Abstract
An improved scheme for contour detection with better performance measure has been proposed. It is based on the response of human visual system during visualization of any type of an image. The scheme consisted of two parts namely to find the edge of the image by using the modified mask of Laplacian of Gaussian edge operator and subsequent modulation of the edge by using watershed algorithm. The method has been applied to a digital image and better performance measure of contour detection has been achieved.
Highlights
Contour detection [1,2,3] in real life images is a major problem to enable them for subsequent processing by machines
The gradient image is accrued from the grayscale image with the help of proposed modified mask of Laplacian of Gaussian edge detection operator
We obtained output images that consist of all edge information and regions about the objects of input image. It have been observed by comparing the resultant images that the segmented images with watershed algorithm using conventional edge detection operators produces over segmentation and the edges in the images are not very sharp
Summary
Contour detection [1,2,3] in real life images is a major problem to enable them for subsequent processing by machines. It has been observed that the use of standard 5x5 mask of Laplacian of Gaussian edge detector for image segmentation does not solve the main problem associated with the watershed transform: over-segmentation. In this paper a modified scheme of Laplacian of Gaussian operator with 9x9 mask for generating gradient images is proposed and produces greater accuracy and lesser over segmentation [5,6] in edge detection with subsequent modulation of the edge by using watershed algorithm. The structure of this work is the following: Section 2 introduces conventional Laplacian of Gaussian edge detection operator for gradient images.
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