Abstract.
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 prob- lem is formulated as finding paths through the network resembling the model outlines, and a computationally efficient detection technique is presented. An ex- tensive experimental evaluation on detecting five diverse object classes over hun- dreds 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.