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检索条件"任意字段=2014 IEEE Visual Communications and Image Processing Conference, VCIP 2014"

共 2409 条记录，以下是111-120 订阅

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MOTION-BASED DEPTH ESTIMATION FOR 2D-TO-3D VIDEO CONVERSION

MOTION-BASED DEPTH ESTIMATION FOR 2D-TO-3D VIDEO CONVERSION

引用

ieee International conference on visual communications and image processing (vcip)

作者： Wang, Ming-Jiun Chen, Chun-Fu Lee, Gwo Giun U.S. Army Research Laboratory MD USA

ISBN: (纸本)9781479902880

This paper presents a motion-based depth estimation algorithm for automatic 2D-to-3D video conversion algorithm by employing the co-occurrence matrix of motion vectors (MVCM). Video scenes possess distinct signatures of MVCM, which enables exploiting the corresponding motion-depth relation for depth generation. The subsequent motion-compensated depth updating scheme provides stable and comfort 3D visual quality as synthesized by depth-image-based rendering. The simulation results of several high-definition image sequences indicate that the proposed algorithm produces better and more reasonable depth than two motion-based depth estimation algorithms. With the adaptive depth estimation scheme using MVCM, the proposed 2D-to-3D video conversion algorithm can accommodate a great variety of visual contents. It thus provides an efficient and reliable solution towards the problem of automatic 3D video content creation.

关键词： Depth estimation motion estimation co-occurrence matrix depth-image-based-rendering(DIBR)

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Generative DNA: Representation Learning for DNA-based Approximate image Storage

Generative DNA: Representation Learning for DNA-based Approx...

引用

ieee International conference on visual communications and image processing (vcip) - visual communications in the Era of AI and Limited Resources

作者： Franzese, Giulio Yan, Yiqing Serra, Giuseppe D'Onofrio, Ivan Appuswamy, Raja Michiardi, Pietro EURECOM Dept Data Sci Biot France

ISBN: (纸本)9781728185514

Synthetic DNA has received much attention recently as a long-term archival medium alternative due to its high density and durability characteristics. However, most current work has primarily focused on using DNA as a precise storage medium. In this work, we take an alternate view of DNA. Using neural-network-based compression techniques, we transform images into a latent-space representation, which we then store on DNA. By doing so, we transform DNA into an approximate image storage medium, as images generated back from DNA are only approximate representations of the original images. Using several datasets, we investigate the storage benefits of approximation, and study the impact of DNA storage errors (substitutions, indels, bias) on the quality of approximation. In doing so, we demonstrate the feasibility and potential of viewing DNA as an approximate storage medium.

关键词： DNA storage representation learning approximate storage compression

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No-Reference Stereoscopic image Quality Assessment Considering Multi-loss Constraints

No-Reference Stereoscopic Image Quality Assessment Consideri...

引用

ieee International conference on visual communications and image processing (vcip)

作者： Han, Yongtian Li, Sumei Yue, Guanghui Chang, Yongli Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China Shenzhen Univ Sch Biomed Engn Hlth Sci Ctr Shenzhen Peoples R China

ISBN: (纸本)9781728180687

In this paper, a three-channel convolutional neural network (CNN) constrained by multiple loss functions is designed for stereoscopic image quality assessment (SIQA). Given that both monocular and binocular information are crucial for SIQA, we take the patches of left images, right images and difference images as the inputs of the three channels respectively. Since using the ground truth as the labels of image patches cannot accurately characterize their quality, we propose to individually label each image patch to preserve the quality difference among different regions and views. Moreover, the multi-loss structure is adopted in the proposed method to consider both local features and global features simultaneously, which can constrain the feature learning from multiple perspectives. And the additional adaptive loss weights make the multi-loss network more flexible and universal. The experimental results show that the proposed method is superior to other existing SIQA methods with state-of-the-art performance.

关键词： stereoscopic image quality assessment multi-loss proxy labels adaptive loss weights convolutional neural network

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Motion Estimation for Quanta image Sensors Using Spatio-Temporal Priors

Motion Estimation for Quanta Image Sensors Using Spatio-Temp...

