Showing papers on "Sketch recognition published in 1987"
30 Apr 1987
TL;DR: Attractive aspects of all of this research are its attention to distortion-invariant, multi-target object recognition and the extensive testing which has been performed of these various architectures on large databases, as well as the design and fabrication of several quite compact optical processing architectures.
Abstract: Optical Pattern Recognition has provided many attractive algorithms and architecture for advanced use in Automatic Target Recognition (ATR) and computer vision. This work is reviewed and highlighted in this paper. Attractive aspects of all of this research are: its attention to distortion-invariant, multi-target object recognition and the extensive testing which has been performed of these various architectures on large databases, as well as the design and fabrication of several quite compact optical processing architectures. Recent Artificial Intelligence (AI) techniques promise to further advance optical processing. These issues are summarized herein.© (1987) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
3 citations
21 Aug 1987
TL;DR: A review of optical pattern recognition algorithms and techniques for various levels of computer vision, reaching the recent upper levels of artificial intelligence, are presented, and briefly summarized.
Abstract: A review of optical pattern recognition algorithms and techniques for various levels of computer vision, reaching the recent upper levels of artificial intelligence, are presented, and briefly summarized.
3 citations
30 Apr 1987
TL;DR: The thesis is that there are no shortcuts in recognition, and a recognition methodology must pay substantial attention to each of the following five steps: conditioning, labeling, grouping, extracting, and matching.
Abstract: Computer recognition and inspection of objects is, in general , a complex procedure requiring a variety of kinds of steps which successively transform the iconic data to recognition information. We hypothesize that the difficulty of today's computer vision and recognition technology to be able to handle unconstrained environments is due to the fact that the existing algorithms are specialized and do not develop one or more of the necessary steps to a high enough degree. Our thesis is that there are no shortcuts. A recognition methodology must pay substantial attention to each of the following five steps: conditioning, labeling, grouping, extracting, and matching.
2 citations
29 Apr 1987
TL;DR: This paper presents a simplified overview of the linguistics and system approach behind speech recognition systems, so that those unfamiliar with this area may obtain a generalized understanding of the setbacks, achievements and operation of the verbal interface.
Abstract: The unnatural form of communication that one must use in interaction with a computer has prompted much research in the area of voice recognition There have been enormous amounts of research in this area in the past decade This paper presents a simplified overview of the linguistics and system approach behind speech recognition systems, so that those unfamiliar with this area may obtain a generalized understanding of the setbacks, achievements and operation of the verbal interface
1 citations