RI: Detecting Boundaries for Segmentation and Recognition

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Shape is a powerful visual cue for recognizing objects in images, segmenting images into regions corresponding to individual objects, and, more generally, understanding the 3D structures of scenes. However, to be able to exploit shape information, we need reliable ways of detecting fragments of object boundaries, a difficult problem in itself. This project investigates possible solutions to these problems. Specifically, it explores ways to reliably detect occluding boundaries by focusing on the question of combining motion cues with appearance cues for better detection of occlusion boundaries. In addition, the project explores different ways in which boundaries can be used in key vision tasks by investigating the integration of boundary information in segmentation and category recognition.The project is expected to generate advances in two areas: 1) detecting boundary information from images as shape cues and 2) using boundary information in segmentation and recognition tasks. In the first area, it extends the current approaches for contour detection to include motion cues. This is motivated by the availability of temporal information in many practical applications. Assuming that we can extract boundary fragments, the issue of using them effectively in segmentation and recognition remains an open question. The project will make substantial contributions toward answering that question, including integrating boundary information with image segmentation and recognition algorithms. The project will result in advances that are directly relevant to a wide range of applications. In particular, the ability to reliably detect boundaries from image sequences is crucial in all applications that involve the analysis of videos for object discovery, recognition, and segmentation. The project is also expected to contribute to the development of more reliable category recognition algorithms based on shape representations, an enabling technology in a wide variety of fields including defense, health care, human-computer interaction, image retrieval and data mining, industrial and personal robotics, manufacturing, scientific image analysis, surveillance and security, and transportation. Although the project focuses primarily on automatic segmentation and recognition, it will also contribute to human-assisted segmentation and image editing, a technology area that is becoming increasingly important with the advent of consumer video and image editing tools. An additional expected product of the project is the generation of a corpus of labeled data, including motion data.URL: http://www.cs.cmu.edu/~hebert/boundaries/