检索结果-内蒙古大学图书馆

IEEE International Conference on Multimedia and Expo (ICME)

作者： Wei Lu Wei Sun Zicheng Zhang Danyang Tu Xiongkuo Min Guangtao Zhai Institute of Image Communication and Network Engineering Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University

High frame rate (HFR) videos can provide consumers with a more immersive viewing experience in motion-rich scenes. However, they also pose a great challenge for video compression and transmission due to the increase in frame rates. Therefore, it is very important to choose proper frame rates and bit rates to achieve a trade-off between transmission bandwidth and visual quality. In this paper, we propose a novel Blind HFR Video Quality Assessment (BH-VQA) model by exploring the efficient and effective motion representation from the deep neural network (DNN). Concretely, we first train a baseline VQA model (i.e. a backbone network and a regressor) on a large-scale VQA database to derive a powerful quality-aware feature extractor for the spatial and motion feature extraction. Then, the HFR video is split into a sequence of video clips and the spatial features of each video clip are extracted just using the first frame of the video clip. To capture temporal distortions caused by frame rate variations and object and camera motion, we calculate deep structural similarities between continuous frames of each video clip as the motion features. Finally, the temporal quality dependencies between video clips are learned through a gated recurrent unit (GRU) network to obtain the perceptual video quality score. Experimental results show that BH-VQA achieves the best performance on two publicly available HFR VQA databases. The code of BH-VQA will be released.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Selective Bokeh Effect Transformation

Selective Bokeh Effect Transformation

引用

IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Juewen Peng Zhiyu Pan Chengxin Liu Xianrui Luo Huiqiang Sun Liao Shen Ke Xian Zhiguo Cao Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology S-Lab Nanyang Technological University

Bokeh effect transformation is a novel task in computer vision and computational photography. It aims to convert bokeh effects from one camera lens to another. To this end, we introduce a new concept of blur ratio, which represents the ratio of the blur amount of a target image to that of a source image, and propose a novel framework SBTNet based on this concept. For cat-eye simulation and lens type transformation, a two-channel coordinate map and a two-channel one-hot map are added as extra inputs. The core of the framework is a sequence of parallel FeaNets, along with a feature selection and integration strategy, which aims to transform the blur amount with arbitrary blur ratio. The effectiveness of the proposed framework is demonstrated through extensive experiments, and our solution has achieved the top LPIPS metric in NTIRE 2023 Bokeh Effect Transformation Challenge.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Object tracking in hyperspectral videos with convolutional features and kernelized correlation filter

arXiv

引用

arXiv 2018年

作者： Qian, Kun Zhou, Jun Xiong, Fengchao Zhou, Huixin Du, Juan Lab of Optoelectronic Imaging and Image Processing Xidian University Xi’an China School of Information and Communication Technology Griffith University Brisbane Australia College of Computer Science Zhejiang University Hangzhou China

Target tracking in hyperspectral videos is a new research topic. In this paper, a novel method based on convolutional network and Kernelized Correlation Filter (KCF) framework is presented for tracking objects of interest in hyperspectral videos. We extract a set of normalized three-dimensional cubes from the target region as fixed convolution filters which contain spectral information surrounding a target. The feature maps generated by convolutional operations are combined to form a three-dimensional representation of an object, thereby providing effective encoding of local spectral-spatial information. We show that a simple two-layer convolutional networks is sufficient to learn robust representations without the need of offline training with a large dataset. In the tracking step, KCF is adopted to distinguish targets from neighboring environment. Experimental results demonstrate that the proposed method performs well on sample hyperspectral videos, and outperforms several state-of-the-art methods tested on grayscale and color videos in the same scene. Copyright © 2018, The Authors. All rights reserved.

关键词： Target tracking

来源：评论

学校读者我要写书评

暂无评论

A Survey: Target Tracking Algorithm Based on Sparse Representation

A Survey: Target Tracking Algorithm Based on Sparse Represen...

引用

International Symposium on Computational Intelligence and Design, ISCID

作者： Dan Lu Linsheng Li Qingsen Yan Institute of Digital Media & Communication Taiyuan University of Science and Technology Taiyuan China Shaanxi Key Laboratory of Speech & Image Information Processing School of Computer Science Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9781479970063

In this paper, we introduce the development of object tracking. In particular, we introduce several kinds of target tracking algorithm based on sparse coding, including a robust visual tracking method by casting tracking as a sparse approximation problem in a particle filter framework, kernel sparse tracking with compressive sensing, and real-time compressive tracking. Show the concept of sparse representation and compressed sensing, analyze the meaning of the sparse representation in the target tracking, and compare the algorithm.

