V
Venkatraman Narayanan
Researcher at University of Madras
Publications - 49
Citations - 2916
Venkatraman Narayanan is an academic researcher from University of Madras. The author has contributed to research in topics: Heuristics & Heuristic. The author has an hindex of 15, co-authored 47 publications receiving 1935 citations. Previous affiliations of Venkatraman Narayanan include Carnegie Mellon University & University of Maryland, College Park.
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Proceedings ArticleDOI
PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes
TL;DR: PoseCNN as discussed by the authors estimates the 3D translation of an object by localizing its center in the image and predicting its distance from the camera, and regresses to a quaternion representation.
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PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes
TL;DR: This work introduces PoseCNN, a new Convolutional Neural Network for 6D object pose estimation, which is highly robust to occlusions, can handle symmetric objects, and provide accurate pose estimation using only color images as input.
Journal ArticleDOI
Visible light degradation of textile effluent using novel catalyst ZnO/γ-Mn2O3
TL;DR: In this paper, the novel ZnO/γ-Mn2O3 (various weight percentages) nanocomposite catalysts were prepared by thermal decomposition method and their size, shape, and surface area were characterized by various techniques.
Journal ArticleDOI
ZnO/CdO nanocomposites for textile effluent degradation and electrochemical detection
R. Saravanan,Francisco Gracia,Mohammad Mansoob Khan,V. Poornima,Vinod Kumar Gupta,Vinod Kumar Gupta,Vinod Kumar Gupta,Venkatraman Narayanan,A. Stephen +8 more
TL;DR: In this paper, the photocatalytic and electrochemical activity of ZnO and CdO nanocomposites were determined by a vapor to solid mechanism and were characterized by different physical and chemical techniques.
Proceedings Article
Multi-Heuristic A*
TL;DR: In this article, the authors present a novel heuristic search framework, called Multi-Heuristic A* (MHA*), which simultaneously uses multiple, arbitrarily inadmissible heuristic functions and one consistent heuristic to search for complete and bounded suboptimal solutions.