Search

Your search keyword '"Sang, Nong"' showing total 1,096 results
Start Over Author "Sang, Nong"
1,096 results on '"Sang, Nong"'

102. Color and Luminance Separated Enhancement for Low-Light Images with Brightness Guidance.

Author

Zhang, Feng, Liu, Xinran, Gao, Changxin, and Sang, Nong

Subjects
IMAGE intensifiers, IMAGE enhancement (Imaging systems), COLOR, REFLECTANCE
Abstract

Existing retinex-based low-light image enhancement strategies focus heavily on crafting complex networks for Retinex decomposition but often result in imprecise estimations. To overcome the limitations of previous methods, we introduce a straightforward yet effective strategy for Retinex decomposition, dividing images into colormaps and graymaps as new estimations for reflectance and illumination maps. The enhancement of these maps is separately conducted using a diffusion model for improved restoration. Furthermore, we address the dual challenge of perturbation removal and brightness adjustment in illumination maps by incorporating brightness guidance. This guidance aids in precisely adjusting the brightness while eliminating disturbances, ensuring a more effective enhancement process. Extensive quantitative and qualitative experimental analyses demonstrate that our proposed method improves the performance by approximately 4.4 % on the LOL dataset compared to other state-of-the-art diffusion-based methods, while also validating the model's generalizability across multiple real-world datasets. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

103. Densenet-Based Multi-scale Recurrent Network for Video Restoration with Gaussian Blur

Author

Wu, Liyou, Sang, Nong, Yang, Junyan, Jing, Lihong, Gao, Changxin, Li, Lerenhan, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Zhao, Yao, editor, Barnes, Nick, editor, Chen, Baoquan, editor, Westermann, Rüdiger, editor, Kong, Xiangwei, editor, and Lin, Chunyu, editor

Published
2019
Full Text
View/download PDF

104. Blurred Template Matching Based on Cascaded Network

Author

Peng, Juncai, Sang, Nong, Gao, Changxin, Li, Lerenhan, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Zhao, Yao, editor, Barnes, Nick, editor, Chen, Baoquan, editor, Westermann, Rüdiger, editor, Kong, Xiangwei, editor, and Lin, Chunyu, editor

Published
2019
Full Text
View/download PDF

105. Graph-Based Scale-Aware Network for Human Parsing

Author

Yang, Beibei, Yu, Changqian, Liu, Jiahui, Gao, Changxin, Sang, Nong, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Lin, Zhouchen, editor, Wang, Liang, editor, Yang, Jian, editor, Shi, Guangming, editor, Tan, Tieniu, editor, Zheng, Nanning, editor, Chen, Xilin, editor, and Zhang, Yanning, editor

Published
2019
Full Text
View/download PDF

106. Orientation Adaptive YOLOv3 for Object Detection in Remote Sensing Images

Author

Lei, Jiahui, Gao, Chongjun, Hu, Jing, Gao, Changxin, Sang, Nong, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Lin, Zhouchen, editor, Wang, Liang, editor, Yang, Jian, editor, Shi, Guangming, editor, Tan, Tieniu, editor, Zheng, Nanning, editor, Chen, Xilin, editor, and Zhang, Yanning, editor

Published
2019
Full Text
View/download PDF

112. Exemplar-based Linear Discriminant Analysis for Robust Object Tracking

Author

Gao, Changxin, Chen, Feifei, Yu, Jin-Gang, Huang, Rui, and Sang, Nong

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Tracking-by-detection has become an attractive tracking technique, which treats tracking as a category detection problem. However, the task in tracking is to search for a specific object, rather than an object category as in detection. In this paper, we propose a novel tracking framework based on exemplar detector rather than category detector. The proposed tracker is an ensemble of exemplar-based linear discriminant analysis (ELDA) detectors. Each detector is quite specific and discriminative, because it is trained by a single object instance and massive negatives. To improve its adaptivity, we update both object and background models. Experimental results on several challenging video sequences demonstrate the effectiveness and robustness of our tracking algorithm., Comment: ICIP2014

Published
2014
Full Text
View/download PDF

113. Center-Level Verification Model for Person Re-identification

Author

Zheng, Ruochen, Chen, Yang, Yu, Changqian, Han, Chuchu, Gao, Changxin, Sang, Nong, 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, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Lai, Jian-Huang, editor, Liu, Cheng-Lin, editor, Chen, Xilin, editor, Zhou, Jie, editor, Tan, Tieniu, editor, Zheng, Nanning, editor, and Zha, Hongbin, editor

