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When are pixels most likely to be edge pixels?
When pixels differ greatly from their neighbors, they are most likely “edge” pixels. For example, think of a picture of white piece of paper on a black tabletop. The edges of that paper are where the colors are most different, where white meets black. In the previous example, we looked at two pixels to find edges.
What do you need to know about programming with pixels?
In programming with pixels, we need to be able to think of every pixel as living in a two dimensional world, but continue to access the data in one (since that is how it is made available to us). We can do this via the following formula: Assume a window or image with a given WIDTH and HEIGHT.
How are pixels stored in a two dimensional window?
We are familiar with the idea of each pixel on the screen having an X and Y position in a two dimensional window. However, the array pixels has only one dimension, storing color values in linear sequence. Take the following simple example.
How are pixels displayed on a computer screen?
Most of the time, we view these pixels as miniature rectangles sandwiched together on a computer screen. With a little creative thinking and some lower level manipulation of pixels with code, however, we can display that information in a myriad of ways.
How to find the edge of an image?
1. Using the methods in Section 4.3.3, model the intensity distribution obtained by finding all the edge pixels in an image and including also all pixels adjacent to these pixels. Show that while this gives a sharper valley than for the original intensity distribution, it is not as sharp as for pixels located by the Laplacian operator.
Each bit corresponds to each edge pixel in the template image. The bit 1 shows that the corresponding edge pixel is used for matching, and the bit 0 shows that the corresponding edge pixel is not used. Fig. 3 shows the evaluation method of an individual fitness in the GA.
How are edge pixels used to reduce computational cost?
Additionally, the size of a template table becomes smaller and the computational cost is less by decreasing the number of edge pixels. To realize this function, the proposed method uses the GA [Holland75] to determine the optimal combination of effective edge pixels for matching from the huge combinations of edge pixels.