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2024 conference on visual communications and image processing

作者： Fukawa, Hiroya Kurihara, Kosuke Maeda, Yoshihiro Sato, Shunichi Hamamoto, Takayuki Tokyo Univ Sci Tokyo 1258585 Japan Shibaura Inst Technol Tokyo 1358548 Japan

ISBN: (纸本)9798331529543;9798331529550

Quanta image sensors are a novel paradigm in image sensor technology. Their direct application to quanta image sensors-based imaging systems is challenging because a bit-plane image is a set of binary images. In this paper, we introduce spatiotemporal priors based on the intensity invariance and smoothness characteristics of the motion vector. Specifically, we model when the image sequences align with the correct motion vector, the spatiotemporal structure becomes more consistent. Moreover, the spatial smoothness prior is incorporated through the smoothing filtering of the evaluation metrics of motion vector candidates. The experimental results show that the proposed method is more effective than conventional methods.

关键词： motion estimation quanta image sensors bit-plane images spatio-temporal prior

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Rate Controllable Learned image Compression Based on RFL Model

Rate Controllable Learned Image Compression Based on RFL Mod...

引用

ieee International conference on visual communications and image processing (vcip)

作者： Zhang, Saiping Wang, Luge Mao, Xionghui Yang, Fuzheng Wan, Shuai Xidian Univ Sch Telecommun Engn Xian Peoples R China Northwestern Polytech Univ Sch Elect & Informat Xian Peoples R China

ISBN: (纸本)9781665475921

In this paper, we propose a rate controllable image compression framework, Rate Controllable Variational Autoencoder (RC-VAE), based on the Rate-Feature-Level (RFL) model established through our exploration on the correlation among target rates, image features and quantization levels. Considering that, when meeting the same target rate, different images should be quantized in different levels, we focus on jointly utilizing the target rate and the extracted features of the image to predict the corresponding quantization level and propose the RFL model. Combining the proposed RFL model with a Hyperprior Continuously Variable Rate (HCVR) image compression network, we further propose the RC-VAE. By controlling information loss in quantization process, the RC-VAE can work at the target rate. Experimental results have demonstrated that one single RC-VAE model can adapt to multiple target rates with higher rate control accuracy and better R-D performance compared with the stateof-the-art rate controllable image compression networks.

关键词： Deep image compression rate control variational autoencoder rate-distortion

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Dual-stream Self-attention Network for image Captioning

Dual-stream Self-attention Network for Image Captioning

引用

ieee International conference on visual communications and image processing (vcip)

作者： Wan, Boyang Jiang, Wenhui Fang, Yuming Wen, Wenying Liu, Hantao Jiangxi Univ Finance & Econ Nanchang Jiangxi Peoples R China Cardiff Univ Cardiff Wales

ISBN: (纸本)9781665475921

Self-attention based encoder-decoder models achieve dominant performance in image captioning. However, most existing image captioning models (ICMs) only focus on modeling the relation between spatial tokens, while channel-wise attention is neglected for getting visual representation. Considering that different channels of visual representation usually denote different visual objects, it may lead to poor performance in terms of object and attribute words in the captioning sentences generated by the ICMs. In this paper, we propose a novel dual-stream self-attention module (DSM) to alleviate the above issue. Specifically, we propose a parallel self-attention based module that simultaneously encodes visual information from the spatial and channel dimensions. Besides, to obtain channel-wise visual features effectively and efficiently, we introduce a group self-attention block with linear computational complexity. To validate the effectiveness of our model, we conduct extensive experiments on the standard IC benchmarks including MSCOCO and Flickr30k. Without bells and whistles, the proposed model performs new SOTAs containing 135.4 CIDEr score on MSCOCO and 70.8 CIDEr score on Flickr30k.

关键词： image Captioning Self-Attention Spatial Attention Channel Attention

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Text-to-image Generation via Semi-Supervised Training

Text-to-Image Generation via Semi-Supervised Training

引用

ieee International conference on visual communications and image processing (vcip)

作者： Ji, Zhongyi Wang, Wenmin Chen, Baoyang Han, Xiao Peking Univ Sch Elect & Comp Engn Shenzhen Grad Sch Shenzhen Peoples R China Macau Univ Sci & Technol Intemat Inst Next Generat Internet Macau Peoples R China

ISBN: (纸本)9781728180687

Synthesizing images from text is an important problem and has various applications. Most of the existing studies of text-to-image generation utilize supervised methods and rely on a fully-labeled dataset, but detailed and accurate descriptions of images are onerous to obtain. In this paper, we introduce a simple but effective semi-supervised approach that considers the feature of unlabeled images as " Pseudo Text Feature". Therefore, the unlabeled data can participate in the following training process. To achieve this, we design a Modality-invariant Semanticconsistent Module which aims to make the image feature and the text feature indistinguishable and maintain their semantic information. Extensive qualitative and quantitative experiments on MNIST and Oxford-102 flower datasets demonstrate the effectiveness of our semi-supervised method in comparison to supervised ones. We also show that the proposed method can be easily plugged into other visual generation models such as image translation and performs well.

