Ekambaram Rajmadhan

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Mislabeled examples in the training data can severely affect the performance of supervised classifiers. In this paper, we present an approach to remove any mislabeled examples in the dataset by selecting suspicious examples as targets for inspection. We show that the large margin and soft margin principles used in support vector machines (SVM) have the(More)
We propose a scheme to detect individuals in any image frame of a video sequence showing densely crowded scenes against cluttered backgrounds. The method uses only spatial information, and in an initial pass through the image a trained Viola–Jones-type local detector is used to locate individuals in the densely crowded scene. This yields a large number of(More)
In this paper, we propose a method to reduce the false alarm rate or alternatively to improve the detection rate of a local detector for individuals within dense crowds. The detected windows from a Viola-type head detector are processed in a second pass by a cascade of boosted classifiers working with Haar-like features to improve performance. The latter(More)
The U.S. Defense Advanced Research Projects Agency’s (DARPA) Neovision2 program aims to develop artificial vision systems based on the design principles employed by mammalian vision systems. Three such algorithms are briefly described in this paper. These neuromorphic-vision systems’ performance in detecting objects in video was measured using a set of(More)
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