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Space variant image processing

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Abstract

This paper describes a graph-based approach to image processing, intended for use with images obtained from sensors having space variant sampling grids. The connectivity graph (CG) is presented as a fundamental framework for posing image operations in any kind of space variant sensor. Partially motivated by the observation that human vision is strongly space variant, a number of research groups have been experimenting with space variant sensors. Such systems cover wide solid angles yet maintain high acuity in their central regions. Implementation of space variant systems pose at least two outstanding problems. First, such a system must be active, in order to utilize its high acuity region; second, there are significant image processing problems introduced by the non-uniform pixel size, shape and connectivity. Familiar image processing operations such as connected components, convolution, template matching, and even image translation, take on new and different forms when defined on space variant images. The present paper provides a general method for space variant image processing, based on a connectivity graph which represents the neighbor-relations in an arbitrarily structured sensor. We illustrate this approach with the following applications: (1) Connected components is reduced to its graph theoretic counterpart. We illustrate this on a logmap sensor, which possesses a difficult topology due to the branch cut associated with the complex logarithm function. (2) We show how to write local image operators in the connectivity graph that are independent of the sensor geometry. (3) We relate the connectivity graph to pyramids over irregular tessalations, and implement a local binarization operator in a 2-level pyramid. (4) Finally, we expand the connectivity graph into a structure we call a transformation graph, which represents the effects of geometric transformations in space variant image sensors. Using the transformation graph, we define an efficient algorithm for matching in the logmap images and solve the template matching problem for space variant images. Because of the very small number of pixels typical of logarithmic structured space variant arrays, the connectivity graph approach to image processing is suitable for real-time implementation, and provides a generic solution to a wide range of image processing applications with space variant sensors.

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Authors and Affiliations

  1. Courant Institute of Mathematical Sciences, New York University, New York, NY

    Richard S. Wallace

  2. AT&T Bell Laboratories, Middletown, NJ

    Ping-Wen Ong

  3. Bell Communications Research, Morristown, NJ

    Benjamin B. Bederson

  4. Department of Cognitive and Neural Systems, Boston University, Boston, MA

    Eric L. Schwartz

Authors
  1. Richard S. Wallace
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  2. Ping-Wen Ong
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  3. Benjamin B. Bederson
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  4. Eric L. Schwartz
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Additional information

This research was supported by DARPA/ONR #N00014-90-C-0049 and AFOSR Life Sciences #88-0275. Please address all correspondence to Richard S. Wallace, NYU Robotics Research Laboratory, 715 Broadway 12th Floor, New York, NY 10003. This report is copyright ©1993 by the authors. This report is a revised draft of a report published as New York University Courant Institute of Mathematical Sciences Computer Science Technical Report (No. 589 and Robotics Report No. 256), October, 1991. Last revised October.

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Wallace, R.S., Ong, PW., Bederson, B.B. et al. Space variant image processing. Int J Comput Vision 13, 71–90 (1994). https://doi.org/10.1007/BF01420796

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  • DOI: https://doi.org/10.1007/BF01420796

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