Image matching in .NET Develop Code-39 in .NET Image matching

Image matching using .net framework todisplay 3 of 9 with web,windows application Monarch of MAP methods. See .net framework bar code 39 [13.

26] for a well-written concise description. Methods like this also nd applications in target tracking and automatic target recognition (ATR) [13.12].

. 13A.2. Neural networks for Visual Studio .NET Code 39 Extended object recognition We have already discussed the fact that pattern recognition techniques provide for us a means of making what is it decisions, when we have been presented with a set of measurements (features) which describe the item being observed. There are many ways to develop classi ers, and methods which follow the neural networks paradigm have been among the most successful.

Neural networks accept features as inputs and produce decisions as outputs. They are based on mathematical abstractions of what we know about how individual neurons compute. There are two types of neural networks which can perform matching, feedforward and recurrent.

. 13A.2.1.

Feedforward neural n Code 39 Full ASCII for .NET etworks In a feedforward neural network, each computational element (which we will henceforth refer to as a neuron ) has a large number of inputs, and a single output. Although many variations have been explored in the literature, the most common version of the computation performed by a single neuron is y = S( i w i xi ), where the function S is a sigmoid, the xi are the inputs and the w i are weights which modify the signi cance of the various inputs to the neuron.

Fig. 13.7 illustrates the architecture and naming conventions for a single neuron.

Almost arbitrary functions can be computed by using layers of neurons, as illustrated in Fig. 13.8.

The principal problem in neural network design is determining the weights in order to make the input produce an appropriate output. In the feedforward case, information about the model(s) is embedded in the weights, and when presented with image information, perhaps represented by shape features, the network produces a yes answer if the feature vector matches the model. The principal method for determining weights involves nding the weights which solve a gradient descent problem, perhaps minimizing the mean squared difference between what the output is and what it should be.

Such an algorithm implements. x1 x2 x3 w1 w2 w3 Fig. 13.7.

Computati on performed by a single neuron. Each input (xi ) is multiplied by a weight (wi ) and the results are added, producing a signal u, which is passed through a sigmoid-like nonlinearity function (S) producing the neuron output y..

Topic 13A Matching Decision boundary is a hyperplane Single-layer perceptron Convex regions. Can be open or closed. Two-layer Arbitrary decision r egions Multilayer (usually 3). Fig. 13.8.

Types of Code39 for .NET feedforward neural networks, and the decision regions which they can implement. .

Fig. 13.9.

A feedfor ward neural network with three inputs and two outputs. Each circle denotes a neuron. Weights are not explicitly shown, but exist on the connections.

. the familiar gradient descent rule w i j (t + t) = w i j (t) ck M S E. w i j Using the three-leve l neural network model illustrated in Fig. 13.9, the gradient descent rule may be readily implemented by making use of the chain rule for derivatives.

Hussain and Kabuka [13.24] demonstrate use of a neural network for character recognition..

Image matching 13A.2.2.

Recurrent neural net bar code 39 for .NET works A recurrent neural network (NN) is one which feeds the output back to the input at run time, as illustrated in Fig. 13.

10. Following the same notation used earlier, in the steady state, the output of neuron i satis es. v i = S(yi ) = S w i j v j Ii . (13.13). Fig. 13.10.

A recurr Visual Studio .NET Code 39 Full ASCII ent neural network with 3 neurons. The weights are not shown, but each input to each neuron has an associated weight.

. This model of the be havior of a neuron is true only in the steady state. That is, since the output is dependent on the input, which is the output, which is dependent . .

. (to iterate is human, to recurse, divine2 ). But such a description is woefully inadequate when things are changing.

In that case, we need some model of the dynamics of the system. Many different models can be used, and the reader is referred to the literature [13.15, 13.

20, 13.23] for a closer examination. Here, we consider a single, rather simple model, one in which the rate of change of output from the summer is dependent on the input, and can be represented by a rst-order differential equation: d yi (t) = yi (t) + dt.

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