The Importance Of Image Segmentation

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IMAGE SEGMENTATION
Among the various image processing techniques image segmentation is very important step to analyse the given image (A. M. Khan, 2013). Image segmentation is the fundamental step to analyze image and extract data from them. The goal of image segmentation is to cluster the pixels into small image region and that region corresponding to individual surfaces, objects, or natural parts of objects. Segmentation subdivides an image into its constituent regions or objects. The level of subdivision is depending on the problem being solved. That is, segmentation should stop when the objects of interest have been isolated. The goal of segmentation is to change and simplify the representation of an image into something that is more …show more content…

It could detect the variation of grey levels, but it is sensitive to noise. Edge detection is an important task in image processing. It is main tool in pattern recognition, image segmentation, and scene analysis. Edge are local changes in the image intensity edge typically occur on the boundary between two regions. The main features are extracted from the edges of an image. Edge detection has major features for image analysis. These features are used by advanced computer vision algorithm. Edge detection is used for object detection which serves various applications like medical image processing, biometrics …show more content…

Thresholding technique is based on image space regions i.e. on characteristics of image. Thresholding operation convert a multilevel image into a binary that is it choose a proper thresholding T, to divide image pixels into several regions and separate objects from background. Any pixel (x, y) is considered as a part of object if its intensity is greater than or equal to threshold value i.e., f(x, y) ≥T, else pixel belong to background.
Based on the selection of threshold value, there are two type of thresholding method:-
1. Global thresholding: - global thresholding methods is used when the intensity distribution between the objects of foreground and background are very distinct. When the difference between foreground and background objects is very distinct, a single value of threshold can simply be used to differentiate both objects apart. Thus, in this type of thresholding, the value of threshold T depends on the property of the pixel and the grey level value of the image. Some of the common used global thresholding methods are Otsu method, entropy based thresholding, etc.
2. Local thresholding: - This method divides an image into several sub regions and then chooses various thresholds Ts for each sub region respectively. Thus, threshold depends on

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