2019 IEEE Visual Communications and Image Processing (VCIP)最新文献_第3页

Image Segmentation Based Graph-Cut Approach to Fast Color Image Coding via Graph Fourier Transform 基于图像分割的图切图像傅里叶变换快速彩色图像编码方法

2019 IEEE Visual Communications and Image Processing (VCIP) Pub Date : 2019-12-01 DOI: 10.1109/VCIP47243.2019.8966021

Kaito Abiko, Kazunori Uruma, Mamoru Sugawara, S. Hangai, T. Hamamoto

{"title":"Image Segmentation Based Graph-Cut Approach to Fast Color Image Coding via Graph Fourier Transform","authors":"Kaito Abiko, Kazunori Uruma, Mamoru Sugawara, S. Hangai, T. Hamamoto","doi":"10.1109/VCIP47243.2019.8966021","DOIUrl":"https://doi.org/10.1109/VCIP47243.2019.8966021","url":null,"abstract":"Colorization-based image coding is a technique to compress chrominance information of an image using a colorization technique. The conventional algorithm applies graph Fourier transform to the colorization-based coding. In this algorithm, several pixels on the image are defined as vertices of the graph, and the chrominance values of that pixels are set as graph signals. Then, the graph signal corresponding to the several chrominance values on the image is transformed to the graph spectrum based on the graph Fourier transform, and the graph spectrum is compressed and stored. Because the stored graph spectrum gives the graph signal on the image based on the inverse graph Fourier transform in decoding phase, the color image is recovered from the luminance image and the several chrominance values corresponding to the graph signal. However, high calculation time is required to perform graph Fourier transform, and therefore, this paper proposes a fast graph Fourier transform to improve the conventional colorization-based image coding algorithm. In numerical examples, although the PSNR value is decreased 0.3 dB, the proposed algorithm is 16.8 times faster than the conventional method.","PeriodicalId":388109,"journal":{"name":"2019 IEEE Visual Communications and Image Processing (VCIP)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123975203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Facial Attention based Convolutional Neural Network for 2D+3D Facial Expression Recognition 基于卷积神经网络的二维+三维面部表情识别

2019 IEEE Visual Communications and Image Processing (VCIP) Pub Date : 2019-12-01 DOI: 10.1109/VCIP47243.2019.8965843

Yang Jiao, Yi Niu, Yuting Zhang, Fu Li, Chunbo Zou, Guangming Shi

引用次数: 8

Stronger Baseline for Vehicle Re-Identification in the Wild 野外车辆再识别的更强基线

2019 IEEE Visual Communications and Image Processing (VCIP) Pub Date : 2019-12-01 DOI: 10.1109/VCIP47243.2019.8965867

Chih-Chung Hsu, Cing-Hao Hung, Chih-Yu Jian, Yi-Xiu Zhuang

{"title":"Stronger Baseline for Vehicle Re-Identification in the Wild","authors":"Chih-Chung Hsu, Cing-Hao Hung, Chih-Yu Jian, Yi-Xiu Zhuang","doi":"10.1109/VCIP47243.2019.8965867","DOIUrl":"https://doi.org/10.1109/VCIP47243.2019.8965867","url":null,"abstract":"Recently, re-identification tasks in computer vision field draw attention. Vehicle re-identification can be used to find the suspect car (target) from a vast surveillance video dataset. One of the most critical issues in the vehicle re-identification task is how to learn the effective feature representation. In general, pairwise learning such as the contrastive and triplet loss functions is adopted to learn the discriminative feature based on the convolution neural network. A good backbone network will lead to a significant improvement in the car re-identification task. In this paper, a stronger baseline method is proposed to achieve a better feature representation ability. First, we integrate the shift-invariant convolutional neural network with ResNet backbone to enhance the consistency feature learning. Afterward, a multi-layer feature fusion module is proposed to incorporate the middle- and high-level features to further improve the performance of car re-identification. Experimental results demonstrated that the proposed stronger baseline method achieves state-of-the-art performance in terms of mean averaging precision.","PeriodicalId":388109,"journal":{"name":"2019 IEEE Visual Communications and Image Processing (VCIP)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127175435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Macropixel-constrained Collocated Position Search for Plenoptic Video Coding 基于宏像素约束的全视屏编码并置位置搜索

2019 IEEE Visual Communications and Image Processing (VCIP) Pub Date : 2019-12-01 DOI: 10.1109/VCIP47243.2019.8965931

Lingjun Li, Xin Jin

引用次数: 6

Multi-view high dynamic range reconstruction via gain estimation 基于增益估计的多视点高动态范围重建

