In this paper, we propose a modified watershed algorithm for image segmentation using distances transform and image smoothing method, an improved version of watershed segmentation.This algorithm allows better boundary localization due to the edge information brought by watersheds. Thus, the proposed method has been found to be able to reduce over segmentation and this would ultimately lead to easier handling by the machine towards higher level of processing at subsequent stages. The algorithm has been tested on colored image obtained from real life and has been found to yield satisfactory segmentation results.
An edge detection algorithm for digital images is proposed in this paper. Edge detection is one of the important and most difficult tasks in image processing and analysis. In images edges can create major variation in the picture quality where edges are areas with strong intensity contrasts. Edges in digital images are areas with strong intensity contrasts and a jump in intensity from one pixel to the next can create major variation in the picture quality. This paper proposed an effective edge detection algorithm based morphological edge detectors and watershed segmentation algorithm using distance transform. The result confirms that the proposed algorithm is found to yield satisfactory and efficient segmentation of the digital images for edge detection. Experimental result presented in this paper is obtained by using MATLAB.
For more than a century fingerprints ware considered to be the identifying mark for the human beings. Fingerprint is a protected human organ and an effective biometric approach to human or personal identification. It acts like living passwords for humans as its texture is stable throughout the human life. Fingerprints are an impression left by the friction ridges of human finger. This paper contains a very useful image segmentation method for fingerprints segmentation by taking the idea from friction ridges of human finger and also with an effective storage capacity for the segmented images. Watershed algorithm depends on ridges to perform a proper segmentation, a property that is often fulfilled in contour detection where the boundaries of the objects are expressed as ridges. The tool we have used is MAT LAB, typically using the MAT LAB editor.
A modified gray scale watershed image segmentation algorithm suitable for low contrast image has been proposed. Digital images acquired from far away stellar objects (like stars, planets, galaxies, comets etc.) are prone to be severally affected by various types of noises and the contrast of these categories of images are generally found to be low. In present study, a preserving de noising method is presented by a contrast adjustment based on adaptive histogram equalization technique. The proposed method has been found to yield satisfactory segmentation of the stellar images. The entropy of the original and the segmented image is compared and the result confirms to the reality.
In various spectrum of image processing, images are acquired with low variations in the intensity level and thus they possess small gradient values. In these cases, it is convenient to apply watershed segmentation on the gradient image, rather than the original image. The most common output of these segmented images is over segmentation and it implies the presence of numerous watershed ridges that do not correspond to the object boundaries of interest. Under this intermingled problematic scenario, the role of the spatial edge sharpening filters should not be ignored. This research paper deals with the role of various edge sharpening filters and to find the ultimate effect of them on the output image using watershed algorithm is presented.
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