UC San Diego
Monday, April 9, 3:30pm, NSH 1507
Supervised Learning of Edges and Object Boundaries
Edge detection is one of the most studied problems in computer vision, yet it remains a very challenging task. It is difficult since often the decision for an edge cannot be made purely based on low level cues such as gradient, instead we need to engage all levels of information, low,middle, and high, in order to decide where to put edges. In this paper we propose a novel supervised learning algorithm for edge and object boundary detection which we refer to as Boosted Edge Learning or BEL for short. A decision of an edge point is made independently at each location in the image; a very large aperture is used providing significant context for each decision. In the learning stage, the algorithm selects and combines a large number of features across different scales in order to learn a discriminative model using an extended version of the Probabilistic Boosting Tree classification algorithm. The learning based framework is highly adaptive and there are no parameters to tune. We show applications for edge detection in a number of specific image domains as well as on natural images. We test on various datasets including the Berkeley datasetand the results obtained are very good.
Short Bio:
Piotr is currently pursuing his PhD in Computer Science at UCSD, under the guidance of Prof. Serge Belongie (expected Sept 2007). He obtained his B.A and M.S degrees from Harvard University. His interests lie in machine learning and pattern recognition, and their application to computer vision. His research is supported by the NSF IGERT fellowship.
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