Range Estimation From Intensity Gradient Analysis
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High-Speed Range Estimation Based on Intensity Gradient Analysis
Author: Kurt D. Skifstad
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-12-06
A fast and reasonably accurate perception of the environment is essential for successful navigation of an autonomous agent. Although many modes of sensing are applicable to this task and have been used, vision remains the most appealing due to its passive nature, good range, and resolution. Most vision techniques to recover depth for navigation use stereo. In the last few years, researchers have started studying techniques to combine stereo with the motion of the camera. Skifstad's dissertation proposes a new approach to recover depth information using known camera motion. This approach results in a robust technique for fast estimation of distances to objects in an image using only one translating camera. A very interesting aspect of the approach pursued by Skifstad is the method used to bypass the most difficult and computationally expensive step in using stereo or similar approaches for the vision-based depth esti mation. The correspondence problem has been the focus of research in most stereo approaches. Skifstad trades the correspondence problem for the known translational motion by using the fact that it is easier to detect single pixel disparities in a sequence of images rather than arbitrary disparities after two frames. A very attractive feature of this approach is that the computations required to detect single pixel disparities are local and hence can be easily parallelized. Another useful feature of the approach, particularly in naviga tion applications, is that the closer objects are detected earlier.
Range Estimation from Intensity Gradient Analysis
Author: University of Michigan. Dept. of Electrical Engineering and Computer Science. Computer Science and Engineering Division
language: en
Publisher:
Release Date: 1988
The Intensity Gradient Analysis (IGA) technique is a depth recovery algorithm that exploits the properties of the MCSO (moving camera, stationary objects) scenario. Depth values are obtained by analyzing temporal intensity gradients arising from the optic flow field induced by known camera motion. In doing so, IGA avoids the feature extraction and correspondence steps of conventional approaches and is therefore very fast. A detailed description of the algorithm is provided along with experimental results from complex laboratory scenes."
Progress In Image Analysis And Processing Ii - Proceedings Of The 6th International Conference
This book presents reports by well-known experts on the most recent research results in image coding, analysis and understanding, and promising applications for solving real problems in manufacturing, remote sensing and biomedicine. The topics covered include shape analysis and computer vision, pattern recognition methods and applications, parallel computer architectures for image processing and analysis, human perception and use of artificial intelligence techniques for image understanding, languages for image abstraction, processing and retrieval, vision modules and neural computation.