Published
2018
Full Text
View/download PDF

114. Global Feature Learning with Human Body Region Guided for Person Re-identification

Author

Li, Zhiqiang, Sang, Nong, Chen, Kezhou, Han, Chuchu, Gao, Changxin, 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, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Lai, Jian-Huang, editor, Liu, Cheng-Lin, editor, Chen, Xilin, editor, Zhou, Jie, editor, Tan, Tieniu, editor, Zheng, Nanning, editor, and Zha, Hongbin, editor

Published
2018
Full Text
View/download PDF

115. Re-ranking Person Re-identification with Adaptive Hard Sample Mining

Author

Han, Chuchu, Chen, Kezhou, Wang, Jin, Gao, Changxin, Sang, Nong, 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, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Lai, Jian-Huang, editor, Liu, Cheng-Lin, editor, Chen, Xilin, editor, Zhou, Jie, editor, Tan, Tieniu, editor, Zheng, Nanning, editor, and Zha, Hongbin, editor

Published
2018
Full Text
View/download PDF

118. Semantic segmentation via pixel‐to‐center similarity calculation.

Author

Wu, Dongyue, Guo, Zilin, Li, Aoyan, Yu, Changqian, Sang, Nong, and Gao, Changxin

Subjects
PIXELS, ARTIFICIAL neural networks, COMPUTER vision, IMAGE segmentation
Abstract

Since the fully convolutional network has achieved great success in semantic segmentation, lots of works have been proposed to extract discriminative pixel representations. However, the authors observe that existing methods still suffer from two typical challenges: (i) The intra‐class feature variation between different scenes may be large, leading to the difficulty in maintaining the consistency between same‐class pixels from different scenes; (ii) The inter‐class feature distinction in the same scene could be small, resulting in the limited performance to distinguish different classes in each scene. The authors first rethink semantic segmentation from a perspective of similarity between pixels and class centers. Each weight vector of the segmentation head represents its corresponding semantic class in the whole dataset, which can be regarded as the embedding of the class center. Thus, the pixel‐wise classification amounts to computing similarity in the final feature space between pixels and the class centers. Under this novel view, the authors propose a Class Center Similarity (CCS) layer to address the above‐mentioned challenges by generating adaptive class centers conditioned on each scenes and supervising the similarities between class centers. The CCS layer utilises the Adaptive Class Center Module to generate class centers conditioned on each scene, which adapt the large intra‐class variation between different scenes. Specially designed Class Distance Loss (CD Loss) is introduced to control both inter‐class and intra‐class distances based on the predicted center‐to‐center and pixel‐to‐center similarity. Finally, the CCS layer outputs the processed pixel‐to‐center similarity as the segmentation prediction. Extensive experiments demonstrate that our model performs favourably against the state‐of‐the‐art methods. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

126. Data Association Based Multi-target Tracking Using a Joint Formulation

Author

Xiang, Jun, Hou, Jianhua, Gao, Changxin, Sang, Nong, 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, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Lai, Shang-Hong, editor, Lepetit, Vincent, editor, Nishino, Ko, editor, and Sato, Yoichi, editor

Published
2017
Full Text
View/download PDF

127. Local Fractional Order Derivative Vector Quantization Pattern for Face Recognition

Author

Li, Jing, Sang, Nong, Gao, Changxin, 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, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Lai, Shang-Hong, editor, Lepetit, Vincent, editor, Nishino, Ko, editor, and Sato, Yoichi, editor

Published
2017
Full Text
View/download PDF

134. Multiple-Classifiers Based Hand Gesture Recognition

Author

Li, Simin, Ni, Zihan, Sang, Nong, Diniz Junqueira Barbosa, Simone, Series editor, Chen, Phoebe, Series editor, Du, Xiaoyong, Series editor, Filipe, Joaquim, Series editor, Kara, Orhun, Series editor, Kotenko, Igor, Series editor, Liu, Ting, Series editor, Sivalingam, Krishna M., Series editor, Washio, Takashi, Series editor, Tan, Tieniu, editor, Li, Xuelong, editor, Chen, Xilin, editor, Zhou, Jie, editor, Yang, Jian, editor, and Cheng, Hong, editor

Published
2016
Full Text
View/download PDF

135. Difficulty-Aware Dynamic Network for Lightweight Exposure Correction

Author

Li, Ziwen, Shao, Yuanjie, Zhang, Feng, Zhang, Jinpu, Wang, Yuehuan, and Sang, Nong

