A Pre-processing Step for Efficient Edge Extraction
Naveen Kumar N1, S. Rajkumar2
1Naveen Kumar N*, Assistant Professor – Senior in SCOPE school, VIT University, Vellore, India.
2S. Rajkumar, Associative Professor in SCOPE School, VIT University, Vellore, India.
Manuscript received on November 11, 2019. | Revised Manuscript received on November 20 2019. | Manuscript published on 30 November, 2019. | PP: 11550-11554 | Volume-8 Issue-4, November 2019. | Retrieval Number: D4539118419/2019©BEIESP | DOI: 10.35940/ijrte.D4539.118419
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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: In this paper, we propose a pre-processing step for an efficient edge extraction technique that takes input as an original image to generate an edge map. Generated edge maps could be inputted for state-of-art traditional edge detection algorithms like Canny, Sobel, Prewitt, and recent edge detection algorithms gb-UCM, CED Contours, Structured forest, Sparse Code Gradients and CNN based edge detection Deep Edge, N4 to get better performance. Further, the proposed algorithm has not required any training or learning to improve the edge detection method and is not depending on any parameters. Visual experiments and quantitative evaluation results show that our proposed algorithm greatly improves the modal quality of edge/edge maps. It preserves the original shape, structure of the objects and local features, which presents in an input image. The proposed method takes very less amount of time to execute and making it more suitable for real-time image processing and computer vision applications that depend on edge like classification, object localization, object recognition, image retrieval, segmentation, shape representation.
Keywords: Contour Detection, Efficient Edge Extraction, Image Retrieval. Local Binary Pattern(LBP), Structured Forest.
Scope of the Article: Energy Efficient Building Technology.