Multiple Object Detection in Images using Template Matching
S. Geethapriya1, K. Devaki2, V. Murali Bhaskaran3,
1S.Geethapriya*, M.E, Department Of Computer Science and Engineering, Rajalakshmi Engineering College, Chennai, India.
2Dr. K. Devaki, Professor, Department of Computer Science and Engineering, Rajalakshmi Engineering College, Chennai, India.
3Dr. V. MuraliBhaskaran, Dean-Academics and Professor, Department of CSE, Rajalakshmi Engineering College, Chennai, India.
Manuscript received on October 12, 2019. | Revised Manuscript received on 22 October, 2019. | Manuscript published on November 10, 2019. | PP: 3502-3506 | Volume-9 Issue-1, November 2019. | Retrieval Number: A5187119119/2019©BEIESP | DOI: 10.35940/ijitee.A5187.119119
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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: Template matching is an application in computer vision for finding similar objects between the images. Template matching is a key component in image analysis process for mapping similar patterns between images. The detection and matching of objects has gained widespread application in quality control, manufacturing and medical applications. The major challenges in template matching process are occlusion, background noise and non-rigid transformation particularly in medical images. The present work proposes a template matching method based on correlation analysis techniques to detect multiple objects occurrence in an image. The method has been experimented for various real-time applications. In medical images, the proposed method gave an accuracy of 86% in detecting the nodules in lung CT images.
Keywords: Template Matching, Correlation Analysis, Hadoop Framework, OpenCV
Scope of the Article: Patterns and Frameworks