Curious snakes: an active contour formulation of information-driven minimum-latency boundary detection
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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 fully automatic detection in complex backgrounds a realistic possibility for active contours, allowing us to exploit their powerful geometric modeling capabilities compared with other approaches used for segmentation of cluttered scenes. To this end, we introduce an automatic initialization method tailored to our model that overcomes one of the primary obstacles in using active contours for fully automatic object detection.