NDLI: On the Importance of Being Asymmetric in Stereopsis—Or Why We Should Use Skewed Parallel Cameras

Content Provider	Springer Nature Link
Author	Francisco, Antônio Bergholm, Fredrik
Copyright Year	1998
Abstract	This paper presents a new clever camera sensor, where relative pose determination is not needed, and the sensor is simultaneously capable of using vergence micromovements. Sweeping depth using vergence micromovements promises subpixel depth precision, measuring zero disparity at each time instant. We show that curves preserving zero disparity are exactly conics, nondegenerate or degenerate. Oddly enough, only circles (Vieth-Müller circles) are routinely considered, either theoretically or in practical work, in vergence stereo. Horopters in human vision, cf. Ogle (1932), closely resemble conics.We introduce translational vergence by suggesting the use of a pair of shift-optics CCD cameras. The nonrigidity causes zero disparity curves to become planes, for each fixation. (They are degenerate conics.) We have parallel optical axes, but slanting left and right primary lines of sight. During vergence movements, the primary lines of sight move over time. This has farreaching consequences: Binocular head-eye systems all involve relative camera rotation, to fixate. But, camera rotation is unnecessary. Hence, for relative depth maps, there is no need for measuring camera rotation (relative camera pose) from mechanical sources. Nor are algorithms needed for calculating epipolar lines. The suggested technique removes the need for camera rotations about the optical centers in a binocular head-eye system.
Starting Page	181
Ending Page	202
Page Count	22
File Format	PDF
ISSN	09205691
Journal	International Journal of Computer Vision
Volume Number	29
Issue Number	3
e-ISSN	15731405
Language	English
Publisher	Kluwer Academic Publishers
Publisher Date	1998-01-01
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Artificial Intelligence (incl. Robotics) Computer Imaging, Graphics and Computer Vision Image Processing Automation and Robotics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Vision and Pattern Recognition Software

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