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= Point Detection = In digital image processing, Image segmentation algorithms are generally based on two properties: discontinuity and similarity. In Discontinuity property, partition of image is based on abrupt changes in pixel intensities. examples are: Point detection, Line detection, Edge detection. On the other hand, similarity-based segmentation techniques group pixels based on their similarity in color, texture, or other features. examples of these method are: Region growing, K-means clustering, Mean shift, Graph cuts in computer vision, Fuzzy clustering, Active contour model. Point detection algorithms aim to identify isolated points or locations in an image where there is a significant change in intensity compared to the surrounding pixels. Discontinuity Detection is usually accomplished by applying a suitable mask to the image. A general 3 * 3 mask: The procedure involves computing the sum of products of the coefficients with the gray levels contained in the region encompassed by the mask. $$ \begin{align} R = w_1z_1 + w_2z_2 + \ldots + w_9z_9 \end{align} $$ $$R = \sum_{i=1}^{9} w_i z_i$$ where $''z_{i}$ is the gray level of the pixel associated with mask coefficient $w_{i}$. F (image pixels)= W (this mask will be putted on every pixel of image)= A =

For any point P at (x,y) position,

If |R| > Threshold Then point is said to be detected at (x,y) position. this formulation measures the weighted differences belween the center point and its neighbors. the idea is that an isolated point (a point whose gray level is significantly different from its background) will be quite different from its surroundings, and thus be easily detectable by this type of mask. This point detection technique is written in book: Digital Image Processing by Gonzalez, Rafael C.; Woods, Richard E. link Also you can check lecture notes of Washington State University here, research paper