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Pose Invariant Generic Object Recognition with Orthogonal Axis Manifolds in Linear Subspace

  • Manisha Kalra
  • P. Deepti
  • R. Abhilash
  • Sukhendu Das
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4338)

Abstract

This paper addresses the problem of pose invariant Generic Object Recognition by modeling the perceptual capability of human beings. We propose a novel framework using a combination of appearance and shape cues to recognize the object class and viewpoint (axis of rotation) as well as determine its pose (angle of view). The appearance model of the object from multiple viewpoints is captured using Linear Subspace Analysis techniques and is used to reduce the search space to a few rank-ordered candidates. We have used a decision-fusion based combination of 2D PCA and ICA to integrate the complementary information of classifiers and improve recognition accuracy. For matching based on shape features, we propose the use of distance transform based correlation. A decision fusion using ‘Sum Rule’ of 2D PCA and ICA subspace classifiers, and distance transform based correlation is then used to verify the correct object class and determine its viewpoint and pose. Experiments were conducted on COIL-100 and IGOIL (IITM Generic Object Image Library) databases which contain objects with complex appearance and shape characteristics. IGOIL database was captured to analyze the appearance manifolds along two orthogonal axes of rotation.

Keywords

Object Recognition Recognition Rate Recognition Accuracy Appearance Model Orthogonal Axis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Manisha Kalra
    • 1
  • P. Deepti
    • 1
  • R. Abhilash
    • 1
  • Sukhendu Das
    • 1
  1. 1.Visualization and Perception Laboratory, Department of Computer Science and EngineeringIndian Institute of Technology – MadrasChennaiIndia

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