PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Object Detection by Contour Segment Networks
Vittorio Ferrari, Tinne Tuytelaars and Luc Van Gool
In: ECCV 2006, Graz, Austria(2006).


We propose a method for object detection in cluttered real images, given a single hand-drawn example as model. The image edges are partitioned into contour segments and organized in an image representation which encodes their interconnections: the Contour Segment Network. The object detection problem is formulated as finding paths through the network resembling the model outlines, and a computationally efficient detection technique is presented. An extensive experimental evaluation on detecting five diverse object classes over hundreds of images demonstrates that our method works in very cluttered images, allows for scale changes and considerable intra-class shape variation, is robust to interrupted contours, and is computationally efficient.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:2301
Deposited By:Vittorio Ferrari
Deposited On:10 November 2006