The chains model for detecting parts by their context

Detecting an object part relies on two sources of information - the appearance of the part itself, and the context supplied by surrounding parts. In this paper we consider problems in which a target part cannot be recognized reliably using its own appearance, such as detecting lowresolution hands, and must be recognized using the context of surrounding parts. We develop the ‘chains model’ which can locate parts of interest in a robust and precise manner, even when the surrounding context is highly variable and deformable. In the proposed model, the relation between context features and the target part is modeled in a non-parametric manner using an ensemble of feature chains leading from parts in the context to the detection target. The method uses the configuration of the features in the image directly rather than through fitting an articulated 3-D model of the object. In addition, the chains are composable, meaning that new chains observed in the test image can be composed of sub...

• Type: Conference
• Year: 2010
• Where: CVPR
• Authors: Leonid Karlinsky, Michael Dinerstein, Daniel Harari, Shimon Ullman
• Keywords: Articulated 3-D Model, Chains, Computer Vision, CVPR 2010, Object Parts Detection