The Cluttered Background Problem for Active Contours: A Minimum-Latency Solution

We present a region-based active contour detection algorithm for objects that exhibit relatively homogeneous photometric characteristics (e.g. smooth color or gray levels), embedded in complex background clutter. Current methods either frame this problem in Bayesian classification terms, where precious modeling resources are expended representing the complex background away from decision boundaries, or use heuristics to limit the search to local regions around the object of interest. We propose an adaptive lookout region, whose size depends on the statistics of the data, that are estimated along with the boundary during the detection process. The result is a “curious snake” that explores the outside of the decision boundary only locally to the extent necessary to achieve a good tradeoff between missed detections and narrowest “lookout” region, drawing inspiration from the literature of minimum-latency set-point change detection and robust statistics. This development makes fu...

• Type: Conference
• Year: 2010
• Where: CVPR
• Authors: Stefano Soatto, Ganesh Sundaramoorthi, Anthony Yezzi
• Keywords: Active Contours, Complex Backgrounds, Computer Vision, CVPR 2010, Detection