Segmentation is a process of sub dividing an image into its constituent regions or objects. Image segmentation algorithms are based on two basic properties of intensity values, Discontinuity and similarity. In the first category, the approach is to partition an Image based on intensity, such as edges in an image. The second categories are based on portioning an image into regions that are similar according to a set of predefined criteria. This involves thresholding, region-growing and region splitting and merging.
Edge Detection
Point and Line detection certainly are important on segmentation. Edge detection is by far the most common approach for detecting meaningful discontinuities in gray level. An edge is a set of connected pixel that lies on the boundary between two regions. In practice optics, sampling and other image acquisition imperfections yield edges that are blurred, with the degree of blurring being determined by factors such as the quality of the image acquisition system, the sampling rate and illumination conditions under which the image is acquired.
Key-point Detection
Key point is the point where the change in the slope curve is different from its surroundings. Key point act as major shape feature in determining the shape of the object. After the number of Key points in an object is been detected, it will be optimized in order to eliminate the false key points considered during evaluation.
0 comments:
Post a Comment