关键词： image Synthesis Text-to-image Generation Semi-Supervised Learning Pseudo Feature

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Annotating Only at Definite Pixels: A Novel Weakly Supervised Semantic Segmentation Method for Sea Fog Recognition

Annotating Only at Definite Pixels: A Novel Weakly Supervise...

引用

ieee International conference on visual communications and image processing (vcip)

作者： Zhu, Xun Xu, Mengqiu Wu, Ming Zhang, Chuang Zhang, Bin Beijing Univ Posts & Telecommun Beijing Peoples R China

ISBN: (纸本)9781665475921

Sea fog recognition is a challenging and significant semantic segmentation task in remote sensing images. The fully supervised learning method relies on the pixel-level label, which is labor-intensive and time-consuming. Moreover, it is impossible to accurately annotate all pixels of the sea fog region due to the limited ability of the human eye to distinguish between low clouds and sea fog. In this paper, we propose a novel approach of point-based annotation for weakly supervised semantic segmentation with the auxiliary information of International Comprehensive Ocean-Atmosphere Data Set (ICOADS) visibility data. It only needs several definite points for both foreground and background, which significantly reduces the annotation cost of manpower. We conduct extensive experiments on Himawari-8 satellite remote sensing images to demonstrate the effectiveness of our annotation method. The mean intersection over union (mIoU) and overall recognition accuracy of our annotation method reach 82.72% and 95.18%, respectively. Compared with the fully supervised learning method, the accuracy and the recognition rate of sea fog area are improved with a maximum increase of 7.69% and 9.69%, respectively.

关键词： sea fog recognition weakly supervised learning point annotation semantic segmentation remote sensing image

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LOSSLESS PREDICTIVE CODING WITH BAYESIAN TREATMENT

LOSSLESS PREDICTIVE CODING WITH BAYESIAN TREATMENT

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ieee International conference on visual communications and image processing (vcip)

作者： Jing, Liu Yang, Xiaokang Zhai, Guangtao Chen, Li Sun, Xianghui Chen, Wanhong Zuo, Ying Shanghai Jiao Tong Univ Shanghai 200030 Peoples R China China Film Archive Beijing Peoples R China

ISBN: (纸本)9781479902880

Natural image statistics have been widely exploited for lossless predictive coding and other applications. However, traditional adaptive techniques always focus on the local consistency of training set regardless of what the predicted target looks like. We investigate the problem of introducing the model evidence of predicted target since self-similarity inherent in natural images gives some kind of prior information for the distribution of predicted result. The proposed Bayesian model integrated with both training evidence and target evidence takes full advantages of local structure as well as self-similarity. Experimental results demonstrate that the proposed context model achieves best results compared with the state-of-the-art lossless predictors.

关键词： lossless image predictive coding Bayesian method local consistency self-similarity

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MEASURING ORDERLINESS BASED ON SOCIAL FORCE MODEL IN COLLECTIVE MOTIONS

MEASURING ORDERLINESS BASED ON SOCIAL FORCE MODEL IN COLLECT...

引用

ieee International conference on visual communications and image processing (vcip)

作者： Bai, Yu Xu, Yi Yang, Xiaokang Yan, Qing Shanghai Jiao Tong Univ Shanghai Key Labs Digital Media Proc & Commun Inst Image Commun & Informat Proc Shanghai 200240 Peoples R China

ISBN: (纸本)9781479902880

Collective motions, one of the coordinated behaviors in crowd system, widely exist in nature. Orderliness characterizes how well an individual will move smoothly and consistently with his neighbors in collective motions. It is still an open problem in computer vision. In this paper, we propose an orderliness descriptor based on correlation of interactive social force between individuals. In order to include the force correlation between two individuals in a distance, we propose a Social Force Correlation Propagation algorithm to calculate orderliness of every individual effectively and efficiently. We validate the effectiveness of the proposed orderliness descriptor on synthetic simulation. Experimental results on challenging videos of real scene crowds demonstrate that orderliness descriptor can perceive motion with low smoothness and locate disorder.

关键词： orderliness collectiveness direction of social force-field Social Force Correlation Propagation(SFCP) algorithm

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