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Titlebook: Advances in Multimedia Information Processing – PCM 2017; 18th Pacific-Rim Con Bing Zeng,Qingming Huang,Xiaopeng Fan Conference proceedings

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61#
發(fā)表于 2025-4-1 04:24:22 | 只看該作者
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64#
發(fā)表于 2025-4-1 14:20:22 | 只看該作者
A Competitive Combat Strategy and Tactics in RTS Games AI and StarCraftcreating an army and If he is building up his army, he is losing out on having a strong base. The key to winning, in StarCraft or any other RTS game is to balance strategy, tactics, macro and micro. To improve the game, one has to be able to keep track of everything that’s going on over the entire m
65#
發(fā)表于 2025-4-1 22:28:00 | 只看該作者
Indoor Scene Classification by?Incorporating Predicted Depth Descriptorh information within the image scene classification systems, mainly because the lack of depth labeling in existing monocular image datasets. In this paper, we introduce a framework to overcome this limitation by incorporating the predicted depth descriptor of the monocular images for indoor scene cl
66#
發(fā)表于 2025-4-2 01:21:31 | 只看該作者
Multiple Thermal Face Detection in Unconstrained Environments Using Fully Convolutional Networksime surveillance. This paper presents an effective method based on fully convolutional network (FCN), density-based spatial clustering of applications with noise (DBSCAN) and non-maximum suppression (NMS) algorithm. Our proposed approach captures the thermal face features automatically using FCN. Th
67#
發(fā)表于 2025-4-2 05:15:03 | 只看該作者
Object Proposal via Depth Connectivity Constrained Groupingt proposal method on RGB-D images with the constraint of depth connectivity, which can improve the key techniques in grouping based object proposal effectively, including segment generation, hypothesis expansion and candidate ranking. Given an RGB-D image, we first generate segments using depth awar
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