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A Novel Capability of object Identification and Recognition Based on Integrated mWMM

Computers, Materials & Continua 2023年第4期75卷 959-976页

作者： MZeeshan Sarwar Mohammed Hamad Alatiyyah Ahmad Jalal Mohammad Shorfuzzaman Nawal Alsufyani Jeongmin Park Department of Computer Science Air UniversityIslamabadPakistan Department of Computer Science College of Sciences and Humanities in AflajPrince Sattam Bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Engineering Tech University of Korea237 Sangidaehak-roSiheung-siGyeonggi-do15073Korea

In the last decade,there has been remarkable progress in the areas of object detection and recognition due to high-quality color images along with their depth maps provided by RGB-D *** enable artificially intelligent machines to easily detect and recognize objects and make real-time decisions according to the given *** cues can improve the quality of object detection and *** main purpose of this research study to find an optimized way of object detection and identification we propose techniques of object detection using two RGB-D *** proposed methodology extracts image normally from depth maps and then performs clustering using the Modified Watson Mixture Model(mWMM).mWMM is challenging to handle when the quality of the image is not ***,the proposed RGB-D-based system uses depth cues for segmentation with the help of *** it extracts multiple features from the segmented *** selected features are fed to the Artificial Neural Network(ANN)and Convolutional Neural Network(CNN)for detecting *** achieved 92.13%of mean accuracy over NYUv1 dataset and 90.00%of mean accuracy for the Redweb_v1 ***,their results are compared and the proposed model with CNN outperforms other state-of-the-art *** proposed architecture can be used in autonomous cars,traffic monitoring,and sports scenes.

关键词： Artificial intelligence convolutional neural network depth images interactive-object detection machine learning

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