patternrecognition(參考版)
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【正文】 Sons, Inc., 1996 45 References ? [8]K. Fukunaga, “Introduction to Statistical Pattern Recognition”, Second Edition, Academic Press, Inc.,1990 ? [9] E. Gose, R. Johnsonbaugh, and Steve Jost, Pattern recognition and Image Analysis, Prentice Hall Inc., New Jersey, 1996 ? [10] Robert J. Schalkoff, Pattern Recognition: Statical, Structural and Neural Approaches, Chap5, John Wiley amp。1 Pattern Recognition ? Speaker: WenFu Wang ? Advisor: JianJiun Ding ? Email: ? Graduate Institute of Communication Engineering ? National Taiwan University, Taipei, Taiwan, ROC 2 Outline ? Introduction ? Minimum Distance Classifier ? Matching by Correlation ? Optimum statistical classifiers ? Matching Shape Numbers ? String Matching 3 Outline ? Syntactic Recognition of Strings String Grammars ? Syntactic recognition of Tree Grammars ? Conclusions 4 Introduction ? Basic pattern recognition flowchart Sensor Feature generation Feature selection Classifier design System evaluation 5 Introduction ? The approaches to pattern recognition developed are divided into two principal areas: decisiontheoretic and structural ? The first category deals with patterns described using quantitative descriptors, such as length, area, and texture ? The second category deals with patterns best described by qualitative descriptors, such as the relational descriptors. 6 Minimum Distance Classifier ? Suppose that we define the prototype of each pattern class to be the mean vector of the patterns of that class: ? Using the Euclidean distance to determine closeness reduces the problem to puting the distance measures 1jjjxwjmxN?? ? j=1,2,… ,W (1) ()jjD x x m?? j=1,2,… ,W (2) 7 Minimum Distance Classifier ? The smallest distance is equivalent to evaluating the functions ? The decision boundary between classes and for a minimum distance classifier is j=1,2,… ,W (3) j=1,2,… ,W (4) 1()2TTj j j jd x x m m m??( ) ( ) ( )i j i jd x d x d x??1( ) ( ) ( ) 02TTi j i j i jx m m m m m m? ? ? ? ? ?8 Minimum Distance Classifier ? Decision boundary of minimum distance classifier 0 1 2 012x1x2DClass C1Class C29 Minimum Distance Classifier ? Advantages: 1. Unusual directviewing 2. Can solve rotation the question 3. Intensity 4. Chooses the suitable characteristic, then solves mirror problem 5. We may choose the color are one kind of characteristic, the color question then solve. 10 Minimum Distance Classifier ? Disadvantages:
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