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Recognizing large isolated 3-D objects through next view planning using inner camera invariants.

Sumantra Dutta Roy1, Santanu Chaudhury, Subhashis Banerjee

  • 1Department of Electrical Engineering, Indian Institute of Technology Bombay, Powai, Mumbai-400 076, India. sumantra@ee.iitb.ac.in

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|April 15, 2005
PubMed
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This study introduces a novel probabilistic framework for 3-D object recognition and pose estimation using an uncalibrated camera. It enables accurate recognition even with partial views and high ambiguity, improving computer vision systems.

Area of Science:

  • Computer Vision
  • Robotics
  • Artificial Intelligence

Background:

  • Single-view 3-D object recognition is limited by insufficient features and ambiguity.
  • Objects can be indistinguishable from a single viewpoint, requiring sequential views.
  • Incomplete object views in images pose significant recognition challenges.

Purpose of the Study:

  • To present a new online scheme for 3-D object recognition and pose estimation.
  • To address challenges of large, isolated objects not fitting within the camera's field of view.
  • To handle uncalibrated projective cameras with potentially varying internal parameters.

Main Methods:

  • Utilizes a probabilistic reasoning framework for recognition and next-view planning.
  • Employs an online scheme for continuous recognition and pose estimation.

Related Experiment Videos

  • Works with uncalibrated cameras, accommodating variations in internal parameters.

    • Main Results:

    • Demonstrates successful recognition and pose estimation of 3-D objects.
    • Achieves accurate results even with high interpretation ambiguity from the initial view.
    • Effectively handles scenarios where objects are not fully visible in the camera's field of view.

    Conclusions:

    • The proposed probabilistic framework offers a robust solution for 3-D object recognition.
    • The scheme is effective for real-world applications involving incomplete and ambiguous visual data.
    • Enables reliable object recognition and pose estimation under challenging uncalibrated camera conditions.