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1,148 results on '"Hu, Ruimin"'

201. Improving Intra Coding in H.264\AVC by Image Epitome

Author

Wang, Qijun, Hu, Ruimin, Wang, Zhongyuan, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Muneesawang, Paisarn, editor, Wu, Feng, editor, Kumazawa, Itsuo, editor, Roeksabutr, Athikom, editor, Liao, Mark, editor, and Tang, Xiaoou, editor

Published
2009
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202. The Methods for Temporal Scalable Extension of AVS Video

Author

Wang, Qijun, Hu, Ruimin, Wang, Zhongyuan, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Muneesawang, Paisarn, editor, Wu, Feng, editor, Kumazawa, Itsuo, editor, Roeksabutr, Athikom, editor, Liao, Mark, editor, and Tang, Xiaoou, editor

Published
2009
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203. Extracting Key Frames for Surveillance Video Based on Color Spatial Distribution Histograms

Author

Chang, Jun, Hu, RuiMin, Wang, ZhongYuan, Hang, Bo, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Muneesawang, Paisarn, editor, Wu, Feng, editor, Kumazawa, Itsuo, editor, Roeksabutr, Athikom, editor, Liao, Mark, editor, and Tang, Xiaoou, editor

Published
2009
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204. Surveillance Audio Attention Model Based on Spatial Audio Cues

Author

Hang, Bo, Hu, RuiMin, Yang, YuHong, Ma, Ye, Chang, Jun, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Muneesawang, Paisarn, editor, Wu, Feng, editor, Kumazawa, Itsuo, editor, Roeksabutr, Athikom, editor, Liao, Mark, editor, and Tang, Xiaoou, editor

Published
2009
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205. Camera-Model Identification Using Markovian Transition Probability Matrix

Author

Xu, Guanshuo, Gao, Shang, Shi, Yun Qing, Hu, RuiMin, Su, Wei, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Ho, Anthony T. S., editor, Shi, Yun Q., editor, Kim, H. J., editor, and Barni, Mauro, editor

Published
2009
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206. Improved Object Tracking Algorithm Based on New HSV Color Probability Model

Author

Tian, Gang, Hu, Ruimin, Wang, Zhongyuan, Fu, Youming, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Yu, Wen, editor, He, Haibo, editor, and Zhang, Nian, editor

Published
2009
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222. A Novel Intra/Inter Mode Decision Algorithm for H.264/AVC Based on Spatio-temporal Correlation

Author

Liu, Qiong, Ye, Shengfeng, Hu, Ruimin, Han, Zhen, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Cham, Tat-Jen, editor, Cai, Jianfei, editor, Dorai, Chitra, editor, Rajan, Deepu, editor, Chua, Tat-Seng, editor, and Chia, Liang-Tien, editor

Published
2006
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223. Watch You Under Low-Resolution and Low-Illumination: Face Enhancement via Bi-Factor Degradation Decoupling

Author

Ding, Xin, Wang, Zheng, Fang, Jing, Shu, Zhenyu, Hu, Ruimin, and Lin, Chia-Wen

Abstract

Face enhancement aims to improve low-quality face images to a higher-quality level. However, in real-world nighttime scenes, complex degradation factors often affect these images, making it challenging to preserve important facial details. Existing image enhancement algorithms typically focus on independently conducting image super-resolution and brightness enhancement, assuming a fixed degradation level based on simulated training datasets. Nonetheless, real nighttime scenes involve complex degradation processes, where degradation factors dynamically and variably manifest. Therefore, achieving effective face enhancement in such scenarios is particularly daunting. This work analyzes and unveils the multiple factors of low resolution and low illumination during degradation. Based on this analysis, we propose a Bi-factor Degradation Decoupling network. Our method leverages a decoupling network to generate qualitative and quantitative features corresponding to each factor’s degradation degree in the low-quality environment. These features are then combined with robust facial feature constraints to recover the details of low-quality faces. Extensive experiments demonstrate that our method surpasses state-of-the-art approaches in both enhancement and face super-resolution.

Published
2024
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224. From Multi-Source Virtual to Real: Effective Virtual Data Search for Vehicle Re-Identification

Author

Wan, Zhijing, Xu, Xin, Wang, Zheng, Wang, Zhixiang, and Hu, Ruimin

Abstract

Without tedious and time-consuming labeling processes, virtual datasets have recently shown their superiority for vehicle re-identification (re-ID). Existing virtual to real vehicle re-ID methods employ only a single virtual dataset for model training, while datasets from different generative sources are not jointly exploited. Multiple source virtual datasets contain more data diversity that can boost model performance. We thus propose a multi-source virtual to real vehicle re-ID pipeline, where multiple source virtual datasets are used during training. However, the multi-source virtual dataset suffers from more data redundancy than the single virtual dataset, which can affect the training efficiency. Intuitively, it can be mitigated by virtual data search. Unlike a single virtual dataset, a performance gap exists between multiple source virtual datasets, indicating their different contributions to model learning. Accordingly, we propose to split the multi-source virtual dataset into the main training set and the auxiliary training set, and then design the sampling strategy separately. For the main training set, the Consistent Attribute Distribution-FEature distance Trade-off (CAD-FET) strategy is designed to search for representative data. For the auxiliary training set, a cluster-based sampling strategy is further proposed to search for the most diverse subset. Besides, a simple yet effective two-stage training strategy is proposed to utilize these subsets reasonably. Extensive virtual-to-real vehicle re-ID experiments show that our data sampling method can reduce the volume of the multi-source virtual dataset by around 77%/96% and boost the model performance when tested on the VeRi776/VehicleID.

Published
2024
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235. Where Have You Gone: Category-aware Multigraph Embedding for Missing Point-of-Interest Identification.

Author

Wu, Junhang, Hu, Ruimin, Li, Dengshi, Xiao, Yilin, Ren, Lingfei, and Hu, Wenyi

Subjects
MULTIGRAPH, SOCIAL networks, TASK performance, DATA quality, SHARED workspaces
Abstract

The prevalence of location-based social networks (LBSNs) provides an opportunity for human mobility behavior understanding and prediction. However, data quality issues (e.g., historical check-in POI missing, data sparsity) always limit the effectiveness of existing LBSN-oriented studies, e.g., next Point-of-Interest (POI) recommendation or prediction. In contrast to previous efforts in the above study, we focus on identifying missing POIs that the user has visited at a past specific time and develop a category-aware multigraph embedding (CAME) model. Specifically, CAME jointly captures temporal cyclic effect, user preference, and sequential transition pattern in a unified way by embedding five relational information graphs into a shared dimensional space from both POI- and category-instance levels. The proposed model also incorporates region-level spatial proximity to explore the geographical influence and derives the ranking score list of candidates for missing POI identification. Extensive experiments against state-of-the-art methods are conducted on two real datasets, and the experimental results show its superiority over other competitors. Significantly, the proposed model can be naturally extended to next POI recommendation and prediction tasks with competitive performances. [ABSTRACT FROM AUTHOR]

Published
2023
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