pattern recognition - sharadavikascom sharadavikas

1、MODULE IUNIT ICONTENTS1.1 Specific Instructional Objectives1.2 Introduction1.3 Machine Perception1.4 An Example:1.5 THE SUB-PROBLEM OF PATTERN CLASSIFICATION1.6 Learning and Adaptation 1.7 Summary 1.8 Keywords1.9 Exercis

2、e1.1 Specific Instructional ObjectivesAt the end of this lesson the student will be able to:? Explain what is Machine perception.? Explain what is an example.? Identify The Sub-Problem of pattern classification system.?

3、Know the costs and risks involved.? Explain the Learning and Adaptation.camera, take some sample images and begin to note some physical differences between the two types of fish -- length, lightness, width, number and sh

4、ape of fins, position of the mouth, and so on -- and these suggest features to explore for use in our classifier. We also notice noise or variations in the images -- variations in lighting, position of the fish on the co

5、nveyor, even “static“ due to the electronics of the camera itself. Given that there truly are differences between the population of sea bass and that MODEL of salmon, we view them as having different models -- different

6、descriptions, which are typically mathematical in form. The overarching goal and approach in pattern classification is to hypothesize the class of these models, process the sensed data to eliminate noise (not due to the

7、models), and for any sensed pattern choose the model that corresponds best. Any techniques that further this aim should be in the conceptual toolbox of the designer of pattern recognition systems. Our prototype system t

8、o perform this very specific task might well have the form shown in Fig.1.1. First the camera captures an image of the fish. Next, the camera's PRE- signals are preprocessed to simplify subsequent operations without

9、loosing relevant PROCESSING information. In particular, we might use a segmentation operation in which the images of different fish are somehow isolated from one another and from the background. The SEGMENTATION informat

10、ion from a single fish is then sent to a feature extractor, whose purpose is to FEATURE reduce the data by measuring certain “features“ or “properties.“ These features EXTRACTION (or, more precisely, the values of these

11、features) are then passed to a classifier that evaluates the evidence presented and makes a final decision as to the species. The preprocessor might automatically adjust for average light level, or threshold the image to