Gaussian Three-Dimensional kernel SVM for Edge Detection Applications
This addresses edge detection in image processing, but it appears incremental as it builds on existing SVM and kernel methods.
The paper tackled edge detection by proposing a novel SVM algorithm with a three-dimensional Gaussian radial basis function kernel, achieving better performance than classical methods like Sobel and Canny detectors.
This paper presents a novel and uniform algorithm for edge detection based on SVM (support vector machine) with Three-dimensional Gaussian radial basis function with kernel. Because of disadvantages in traditional edge detection such as inaccurate edge location, rough edge and careless on detect soft edge. The experimental results indicate how the SVM can detect edge in efficient way. The performance of the proposed algorithm is compared with existing methods, including Sobel and canny detectors. The results show that this method is better than classical algorithm such as canny and Sobel detector.