2019 IEEE Visual Communications and Image Processing (VCIP) Pub Date : 2019-12-01 DOI: 10.1109/VCIP47243.2019.8965880

Firas Abedi, Qiong Liu, You Yang

引用次数: 3

An Improved Gaussian Mixture Model Based Hole-filling Algorithm Exploiting Depth Information 利用深度信息的改进高斯混合模型充填算法

2019 IEEE Visual Communications and Image Processing (VCIP) Pub Date : 2019-12-01 DOI: 10.1109/VCIP47243.2019.8965964

Tiantian Zhu, Pan Gao

{"title":"An Improved Gaussian Mixture Model Based Hole-filling Algorithm Exploiting Depth Information","authors":"Tiantian Zhu, Pan Gao","doi":"10.1109/VCIP47243.2019.8965964","DOIUrl":"https://doi.org/10.1109/VCIP47243.2019.8965964","url":null,"abstract":"Virtual views generation is of great significance in free viewpoint video (FVV) as it can avoid the need to transmit a large volume of video data. An important issue in generating virtual views is how to fill the holes caused by occlusion. Using the Gaussian mixture model (GMM) to generate the background reference image is a commonly used hole-filling method. However, GMM usually has poor performance for sequences with reciprocal motion. In this paper, we propose an improved GMM-based method. To avoid the foreground pixels misclassified as the background pixels, we use depth information to adjust the learning rate in GMM. Foreground pixel is given a smaller learning rate than the background. Further, a refined foreground depth correlation (FDC) algorithm is proposed, which generates the background frame by tracking the change of the foreground depth in the temporal direction. In contrast to existing algorithms, we use a sliding window to obtain multiple background reference frames. These reference frames are then fused together to generate a more accurate background frame. Finally, we adaptively choose the background pixel from the GMM and FDC for hole filling. The experimental results show that subjective gain can be achieved, and significant objective gain can be observed in reciprocal motion sequences.","PeriodicalId":388109,"journal":{"name":"2019 IEEE Visual Communications and Image Processing (VCIP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121409061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Wireless Cooperative Caching System 无线协同缓存系统

2019 IEEE Visual Communications and Image Processing (VCIP) Pub Date : 2019-12-01 DOI: 10.1109/VCIP47243.2019.8965684

Chaoyu Gu, Jian Xiong, Haonan Xie, Peng Cheng

引用次数: 1

Deep Feature Guided Image Retargeting 深度特征引导图像重定位

2019 IEEE Visual Communications and Image Processing (VCIP) Pub Date : 2019-12-01 DOI: 10.1109/VCIP47243.2019.8966008

Jinan Wu, Rong Xie, Li Song, Bo Liu

引用次数: 3

Modified Baseline for Light Field Stitching 修改基线光场拼接

2019 IEEE Visual Communications and Image Processing (VCIP) Pub Date : 2019-12-01 DOI: 10.1109/VCIP47243.2019.8965971

Yilei Chen, P. An, Xinpeng Huang, Chunli Meng, Qiang Wu

引用次数: 4

A Dataset and Evaluation Framework for Deep Learning Based Video Stabilization Systems 基于深度学习的视频稳定系统的数据集和评估框架

2019 IEEE Visual Communications and Image Processing (VCIP) Pub Date : 2019-12-01 DOI: 10.1109/VCIP47243.2019.8966057

Maria Silvia Ito, E. Izquierdo

{"title":"A Dataset and Evaluation Framework for Deep Learning Based Video Stabilization Systems","authors":"Maria Silvia Ito, E. Izquierdo","doi":"10.1109/VCIP47243.2019.8966057","DOIUrl":"https://doi.org/10.1109/VCIP47243.2019.8966057","url":null,"abstract":"Although traditional methods for video stabilization are time consuming and prone to failure, more promising Deep Learning based solutions have not been thoroughly studied yet. This is mostly due to the lack of suitable training and testing datasets. To address this problem, this paper introduces a comprehensive dataset for training and assessing techniques for video stabilization. It consists of many shaky video sequences, their stable videos and the respective motion parameters that map each frame of the stable video into the corresponding frame in the unstable video. An important aspect of the dataset is the availability of motion parameters. This critical feature enables better assessment of any video stabilization technique, since it allows for additional comparison of the estimated motion parameters with the provided Ground Truth motion parameters. Using this dataset, an evaluation framework for video stabilization technology is also introduced. To demonstrate the practical use of the introduced dataset and the evaluation framework, we compare the performance of two state of the art techniques for video stabilization. The results of this extensive evaluation are presented and both the database and the evaluation framework are also provided in this paper.","PeriodicalId":388109,"journal":{"name":"2019 IEEE Visual Communications and Image Processing (VCIP)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133533932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7