Edge Detection Based on Improved Non-Maximum Suppression Method
Sreedhar. T1, Sathappan. S2
1Sreedhar. T, Ph. D Research Scholar, Department of Computer Science, Erode Arts & Science College (Autonomous), Erode, India.
2Sathappan. S, Research Supervisor and Associate Professor, Department of Computer Science, Erode Arts & Science College (Autonomous), Erode, India.
Manuscript received on 02 July 2019 | Revised Manuscript received on 09 July 2019 | Manuscript published on 30 July 2019 | PP: 3264-3269 | Volume-8 Issue-9, July 2019 | Retrieval Number: I9007078919/19©BEIESP | DOI: 10.35940/ijitee.I9007.078919
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Semantic Segmentation and edge detection are important research fields for scene understanding in computer vision. A hierarchical framework called Contextual Hierarchical Model (CHM) was proposed for semantic image segmentation and edge detection. It learned contextual information using a Logistic Disjunctive Normal Networks (LDNN) classifier. The class average accuracy of CHM was improved by defining a global constraint using Conditional Random Field (CRF), Hierarchical CRF (HCRF), Higher order HCRF (HHCRF). The LDNN was improved by using proximal gradient method which minimizes the quadratic error and it had high convergence rate than gradient descent method. The weight and bias terms of LDNN was optimized by using Grey Wolf Optimization algorithm (GWO) which improves the classification accuracy and it also reduces time complexity of LDNN. During the edge detection using CHM, a multi-scale strategy was adopted to compute edge maps. A Non-Maximum Suppression (NMS) was used to obtain the thinned edges in images. However, it is a post-processing step which consumes additional time in edge detection process. So, in this paper, the edge detection is interpreted as a classification problem where the thinned edges in images are obtained without any post-processing step. It reduces the time consumption. Two key ingredients such as loss and joint processing are included in NMS to improve the detection of thinned edges. The loss is used to penalize the double edge detection and the joint processing is used to reduce the loss of edge detection by including a pair features as additional feature for edge detection. The pair features, Haar, Histogram of Gradient (HOG) and SIFT features are given as input to LDNN to detect the edges that improve the efficiency of CHM based edge detection.
Keywords: Semantic Image Segmentation, Edge Detection, Non-Maximum Suppression, Post-Processing.
Scope of the Article: Image Processing and Pattern Recognition