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Fast point-of-interest detection from real-time stereo

Published: 26 November 2012 Publication History

Abstract

The use of Points-of-Interest can significantly reduce computational complexity and memory required for high-level vision tasks. We describe a simple and effective technique to detect Points-of-Interest from noisy disparity maps generated by a real-time stereo system, which considers all sources of information, i.e. the disparity map and the left and right images of a stereo pair. The first step in our approach assigns states to each point and marks possible border points. Then we detect left and right image edges. Matching these three sources of information leads to the Points-of-Interest, which we call Triple Edge points. The final step identifies significant points by projecting the detected points to real-world 3D space. Generation of the Triple Edge points is computationally more efficient than other techniques of Point-of-Interest detection. Some simple experimental results show that the Triple Edge points are a reliable source of information for identifying multiple objects in low-texture scenes with noisy disparity maps. We argue that they can be used in other high-level tasks such as object extraction, pose estimation, recognition and 3D reconstruction.

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IVCNZ '12: Proceedings of the 27th Conference on Image and Vision Computing New Zealand
November 2012
547 pages
ISBN:9781450314732
DOI:10.1145/2425836
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

  • HRS: Hoare Research Software Ltd.
  • Google Inc.
  • Dept. of Information Science, Univ.of Otago: Department of Information Science, University of Otago, Dunedin, New Zealand

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 November 2012

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Author Tags

  1. depth features
  2. keypoint detection
  3. point-of-interest
  4. real-time stereo

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  • Research-article

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IVCNZ '12
Sponsor:
  • HRS
  • Dept. of Information Science, Univ.of Otago
IVCNZ '12: Image and Vision Computing New Zealand
November 26 - 28, 2012
Dunedin, New Zealand

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Overall Acceptance Rate 55 of 74 submissions, 74%

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