Recognition and Location Estimation for Multiple Indoor Static Objects
R. Balamurugan1, R. Arunkumar2, G. Prabakaran3
1R. Balamurugan, Research Scholar, Department of Computer Science and Engineering, Annamalai University.
2Dr. R. Arunkumar, Associate Professor, Department of Computer Science and Engineering, Annamalai University.
3Dr. G. Prabakaran, Associate Professor, Department of Computer Science and Engineering, Annamalai University.
Manuscript received on 01 March 2019 | Revised Manuscript received on 07 March 2019 | Manuscript published on 30 July 2019 | PP: 5152-5156 | Volume-8 Issue-2, July 2019 | Retrieval Number: B2951078219/19©BEIESP | DOI: 10.35940/ijrte.B2951.078219
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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: Locating objects in an image is a very useful task for robotic navigation and visually impaired persons. The ultimate goal of my work is to position the recognized objects in the image. Objects are detected using Adaboost techniques and also recognized from the real-time images. Objects are detected using AdaBoost classifier. SIFT features are extracted from the objects found in the image and classified using Support Vector Machine, and the position of an objects are estimated. We proposed IOLE algorithm to estimate the location of object in an image.
Index Terms: Object Detection, AdaBoost Classifier, Support Vector Machine (SVM) Classifier, Scale Invariant Feature Transform (SIFT). Image Object Location Estimation Algorithm (IOLE)
Scope of the Article: Image Processing and Pattern Recognition