barcode for .net C# Conclusion in .NET Integrating barcode 3/9 in .NET Conclusion

13.4 Conclusion using barcode encoding for none control to generate, create none image in none applications.barcode c# class Association g none none raphs use the concepts and formalisms of consistent labeling directly. The advantage of using a graph structure is that the search for largest clique is aided by a body of available software for performing such searches as quickly as computational complexity allows. Similarly, the springs and templates ideas measure both consistency and deviation from consistency.

The springs and templates concepts also illustrate both how one might construct an appropriate objective function, and a problem that can easily arise if one does not pay attention to interpretation of the objective function if we are summing match quality, a good match of many things (adding up lots of small numbers) may be more than (and therefore worse than) a poor match of only a few things (adding up just a few rather large numbers). We began this chapter by pointing out that formal optimization methods, either descent or hill-climbing, are hard to apply to image matching because the search space is littered with local minima. However, if we initialize the algorithm suf ciently close to the solution, such techniques will work.

We used the sum of squared differences (SSD), also sometimes called the sum-squared error, as the objective function. Eigenimages are lower dimensionality representations of the original images. The projections are chosen by minimizing the error between the original data and the projected data.

. Windows Forms Association graphs are a kind of consistent labeling. Objective functions need suitable normalization. SSD is a common objective function. 13.5 Vocabulary You should know the meanings of the following terms. Association graph Clique Image matching Correspondenc none none e Deformable template Eigenimage Hill-climbing Matching metric PCA Template Assignment 13.1 In this chapter, we stated that the problem of finding the largest clique is NP-complete. What does that really mean Suppose you have an association graph with ten nodes, interconnected with 20 edges.

How many tests must you perform to find all cliques (which you must do in order to identify which of these are maximal) You ARE permitted (encouraged!) to look up clique-finding in a graph theory text. 13.2 In section 13.

3.1, an example problem is presented which involves an association graph which allows for segmentation errors. The result of that graph is two maximal cliques, which (presumably) mean two different interpretations of the scene.

Describe in words these two interpretations. 13.3 In the bibliography for this chapter, there is an incomplete citation to Olson [13.

36]. First, locate a copy of that paper. You may use a search engine, the Web, the library, or any other resource you wish.

In that paper, the author does template matching in a different way: Using a binary (edge) image and a similar template, he does not ask Does the template match the image at this point Instead he asks, At this point, how far is it to the nearest edge point How does he perform this operation, apparently a search, efficiently Once he knows the distance to the nearest edge point, how does he make use of that information to compute a quality of match measure . Assignment Assignment 13.5 Vocabulary Assignment 13.4 In an im age-matching problem, we have two types of objects, lions and antelope (which occupy only one pixel each)..

A scene may c none for none ontain only lions and antelope. Lions hunt in packs, so if you see one lion, you will see at least one other lion, usually about 5 pixels away. Antelope stay as close to one another as possible.

Except for certain rare, and (for the antelope) unpleasant events, lions and antelope are VERY far apart.. We wish to us e relaxation labeling to solve this assignment problem. All the formulae are in the book except the formula for the consistency r(a, 1 ,b, 2 ), where a and b are points of interest in the image and the lambdas are labels for either antelope or lion. Invent an r function for this problem.

That is, tell how to compute values for: (1) r(a, lion, b, antelope) (2) r(a, lion, b, lion) (3) r(a, antelope, b, lion) (4) r(a, antelope, b, antelope)..
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