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Recent studies have shown that machine learning can improve the accuracy of detecting object boundaries in images. In the standard approach, a boundary detector is trained by minimizing its pixel-level disagreement with human boundary tracings. This naive metric is problematic because it is overly sensitive to boundary locations. This problem is solved by metrics provided with the Berkeley Segmentation...
Offline fingerprints find immense application in the fields of user authentication and criminal identification. But if an insider or a criminal gets unauthorized access to the printed database of fingerprints at a criminology department, then he might tamper or tear them. This may lead to loss of evidence, which could have been useful at the time of post-detection. We feel that no work has been carried...
Classifying an event captured in an image is useful for understanding the contents of the image. The captured event provides context to refine models for the presence and appearance of various entities, such as people and objects, in the captured scene. Such contextual processing facilitates the generation of better abstractions and annotations for the image. Consider a typical set of consumer images...
Automated clinical image data collection tools and apparatus are becoming increasingly important to the medical industry, and imaging databases are growing at an unprecedented rate. Consequently, grid-based telemedicine efforts require the autonomous classification of patient images from distributed sources for fast and accurate image storage, management, and retrieval. In this paper, we present a...
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