Determining three-dimensional shape from orientation and spatial frequency disparities

  • David G. Jones
  • Jitendra Malik
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 588)


Binocular differences in orientation and foreshortening are systematically related to surface slant and tilt and could potentially be exploited by biological and machine vision systems. Indeed, human stereopsis may possess a mechanism that specifically makes use of these orientation and spatial frequency disparities, in addition to the usual cue of horizontal disparity. In machine vision algorithms, orientation and spatial frequency disparities are a source of error in finding stereo correspondence because one seeks to find features or areas which are similar in the two views when, in fact, they are systematically different. In other words, it is common to treat as noise what is useful signal.

We have been developing a new stereo algorithm based on the outputs of linear spatial filters at a range of orientations and scales. We present a method in this framework, making use of orientation and spatial frequency disparities, to directly recover local surface slant. An implementation of this method has been tested on curved surfaces and quantitative experiments show that accurate surface orientation can be recovered efficiently. This method does not require the explicit identification of oriented line elements and also provides an explanation of the intriguing perception of surface slant in the presence of orientation or spatial frequency disparities, but in the absence of systematic positional correspondence.


Spatial Frequency Image Patch Surface Orientation Stereo Pair Filter Response 
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 1992

Authors and Affiliations

  • David G. Jones
    • 1
    • 2
  • Jitendra Malik
    • 1
    • 2
  1. 1.Dept. of Electrical EngineeringMcGill UniversityMontréalCanada
  2. 2.Computer Science DivisionUniversity of CaliforniaBerkeleyUSA

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