Pattern recognition: 4th international conference, Cambridge, U.K., March 28-30, 1988 : proceedings
Springer, 1988 - Computers - 668 pages
Pattern recognition is traditionally considered to cover all aspects of sensory data perception ranging from data acquisition, through preprocessing and low level analysis, to high level interpretation. Owing to its breadth and important application potential, the field of pattern recognition has been attracting considerable attention of researchers in academia and industry and consequently it has been witnessing a rapid growth and perpetual development. The need for dissemination of the latest results is being served by a host of international conferences on pattern recognition. One such series of meetings is regularly held in the United Kingdom under the auspices of the British Pattern Recognition Association. This volume contains papers presented at the BPRA 4th International Conference on Pattern Recognition held in Cambridge, March 28-30, 1988. Alongside the conventional topics of statistical and syntactic pattern recognition, contributions address issues in the hot subject areas of adaptive learning networks, computer vision, knowledge base methods and architectures for pattern processing, and among others, report progress in the application domains of document processing, speech and text recognition and shape analysis for industrial robotics. It is believed that the collection is not merely a report on current activities but that it will also be an important source of inspiration for future developments in the field of pattern recognition.
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algorithm analysis application approach array attributes binary classification color complex Computer Vision considered corner detector corresponding curvature curve defined denote described detection detector discrimination distribution edge error estimate example feature extraction function fuzzy Gaussian Gaussian curvature graph grey level Hamming distance histogram Hough transform hypergraph IEEE IEEE Trans image processing image sequence implementation input invariant iteration label linear mapping matching matrix mean curvature measure membership function method node noise object obtained operation optical flow orientation output paper parallel parameters path line Pattern Recognition peak performance pixels problem Proc procedure processing element processor prototype pattern Radon transform range image reconstructed regions representation represented rule sample scanning scene shown in Figure SIMD space spatial statistical string structure surface symbol target technique texture threshold training set tree tuple vector vertical window