关键词： Target tracking Kernel Robustness Visualization image coding Pattern recognition Compressed sensing

来源：评论

学校读者我要写书评

暂无评论

Video-based human-object interaction detection from tubelet tokens 22

Video-based human-object interaction detection from tubelet ...

引用

Proceedings of the 36th International Conference on Neural Information processing Systems

作者： Danyang Tu Wei Sun Xiongkuo Min Guangtao Zhai Wei Shen Institute of Image Communication and Network Engineering Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University

ISBN: (纸本)9781713871088

We present a novel vision Transformer, named TUTOR, which is able to learn tubelet tokens, served as highly-abstracted spatiotemporal representations, for video-based human-object interaction (V-HOI) detection. The tubelet tokens structurize videos by agglomerating and linking semantically-related patch tokens along spatial and temporal domains, which enjoy two benefits: 1) Compactness: each tubelet token is learned by a selective attention mechanism to reduce redundant spatial dependencies from others; 2) Expressiveness: each tubelet token is enabled to align with a semantic instance, i.e., an object or a human, across frames, thanks to agglomeration and linking. The effectiveness and efficiency of TUTOR are verified by extensive experiments. Results show our method outperforms existing works by large margins, with a relative mAP gain of 16.14% on VidHOI and a 2 points gain on CAD-120 as well as a 4 × speedup.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Visual comfort assessment of stereoscopic images with multiple salient objects

Visual comfort assessment of stereoscopic images with multip...

引用

IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

作者： Xiaonan Zhang Jun Zhou Jianyu Chen Xiaoqiang Guo Ya Zhang Xiao Gu Shanghai Key Laboratory of Digital Media Processing and Transmissions Shanghai China Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai China Academy of Broadcasting Science Beijing China

This paper focuses on discomfort assessment issue for S3D images with multiple salient objects, and proposes four kinds of visual features of S3D images that have potential correlations with visual discomfort, including disparity distribution features, disparity jump features, object distribution features as well as object width features. A stereoscopic image database is designed focusing on multiple salient object scenarios and prediction model is built using support vector regression based on the proposed features. Prediction performance evaluation result shows that the proper combinations of the proposed features can achieve effective visual discomfort prediction for these types of pictures, and also shows that the convergence adjustments induced by the disparity discrepancies among the salient objects do cause visual discomfort in these scenarios.

关键词： Visualization Stereo image processing Feature extraction Three-dimensional displays image databases Convergence

来源：评论

学校读者我要写书评

暂无评论

Selection of Cancer Biomarkers from Microarray Gene Expression Data Utilizing the Bi-Objective Optimization Method

Selection of Cancer Biomarkers from Microarray Gene Expressi...

引用

2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Jiang, Hanjing Lin, Jianqing Zhu, Huachao Huang, Yabing Huazhong University of Science and Technology Key Lab. of Image Information Processing and Intelligent Control of Education Ministry of China Institute of Artificial Intelligence School of Artificial Intelligence and Automation Wuhan430074 China Wuhan University School of Computer Science Wuhan430064 China Renmin Hospital of Wuhan University Department of Pathology Wuhan430060 China

ISBN: (纸本)9798350337488

Cancer-associated biomarker genes play an indispensable role in the intricate tapestry of cancer development and manifestation. The expression of biomarkers in different types of tumor cells has beneficial implications for shedding light on the development of various cancers, guiding clinical diagnosis, and treatment. Microarray technology enables the expression levels of thousands of genes in samples to be sequenced simultaneously. However, sparse and high-dimensional microarray data present a formidable challenge in identifying biomarker genes. This study presents EREF-NSGA2, a novel method for cancer biomarker selection from microarray data, employing a hybrid gene selection approach. Firstly, the combination of the wrapper and embedded gene selection methods is proposed to filter the microarray data, which efficiently decreases the search space of the algorithm. After that, the improved NSGA-II algorithm is used to search the genes subset obtained from the previous step to reach the optimal subset of cancer biomarker genes. The proposed EREF-NSGA2 is compared with other reported methods on six cancer benchmark gene expression datasets. A detailed biological analysis is performed to analyze the relationship between the selected genes and the cancer data sets they belong to. To summarize, EREF-NSGA2 proves its effectiveness in selecting a feature subset comprising the fewest genes while maintaining the highest classification accuracy. © 2023 IEEE.

