Motion Stereo And Ego-Motion Complex Logarithmic Mapping (ECLM)

Sandra L. Bartlett; Ramesh Jain

doi:10.1117/12.976586

22 August 1988 Motion Stereo And Ego-Motion Complex Logarithmic Mapping (ECLM)

Sandra L. Bartlett, Ramesh Jain

Proceedings Volume 0938, Digital and Optical Shape Representation and Pattern Recognition; (1988) https://doi.org/10.1117/12.976586
Event: 1988 Technical Symposium on Optics, Electro-Optics, and Sensors, 1988, Orlando, FL, United States

Abstract

Stereo information can be obtained using a single translating camera. The analysis of a sequence of images obtained using such a camera configuration may be facilitated by using a mapping similar to the retino-striate mapping in mammals, which is the Complex Logarithmic Mapping (CLM). The mathmatical properties of this mapping make it ideal for object recognition, depth determination, and dynamic scene segmentation. There is widespread interest in the military, space, and industrial communities in algorithms for analysing images from a moving camera. Applications include autonomous vehicle navigation, space station construction and maintenence, and robot arm control for manufacture and inspection. In all of these cases, at least rough ego-motion information is available. Using this information to chose the origin of the mapping allows us to use important characteristics of optical flow without the need to calculate it. This technique, called Ego-Motion Complex Logarithmic Mapping (ECLM), can be used to calculate depths. Since the mapping is performed on a digitized image, a direct implementation of the CLM results in an image that contains less information than the original image. Computer vision methods for static scene analysis rely mainly on point, line, and region techniques. We explore how some of these algorithms perform in ECLM space and how they can be used for depth determination.

Citation Download Citation

Sandra L. Bartlett and Ramesh Jain "Motion Stereo And Ego-Motion Complex Logarithmic Mapping (ECLM)", Proc. SPIE 0938, Digital and Optical Shape Representation and Pattern Recognition, (22 August 1988); https://doi.org/10.1117/12.976586

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