Abstract

Recently, deep learning-based methods have been successfully applied to the field of exposure correction. However, most of the existing methods treat different locations of an image in the same way, ignoring the inhomogeneous recovery difficulty and spatially-varying visual patterns in the image, which is sub-optimal and not perfectly efficient. In this paper, we propose a difficulty-aware dynamic network (DDNet) for lightweight exposure correction. Specifically, we propose a difficulty-aware strategy that determines the difficulty of feature patches according to a difficulty mask. Then, only the difficult patches are further refined instead of the whole features, which greatly reduces the overall computational complexity. Moreover, in order to achieve spatially-varying processing with a minimal computational burden, we design a spatial-aware dynamic convolution (SDConv), which is generated by predicting a set of basic kernels and a spatial-aware weight map. Benefiting from these designs, our method can strike a good trade-off between performance and complexity. Extensive experiments on several datasets demonstrate that our approach outperforms the state-of-the-art methods both qualitatively and quantitatively while requiring cheaper computational costs.

Published
2024
Full Text
View/download PDF

139. Multi-Centroid Representation Network for Domain Adaptive Person Re-ID

Author

Wu, Yuhang, Huang, Tengteng, Yao, Haotian, Zhang, Chi, Shao, Yuanjie, Han, Chuchu, Gao, Changxin, and Sang, Nong

Subjects
FOS: Computer and information sciences, ComputingMethodologies_PATTERNRECOGNITION, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, General Medicine
Abstract

Recently, many approaches tackle the Unsupervised Domain Adaptive person re-identification (UDA re-ID) problem through pseudo-label-based contrastive learning. During training, a uni-centroid representation is obtained by simply averaging all the instance features from a cluster with the same pseudo label. However, a cluster may contain images with different identities (label noises) due to the imperfect clustering results, which makes the uni-centroid representation inappropriate. In this paper, we present a novel Multi-Centroid Memory (MCM) to adaptively capture different identity information within the cluster. MCM can effectively alleviate the issue of label noises by selecting proper positive/negative centroids for the query image. Moreover, we further propose two strategies to improve the contrastive learning process. First, we present a Domain-Specific Contrastive Learning (DSCL) mechanism to fully explore intradomain information by comparing samples only from the same domain. Second, we propose Second-Order Nearest Interpolation (SONI) to obtain abundant and informative negative samples. We integrate MCM, DSCL, and SONI into a unified framework named Multi-Centroid Representation Network (MCRN). Extensive experiments demonstrate the superiority of MCRN over state-of-the-art approaches on multiple UDA re-ID tasks and fully unsupervised re-ID tasks., Accepted by AAAI2022

Published
2022

144. Mixture Models for Object Detection

Author

Kuang, Xiaoqin, Sang, Nong, Chen, Feifei, Gao, Changxin, Wang, Runmin, Diniz Junqueira Barbosa, Simone, Series editor, Chen, Phoebe, Series editor, Du, Xiaoyong, Series editor, Filipe, Joaquim, Series editor, Kara, Orhun, Series editor, Kotenko, Igor, Series editor, Liu, Ting, Series editor, Sivalingam, Krishna M., Series editor, Washio, Takashi, Series editor, Zha, Honbin, editor, Chen, Xilin, editor, Wang, Liang, editor, and Miao, Qiguang, editor

Published
2015
Full Text
View/download PDF

145. A Discriminant Sparse Representation Graph-Based Semi-Supervised Learning for Hyperspectral Image Classification

Author

Shao, Yuanjie, Gao, Changxin, Sang, Nong, Diniz Junqueira Barbosa, Simone, Series editor, Chen, Phoebe, Series editor, Du, Xiaoyong, Series editor, Filipe, Joaquim, Series editor, Kara, Orhun, Series editor, Kotenko, Igor, Series editor, Liu, Ting, Series editor, Sivalingam, Krishna M., Series editor, Washio, Takashi, Series editor, Zha, Honbin, editor, Chen, Xilin, editor, Wang, Liang, editor, and Miao, Qiguang, editor

Published
2015
Full Text
View/download PDF

146. Log-Gabor Weber Descriptor for Face Recognition

Author

Li, Jing, Sang, Nong, Gao, Changxin, 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, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Jawahar, C.V., editor, and Shan, Shiguang, editor

Published
2015
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results