Image Understanding Workshop: Proceedings of a Workshop Held at Los Angeles, California, February 23-25, 1987, 1. sējumsM. Kaufmann, 1987 - 1000 lappuses |
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205. lappuse
... authors and should not be interpreted as representing the official policies , either expressed or implied , of the Defense Mapping Agency , of the Defense Advanced Research Projects Agency , or the US Government . analysis tools for ...
... authors and should not be interpreted as representing the official policies , either expressed or implied , of the Defense Mapping Agency , of the Defense Advanced Research Projects Agency , or the US Government . analysis tools for ...
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... authors and should not be interpreted as representing the official policies , either expressed or implied , of the Defense Advanced Research Projects Agency or of the U.S. Government . This paper describes a method for visual guidance ...
... authors and should not be interpreted as representing the official policies , either expressed or implied , of the Defense Advanced Research Projects Agency or of the U.S. Government . This paper describes a method for visual guidance ...
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Bieži izmantoti vārdi un frāzes
algorithm analysis angle apars applied approach architecture Artificial Intelligence boundaries camera color components Computer Science Computer Science Dept Computer Vision Connection Machine constraints coordinate corresponding curvature curves DARPA DARPA Image Understanding database depth derived described detection developed discontinuities edge edge detection error example extraction Figure function geometric grouping hierarchy Hough transform hypotheses IEEE image processing Image Understanding Workshop implementation instantiation knowledge label linear Lisp Machine Machine matching measure method module motion motion field navigation Navlab object obstacle operations optical flow orientation parallel parameters patch pixels planar plane Poggio problem Proc processor projection PSDB range data range image recognition region representation road Robotics rotation runway scene model schema semantic semantic network shape SIMD slots smooth spatial stereo structure surface surface normal task techniques terrain texture three-dimensional tion tokens transform values vehicle vertex-pair vision system visual voxel
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321. lappuse - The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Defense Advanced Research Projects Agency or the US Government.
379. lappuse - This report describes research done at the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for the laboratory's artificial intelligence research is provided in part by the...
321. lappuse - This research was sponsored by the Defense Advanced Research Projects Agency (DOD), ARPA Order No.
8. lappuse - This research was supported in part by the Defense Advanced Research Projects Agency under...
153. lappuse - Center for Automation Research University of Maryland College Park, MD 20742 ABSTRACT...
71. lappuse - This work was supported in part by the Defense Advanced Research Projects Agency under Contract...
360. lappuse - This research is supported by the Defense Advanced Research Projects Agency under Contract No.
53. lappuse - Marroquin, Probabilistic Solution of Inverse Problems, Ph.D. thesis, Massachusetts Institute of Technology, 1985. 7. S. Geman, D. Geman, "Stochastic relaxation, Gibbs distribution, and Bayesian restoration of images".
390. lappuse - This work was supported in part by the Air Force Systems Command, Rome Air Development Center, Griffiss Air Force Base, New York 13441-5700, and the Air Force Office of Scientific Research, Boiling AFB DC 20332 under Contract No. F30602-85-C-0008, which supports the Northeast Artificial Intelligence Consortium (NAIC).
2. lappuse - A major source of three-dimensional (3-D) information about objects in the world is available to the observer in the form of time-varying imagery. Relative motion between textured objects and observer generates a time-varying optic array at the image, from which image motion of contours, edge fragments and feature points can be extracted. These dynamic features serve to sample the underlying "image flow