Recognition without Correspondence using Multidimensional Receptive Field Histograms

The appearance of an object is composed of local structure. This local structure can be described and characterized by a vector of local features measured by local operators such as Gaussian derivatives or Gabor filters. This article presents a technique where appearances of objects are represented by the joint statistics of such local neighborhood operators. As such, this represents a new class of appearance based techniques for computer vision. Based on joint statistics, the paper develops techniques for the identification of multiple objects at arbitrary positions and orientations in a cluttered scene. Experiments show that these techniques can identify over 100 objects in the presence of major occlusions. Most remarkably, the techniques have low complexity and therefore run in real-time.

• Type: Journal
• Year: 2000
• Where: IJCV
• Authors: Bernt Schiele, James L. Crowley
• Keywords: IJCV 2000, Joint Statistics, Local Neighborhood Operators, Local Structure