ATR algorithms in .NET Make 3 of 9 barcode in .NET ATR algorithms

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18.5 ATR algorithms use vs .net code-39 implementation tocompose bar code 39 with .net Microsoft Office Development. Microsoft Office 2000/2003/2007/2010 Another ty .net framework Code 39 Extended pe of ATR system is based on the premise that the more sensory data that is available from the target of interest, the better the system performance. This is intuitively obvious for sensors that have complementary properties.

Due to numerous limitations of single-sensor ATR systems, there has been a move toward multisensor targeting systems and, hence, the problem of correlating and combining data generated from multiple sensors. This is also referred to sometimes as multisensor fusion, however, the information sources may be different sensors (sensor fusion) or different algorithms (algorithm fusion) [18.32].

Finally, some researchers break the set of ATR algorithms into model-based methods, statistics-based methods, and template-based methods. These three categorizations are discussed in more detail below..

Model-based techniques Most model .net framework barcode 3/9 -based techniques are geometry-based. They pose the question: Given a certain viewing angle, what should the target look like [18.

1, 18.6, 18.12, 18.

13]. This is potentially a powerful philosophy, as it provides information on portions of the target that may have been occluded due to its position e.g.

, from a certain viewpoint the barrel of a tank may be missing. However if we have a 3D model of the target, we could generate all possible views and perform a combinatorial search [18.65] to get a match.

Model-based techniques are readily combined with different data types, especially range (laser radar) images [18.87]. However, like almost everywhere in machine vision, some optimization problem must be solved, using neural networks [18.

29], genetic algorithms [18.10], or other optimization methods. Usually, descriptions that correspond to scene structure and geometry alone are obtained as opposed to scene physics (heat, light, material properties, etc.

). Matching is then the process of hypothesizing and verifying matches between model and image points. This process produces a 3D to 2D transformation which brings the 3D model points into correspondence with the 2D image points.

The best match is then the transformation which best explains the scene. Solution of the 3D to 2D correspondence is basically solution of the perspective equation. Errors between projected model points and the corresponding image points help verify how good a match is.

These methods are powerful, but require a lot of processing and a large database. They perform poorly when targets are occluded [18.73] as this results in a case of incomplete information.

To this end, a lot of work is being done in obtaining the actual geometric shape of the target from occluded views [18.75]. Occlusions, in general, fall into two distinct categories contiguous (buildings or whole trees as shown in Fig.

18.7) and distributed (tree branches). The rst type is easier to deal with as it is possible to have enough information on the nonoccluded.

Fig. 18.7. Image of a truck occluded by a tree [18.65]. SPIE, used with permission. Automatic target recognition A histogram may be constructed of the chain code directions. portion to Visual Studio .NET USS Code 39 solve the problem. In [18.

73], Sadjadi presents an approach to detection under occlusion. The boundary of the segmented region of the image is converted to a chain code. The chain code is then matched using histogram intersection (described in the next section) with a set of models.

The match is used as a measure of the con dence of the system. If, however, the degree of con dence is still low, the system assumes that the object is partially occluded and now the object is matched with occlusion models in a database..

Statistics-based techniques The ideas VS .NET Code39 behind statistics-based techniques are the same as those described in 14: (1) Obtain features; (2) develop statistical measurements which characterize different classes; (3) make decisions which optimize some measure such as minimum cost, or maximum probability of correct decision. In this section, we consider only multispectral measurements.

. Multispectral matching One techni 3 of 9 barcode for .NET que for multispectral analysis uses the concept of histogram intersection introduced by Swain and Ballard [18.82] for the realm of color images.

The idea is simply to compare the histograms of two images and determine any overlap factor (how many pixels in the data base histogram match those in the histogram of the new image). Speci cally, given a pair of histograms, I (from new image) and M (from database), each containing n bins, the intersection is de ned to be n j=1 min(I j , M j ). The result is the number of pixels that have the same color in the two images.

This number may be normalized to obtain overlap factor. The color of an object is subject to signi cant change depending upon varying lighting conditions; and in this situation, this simple algorithm will clearly not give us a good match. To overcome this problem, Funt and Finlayson [18.

24] combined the idea of histogram intersection with a concept referred to as color constancy [18.23], which removes effects of varying illumination conditions and in effect, normalizes the image to a standard illuminant. Now that we have a data base also in standard illuminant conditions, we can compare apples with apples and use the histogram intersection method described above.

We have not put any restriction to the dimensionality of the histogram, and hence can extend this concept to higher dimensions (more sensors), obtaining a more robust system. Another measure of spectral match between a known target signature and an observed signature is obtained by treating the signatures as vectors and nding the inner product between the two vectors [18.93].

The better the match, the closer the angular separation to zero. In other words, if we have two d-dimensional measurements in the spectral signatures, X and Y , then the distance between these two measurements.
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