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Segmentation using none toget none for web,windows applicationscan a barcode and print + C D H Visal Basic .NET Fig. 8.6. A graph with two connected components. Fig. 8.7. An image containing two connected components. 111122211111111111111 11111111 11122221111113333333333333111 11112221111113333333333333111 11112211111111111111333311111 11112211111111111111333331111 11112211111111111111333333111 11222222111111111113333333111 11224422211111111133333331111 11222222111111111111333111111 11112211111111111111111111111. Fig. 8.8. The label image corresponding to Fig. 8.7. The production of a s egmented picture such as Fig. 8.2 requires an analysis of connectedness.

That is, a pixel is in region i if it is above threshold and is adjacent to a pixel in region i. Since regions may curve and fork, the analysis cannot be as simple as starting at the top and marking connected pixels going down. Instead, a more sophisticated technique is needed.

The term connected component comes from graph theory. Consider the graph in Fig. 8.

6. This graph has eight vertices, eight edges, and two connected components. That is, there is a path along edges from A to D, or B to E, or F to H, etc.

But there is no path from A to F. In image analysis, we think of the foreground pixels as vertices in a graph, and the adjacent to property as determining the edges in the graph. With this de nition, we see that the image in Fig.

8.7 contains two connected components. Graphs will be revisited in more detail in 12.

In order to make use of the concept of connected component labeling (CCL), we must also de ne a label image which is an iconic representation, isomorphic to the original image, in which each pixel contains the number of the component to which it belongs. One algorithm which produces a label image is known as region growing. It utilizes a label memory corresponding to the frame buffer just as Fig.

8.8 corresponds to Fig. 8.

7. In this description, we will refer to black pixels as object and white as background. Initially, each cell in the label memory L is set to zero.

We will refer to the picture memory as f. Thus the labeling operation can be written as L(x, y) N for some label number N..

Recursive region growing algorithm This algorithm implem ents region growing by using a pushdown stack on which to temporarily keep the coordinates of pixels in the region.. 8.3 Connected component analysis (1) Find an unlabeled black pixel; that is, L(x, y) = 0. Choose a new label number for this region, call it N. If all pixels have been labeled, stop.

(2) L(x, y) N . (3) If f (x 1, y) is black and L(x 1, y) = 0, push the coordinate pair (x 1, y) onto the stack. If f (x + 1, y) is black and L(x + 1, y) = 0, push (x + 1, y) onto the stack.

If f (x, y 1) is black and L(x, y 1) = 0, push (x, y 1) onto the stack. If f (x, y + 1) is black and L(x, y + 1) = 0, push (x, y + 1) onto the stack. (4) Choose a new (x, y) by popping the stack.

(5) If the stack is empty, go to 1, else go to 2. This labeling operation results in a set of connected regions, each assigned a unique label number. To nd the region to which any given pixel belongs, the computer has only to interrogate the corresponding location in the L memory and read the region number.

. EXAMPLE Applying region growing Fig. 8.9 shows a 4 7 array of pixels.

Assume the initial value of x, y is 2, 4 . Apply algorithm grow and show the contents of the stack and L each time step (3) is executed. Let the initial value of N be 1.

. Solution Pass 1. Immediately a fter execution of step (3). The algorithm has examined pixel 2, 4 , examined its 4-neighbors, and detected only one 4-neighbor in the foreground, the pixel at 3, 4 .

Thus, the coordinates of that pixel are placed on.
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