barcode generator vb.net download OBJECT RECOGNITION in Software

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CHAPTER 15 OBJECT RECOGNITION
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Figure 1513: A posteriori probabilities for two different values of a priori probabilities for objects
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(153) where
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The unknown object should be assigned to the class with the highest a posteriori probability P(Wjlx) As can be seen from the above equations, and as shown in Figure 1513, a posteriori probability depends on prior knowledge about the objects If a priori probability of the object changes, so will the result We discussed the Bayesian approach above for one feature It can be easily extended to multiple features by considering conditional density functions for multiple features
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Off-Line Computations
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The above classification approaches consider the feature space, and then, based on the knowledge of the feature characteristics of objects, a method is used to partition the feature space so that a class decision is assigned to each point in the feature space To assign a class to each point in the feature space, all computations are done before the recognition of unknown objects begins
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155 RECOGNITION STRATEGIES
This is called off-line computation These off-line computations reduce the computations at the run time The recognition process can be effectively converted to a look-up table and hence can be implemented very quickly
Neural Nets
Neural nets have been proposed for object recognition tasks Neural nets implement a classification approach Their attraction lies in their ability to partition the feature space using nonlinear boundaries for classes These boundaries are obtained by using training of the net During the training phase, many instances of objects to be recognized are shown If the training set is carefully selected to represent all objects encountered later during the recognition phase, then the net may learn the classification boundaries in its feature space During the recognition phase, the net works like any other classifier The most attractive feature of neural nets is their ability to use nonlinear classification boundaries and learning abilities The most serious limitations have been the inability to introduce known facts about the application domain and difficulty in debugging their performance
Matching
Classification approaches use effective features and knowledge of the application In many applications, a priori knowledge about the feature probabilitities and the class probabilities is not available or not enough data is available to design a classifier In such cases one may use direct matching of the model to the unknown object and select the best-matching model to classify the object These approaches consider each model in sequence and fit the model to image data to determine the similarity of the model to the image component This is usually done after the segmentation has been done In the following we discuss basic matching approaches
Feature Matching
Suppose that each object class is represented by its features As above, let us assume that the jth feature's value for the ith class is denoted by iij For an unknown object the features are denoted by Uj The similarity of the object
CHAPTER 15 OBJECT RECOGNITION
with the ith class is given by
Si =
LWjSj j=l
(155)
where Wj is the weight for the jth feature The weight is selected based on the relative importance of the feature The similarity value of the jth feature is Sj This could be the absolute difference, normalized difference, or any other distance measure The most common method is to use (156) and to account for normalization in the weight used with the feature The object is labeled as belonging to class k if Sk is the highest similarity value Note that in this approach, we use features that may be local or global We do not use any relations among the features
Symbolic Matching
An object could be represented not only by its features but also by the relations among features The relations among features may be spatial or some other type An object in such cases may be represented as a graph As shown in Figure 158, each node of the graph represents a feature, and arcs connecting nodes represent relations among the objects The object recognition problem then is considered as a graph matching problem A graph matching problem can be defined as follows Given two graphs G 1 and G2 containing nodes N ij , where i and j denote the graph number and the node number, respectively, the relations among nodes j and k is represented by Rijk Define a similarity measure for the graphs that considers the similarities of all nodes and functions In most applications of machine vision, objects to be recognized may be partially visible A recognition system must recognize objects from their partial views Recognition techniques that use global features and must have all features present are not suitable in these applications In a way, the partialview object recognition problem is similar to the graph embedding problem studied in graph theory The problem in object recognition becomes different when we start considering the similarity of nodes and relations among them We discuss this type of matching in more detail later, in the section on verification
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