Cumulative Mean Intensity Differential Transition Algorithm for Edge Detection
Ganesh Pai1, M Sharmila Kumari2
1Ganesh Pai, Department of Computer Science & Engineering, P.A. College of Engineering, Mangalore (Karnataka), India.
2M Sharmila Kumari, Department of Computer Science & Engineering, P. A. College of Engineering, Mangalore (Karnataka), India.
Manuscript received on 13 February 2020 | Revised Manuscript received on 20 February 2020 | Manuscript Published on 28 February 2020 | PP: 11-17 | Volume-8 Issue-5S February 2020 | Retrieval Number: E10030285S20/2020©BEIESP | DOI: 10.35940/ijrte.E1003.0285S20
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© 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: Edge Detection plays a vital role in machine vision applications and thereby variety of edge detection algorithms being developed over time for both grey scale and colour images. In this paper, a new technique for edge detection called cumulative mean intensity differential transition algorithm (CuMIDT Algorithm) is proposed. This approach focuses on learning variations in the local pixel intensities and predicting the possible edge when the intensity deviation goes out of the stipulated window area. Ramps at the edge boundaries and zero crossing are addressed using differential transition model. Experimentation are done on standard FDDB dataset and real dataset. It is observed that the proposed approach gives better results when compared to the recently proposed novel edge detection algorithms.
Keywords: Edge Detection, CuMIDT, Differential Transition Model.
Scope of the Article: Parallel and Distributed Algorithms