关键词： Bi-objective optimization Cancer biomarker Gene selection NSGA-II

来源：评论

学校读者我要写书评

暂无评论

A LIGHTWEIGHT SALIENCY PREDICTION MODEL FOR OMNIDIRECTIONAL imageS

A LIGHTWEIGHT SALIENCY PREDICTION MODEL FOR OMNIDIRECTIONAL ...

引用

2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Zhu, Dandan Chen, Yongqing Zhao, Defang Min, Xiongkuo Zhou, Qiangqiang Yu, Shaobo Zhai, Guangtao Yang, Xiaokang MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China College of Information and Communication Hainan University China School of Software Engineering Tongji University China Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China School of Software Jiangxi Normal University China Information Technology Services East China Normal University China

ISBN: (纸本)9781665438643

At present, most high-performing saliency prediction models for omnidirectional images (ODIs) depend on deeper or wider convolutional neural networks (CNNs), benefiting from their superior feature representation capability but suffering from high computational costs. To address this issue, we propose a novel lightweight saliency prediction model to predict the eye fixations on ODIs. Specifically, our proposed model consists of three modules: a lightweight feature representation module, a supervised attention module, and a dynamic convolution aggregation module. Different from the existing saliency prediction models, our proposed model is the first to introduce the dynamic convolution into the saliency prediction and aggregate multiple parallel convolution kernels dynamically based on their attention. Such a dynamic convolution operation is not only computationally efficient (small kernel size), but also increases the feature representation capability since these convolution kernels are aggregated in a non-linear manner via attention. Experimental results on two benchmark datasets show that our model is lightweight and outperforms other state-of-the-art methods. © 2021 IEEE

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Possibilistic similarity based image classification

Possibilistic similarity based image classification

引用

2nd International Conference on Pattern Recognition Applications and Methods, ICPRAM 2013

作者： Alsahwa, B. Almouahed, S. Guériot, D. Solaiman, B. Image and Information Processing Dept. Telecom Bretagne Institut Mines-Télécom Brest France Lab-STICC UMR CNRS 3192 Laboratoire en sciences et Technologies de l'Information de la Communication et de la Connaissance Institut Mines-Télécom-Télécom Bretagne-UE Brest France

ISBN: (纸本)9789898565419

In this study, an approach for image classification based on possibilistic similarity is proposed. This approach, due to the use of possibilistic concepts, enables an important flexibility to integrate both contextual information and a priori knowledge. Possibility distributions are, first, obtained using a priori knowledge given in the form of learning areas delimitated by an expert. These areas serve for the estimation of the probability density functions of different thematic classes. The resulting probability density functions are then transformed into possibility distributions using Dubois-Prade's probability-possibility transformation. Several measures of similarity between classes were tested in order to improve the discrimination between classes. The classification is then performed based on the principle of possibilistic similarity. Synthetic and real images are used in order to evaluate the performances of the proposed model.

关键词： Probability density function

来源：评论

学校读者我要写书评

暂无评论

A method of pixel unmixing by classes based on the possibilistic similarity

A method of pixel unmixing by classes based on the possibili...

引用

3rd International Conference on Pattern Recognition Applications and Methods, ICPRAM 2014

作者： Alsahwa, B. Almouahed, S. Guériot, D. Solaiman, B. Image and Information Processing Dept. Telecom Bretagne Institut Mines-Télécom Brest France Lab-STICC UMR CNRS 3192 Laboratoire en Sciences et Technologies de L'information de la Communication et de la Connaissance Institut Mines-Télécom-Télécom Bretagne-UEB Brest France

ISBN: (纸本)9789897580185

In this paper, an approach for pixel unmixing based on possibilistic similarity is proposed. This approach uses possibility distributions to express both the expert's semantic knowledge (a priori knowledge) and the contextual information. Dubois-Prade's probability-possibility transformation is used to construct these possibility distributions starting from statistical information (learning areas delimitated by an expert for each thematic class in the analyzed scene) which serve, first, for the estimation of the probability density functions using the kernel density estimation. The pixel unmixing is then performed based on the possibilistic similarity between a local possibility distribution estimated around the considered pixel and the obtained possibility distributions representing the predefined thematic classes. The obtained similarity values are used in order to obtain the abundances of different classes in the considered pixel. Accuracy analysis of pixels unmixing demonstrates that the proposed approach represents an efficient estimator of their abundances of the predefined thematic classes and, in turn, higher classification accuracy is achieved. Synthetic images are used in order to evaluate the performances of the proposed approach. Copyright © 2014 SCITEPRESS.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：