape object detection, which is mostly related to computer vision and image processing, deals with detecting objects from an image. In this paper, we consider the problem of detecting arbitrary shape objects as a clustering application by decomposing images into representative data points, and then performing clustering on these points. Our method for arbitrary shape object detection is based on COMUSA which is an efficient algorithm for combining multiple clusterings. Extensive experimental evaluations on real and synthetically generated data sets demonstrate that our method is very accurate and efficient.