Receptive field cooccurrence histograms for object detection

Object recognition is one of the major research topics in the field of computer vision. In robotics, there is often a need for a system that can locate certain objects in the environment - the capability which we denote as 'object detection'. In this paper, we present a new method for object detection. The method is especially suitable for detecting objects in natural scenes, as it is able to cope with problems such as complex background, varying illumination and object occlusion. The proposed method uses the receptive field representation where each pixel in the image is represented by a combination of its color and response to different filters. Thus, the cooccurrence of certain filter responses within a specific radius in the image serves as information basis for building the representation of the object. The specific goal in this paper is the development of an online learning scheme that is effective after just one training example but still has the ability to improve its performance with more time and new examples. We describe the details behind the algorithm and demonstrate its strength with an extensive experimental evaluation.