Obstacle detection by recognizing binary expansion patterns
Authors:
Baram, Yoram (Technion-Israel Inst of Technology) 
Barniv, Yair
(NASA/Ames Research Center).
Journal Title: Tansactions on Aerospace and Electronic
Systems
Volume: 32 Issue: 1 Page: pp 191-198.
Published: Jan 01, 1996
- Abstract:
- A technique is described for obstacle detection, based on the
expansion of the image-plane projection of a textured object, as its
distance from the sensor decreases. Information is conveyed by
vectors whose components represent first-order temporal and spatial
derivatives of the image intensity, which are related to the time to
collision through the local divergence. Such vectors may be
characterized as patterns corresponding to 'safe' or 'dangerous'
situations. We show that the essential information is conveyed by
single-bit vector components, representing the signs of the relevant
derivatives. We use two recently developed, high capacity
classifiers, employing neural learning techniques, to recognize the
imminence of collision from such patterns.
- Major Subject Terms:
- BINARY DATA IMAGERY MACHINE LEARNING MEASURING INSTRUMENTS PATTERN RECOGNITION
- Minor Subject Terms:
- COLLISION AVOIDANCE COMPUTER VISION OPTICAL FLOW (IMAGE ANALYSIS)