Show simple item record

dc.contributor.authorSoatto, Stefanoen_US
dc.contributor.authorSundaramoorthi, Ganeshen_US
dc.contributor.authorYezzi, Anthonyen_US
dc.date.accessioned2013-09-10T14:37:01Z
dc.date.available2013-09-10T14:37:01Z
dc.date.issued2010-06
dc.identifier.citationS. Soatto, G. Sundaramoorthi, and A. Yezzi, “Curious snakes: an active contour formulation of information-driven minimum-latency boundary detection,” 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2855-2862 (13-18 June 2010)en_US
dc.identifier.isbn978-1-4244-6984-0
dc.identifier.issn1063-6919
dc.identifier.urihttp://hdl.handle.net/1853/48924
dc.description©2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or distribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.en_US
dc.descriptionDOI: 10.1109/CVPR.2010.5540020en_US
dc.description.abstractWe 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.en_US
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectBayes methodsen_US
dc.subjectComputational geometryen_US
dc.subjectActive contoursen_US
dc.subjectEdge detectionen_US
dc.subjectObject detectionen_US
dc.subjectPhotometryen_US
dc.titleCurious snakes: an active contour formulation of information-driven minimum-latency boundary detectionen_US
dc.typeProceedingsen_US
dc.typePost-printen_US
dc.contributor.corporatenameUniversity of California, Los Angeles. Computer Science Dept.en_US
dc.contributor.corporatenameGeorgia Institute of Technology. School of Electrical and Computer Engineeringen_US
dc.publisher.originalInstitute of Electrical and Electronics Engineersen_US
dc.identifier.doi10.1109/CVPR.2010.5540020


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record