Generic Object Detection using Model Based Segmentation

This paper presents a novel approach for detection and segmentation of generic shapes in cluttered images. The underlying assumption is that generic objects that are man made, frequently have surfaces which closely resemble standard model shapes such as rectangles, semi-circles etc. Due to the perspective transformations of optical imaging systems, a model shape may appear differently in the image with various orientations and aspect ratios. The set of possible appearances can be represented compactly by a few vectorial eigenbases that are derived from a small set of model shapes which are affine transformed in a wide parameter range. Instead of regular boundary of standard models, we apply a vectorial boundary which improves robustness to noise, background clutter and partial occlusion. The detection of generic shapes is realized by detecting local peaks of a similarity measure between the image edge map and an eigenspace combined set of the appearances. At each local maxima, a fast ...

• Type: Conference
• Year: 1999
• Where: CVPR
• Authors: Zhiqian Wang, Jezekiel Ben-Arie
• Keywords: Computer Vision, CVPR 1999, Generic Shapes, Multidimensional Search Corresponds, Noisy Shapes, Standard Model Shapes, Various Interfering Distortions