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Recognition without Correspondence using Multidimensional Receptive Field Histograms

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Abstract

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.

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Authors and Affiliations

  1. MIT Media Laboratory, Room 384C, 20 Ames Street, Cambridge, MA, 02139, USA

    Bernt Schiele

  2. GRAVIR, INRIA Rhône–Alpes, 655, Avenue de l'Europe, 38300, Monbonnot, France

    James L. Crowley

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  1. Bernt Schiele
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  2. James L. Crowley
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Schiele, B., Crowley, J.L. Recognition without Correspondence using Multidimensional Receptive Field Histograms. International Journal of Computer Vision 36, 31–50 (2000). https://doi.org/10.1023/A:1008120406972

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