On Comparison of Different Image Segmentation Techniques
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Abstract
Nowadays, image processing is mostly denoted to the processing of digital images. Image processing has two branches: improving images and machine vision. Improving images are the methods that use blurring and increase contrast to improve the visual quality of images and ensure that they are displayed correctly in the destination environment (such as a printer or monitor), while machine vision deals with methods that can be used to mean and understand the content of images to be used in robotics. Image segmentation is one of the greatest significant steps on digital image processing. Image segmentation is separation of picture pixels into separate zones that are identical or as closely correlated as possible in terms of brightness, texture, or color. Image segmentation has lots of applications in many image processing tasks, such as image therapy, machine vision, image compression, object detection. This paper, will provide comprehensive review about different image segmentation techniques. We will compare each of the existing methods with each other and will investigate the positive potentials and drawbacks of each of these methods.
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