Publication Type : Conference Proceedings
Publisher : Proceedings of International Conference on Computational Intelligence and Data Engineering (Lecture Notes on Data Engineering and Communications Technologies), Springer Singapore
Source : Proceedings of International Conference on Computational Intelligence and Data Engineering (Lecture Notes on Data Engineering and Communications Technologies), Springer Singapore, Volume 28, Singapore, p.231-240. (2019)
ISBN : 9789811364594
Campus : Bengaluru, Coimbatore
School : Department of Computer Science and Engineering, School of Engineering
Department : Computer Science
Year : 2019
Abstract : The object recognition is one of the most challenging tasks in computer vision, especially in the case of real-time robotic object recognition scenes where it is difficult to predefine an object and its location.Hena, Kausar To address this challenge, we propose an object detection method that can be adaptive to learn objects independent of the environment, by enhancing the relevant features of the object and by suppressing the other irrelevant feature. The proposed method has been modeled to learn the association of features from the given training dataset.Amudha, J. Using dynamic evolution of neuro-fuzzy inference system (DENFIS) model has been used to generate number of rules from the cluster formed from the dataset. The validation of the model has been carried on various datasets created from the real-world scenario.Aarthi, R. The system is capable of locating the target regardless of scale, illumination variance, and background
Cite this Research Publication : K. Hena, Amudha J., and Aarthi, R., “A Dynamic Object Detection In Real-World Scenarios”, Proceedings of International Conference on Computational Intelligence and Data Engineering (Lecture Notes on Data Engineering and Communications Technologies), vol. 28. Springer Singapore, Singapore, pp. 231-240., 2019.