ゲーム理論とDNNによるシーン認識とヒューマンロボットインタラクションへの応用
Scene recognition based on DNN and game theory with its applications in human-robot interaction
マルコフ確率場とゲーム理論で画像前処理を行い、三重項サンプルを用いた改良DNNでシーン認識を実現し、ロボット視覚制御に応用した研究。
著者: R. Q. Wang, W. Z. Wang, D. Z. Zhao, G. H. Chen, D. S. Luo
分類: cs.RO, cs.CV, cs.LG
原文アブストラクト
Scene recognition model based on the DNN and game theory with its applications in human-robot interaction is proposed in this paper. The use of deep learning methods in the field of scene recognition is still in its infancy, but has become an important trend in the future. As the innovative idea of the paper, we propose the following novelties. (1) In this paper, the image registration problem is transformed into a problem of minimum energy in Markov Random Field to finalize the image pre-processing task. Game theory is used to find the optimal. (2) We select neighboring homogeneous sample features and the neighboring heterogeneous sample features for the extracted sample features to build a triple and modify the traditional neural network to propose the novel DNN for scene understanding. (3) The robot control is well combined to guide the robot vision for multiple tasks. The experiment is then conducted to validate the overall performance.
