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Robust visual Tracking Via An Imbalance-Elimination Mechanism

Robust Visual Tracking Via An Imbalance-Elimination Mechanis...

引用

IEEE visual communications and image processing (VCIP)

作者： Jin Feng Kaili Zhao Xiaolin Song Anxin Li Honggang Zhang Beijing University of Posts and Telecommunications Beijing China DOCOMO Beijing Labs China

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

The competitive performances in visual tracking are achieved mostly by tracking-by-detection based approaches, whose accuracy highly relies on a binary classifier that distinguishes targets from distractors in a set of candidates. However, severe class imbalance, with few positives (e.g., targets) relative to negatives (e.g., backgrounds), leads to degrade accuracy of classification or increase bias of tracking. In this paper, we propose an imbalance-elimination mechanism, which adopts a multi-class paradigm and utilizes a novel candidate generation strategy. Specifically, our multi-class model assigns samples into one positive class and four proposed negative classes, naturally alleviating class imbalance. We define negative classes by introducing proportions of targets in samples, which values explicitly reveal relative scales between targets and backgrounds. Further-more, during candidate generation, we exploit such scale-aware negative patterns to help adjust searching areas of candidates to incorporate larger target proportions, thus more accurate target candidates are obtained and more positive samples are included to ease class imbalance simultaneously. Extensive experiments on standard benchmarks show that our tracker achieves favorable performance against the state-of-the-art approaches, and offers robust discrimination of positive targets and negative patterns.

关键词： visualization Target tracking visual communication image processing Predictive models Prediction algorithms Standards

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Optical Flow Estimation Between images of Different Resolutions via Variational Method

Optical Flow Estimation Between Images of Different Resoluti...

引用

IEEE visual communications and image processing (VCIP)

作者： Rui Zhao Ruiqin Xiong Shuyuan Zhu Bing Zeng Tiejun Huang Wen Gao Peking University Beijing China University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

Traditional optical flow estimation methods mostly focus on images of the same resolution. However, there are some situations requiring optical flow between images of different resolutions, where the traditional approaches suffer from the inequality of spectrum aliasing level. In this paper, we propose a method estimating the flow fields between a clear image and a highly undersampled one. The proposed method simultaneously describes the motion and integral relationship between the images via an integral form image under the assumption of brightness and gradient consistency as well as motion smoothness. We also derive the numerical solution briefly, through which we can solve the equations easily via linearizations. Experimental results on Middlebury and MPI-Sintel datasets demonstrate that our proposed method outperforms traditional methods preprocessing images of different resolutions to be the same size, offering more accurate results.

关键词： Optical imaging image resolution Estimation Task analysis Nonlinear optics Mathematical model Lighting

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Research on Multi-focus image Fusion processing Algorithm

Research on Multi-focus Image Fusion Processing Algorithm

引用

Chinese Control and Decision conference, CCDC

作者： Tongying LI Hongbo ZHU Nantong Polytechnic College Nantong China Key Lab on Wideband Wireless Communications Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158563

When two or more objects in the scene are at different distances from the camera, the image obtained cannot be in focus everywhere. Usually many images are taken at different focus settings. By fusing images with different focus point, and image that is in focus everywhere can be obtained. This paper uses wavelet transform theory to deal with the fusion of the multi-focuses images and implements it using computer simulation. It can be analyzed from the experimentation results that each object is clear after the fusion and the fused image is more suitable for human visual perception and computer processing.

关键词： image fusion Matlab Discrete wavelet transforms

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GRNet: Deep Convolutional Neural Networks based on Graph Reasoning for Semantic Segmentation

GRNet: Deep Convolutional Neural Networks based on Graph Rea...

引用

IEEE visual communications and image processing (VCIP)

作者： Yang Wu Aimin Jiang Yibin Tang Hon Keung Kwan College of Internet of Things Hohai University University of Windsor

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

In this paper, we develop a novel deep-network architecture for semantic segmentation. In contrast to previous work that widely uses dilated convolutions, we employ the original ResNet as the backbone, and a multi-scale feature fusion module (MFFM) is introduced to extract long-range contextual information and upsample feature maps. Then, a graph reasoning module (GRM) based on graph-convolutional network (GCN) is developed to aggregate semantic information. Our graph reasoning network (GRNet) extracts global contexts of input features by modeling graph reasoning in a single framework. Experimental results demonstrate that our approach provides substantial benefits over a strong baseline and achieves superior segmentation performance on two benchmark datasets.

关键词： Semantics Convolution Cognition image segmentation Feature extraction Training Network architecture

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processing Color images, Brightness and Color Conversion

Processing Color Images, Brightness and Color Conversion

引用

2019 International conference on Information Science and communications Technologies, ICISCT 2019

作者： Beknazarova, Saida Safibullaevna Mukhamadiyev, Abduvali Shukurovich Kizi, Jaumitbayeva Mehriban Karamtdin Tashkent University of Information Technologies Named by Muhammad Al-Khwarizmi Dept. Audiovisual Technologies Tashkent Uzbekistan

ISBN: (纸本)9781728125640

The use of color in image processing is due to two main reasons. Firstly, color is an important feature that often facilitates recognition and selection of an object in an image. Secondly, a person is able to distinguish between thousands of different shades of color and only about two dozen shades of gray. The second circumstance is especially important when visual (that is, performed directly by man) image *** of color images can divided into two main areas: image processing in natural colors and image processing in pseudo-colors. In the first case, the images in question are usually formed by color image recording devices, such as a color television camera or a color scanner. In the second case, the task is to assign colors to some intensity values of a monochrome signal or to some ranges of changes in its intensity. Until relatively recently, digital processing of color images was carried out mostly at the level of pseudo colors. Over the past decade, however, color input devices and color image processing hardware have become quite affordable. As a result, natural image processing technology currently used in a wide range of applications, including publishing systems, visualization systems, and the Internet. It will be clear from the subsequent discussions that some halftone processing techniques developed in previous chapters are directly applicable to color images. Other methods should be reformulated and consistent with the properties of the color space, which constructed later in this chapter. The methods described in this chapter are far from exhaustive;they illustrate a variety of techniques applicable to color image processing. © 2019 IEEE.

关键词： Luminance

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Efficient Light Deep Network for Street Scene Parsing

Efficient Light Deep Network for Street Scene Parsing

引用

IEEE visual communications and image processing (VCIP)

作者： ZheHui Wang Sanyuan Zhao Jianbing Shen Zhengchao Lei Beijing Institute of Technology Coordination Center of China

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

The semantic segmentation is a dense pixel label pre-diction task, which takes quite a lot of resources and computation cost in most of the time. In our approach, we pay attention to balance the speed and better performance which outperforms the state of the art in speed and accuracy for real-time performance. We come up with the idea of new efficient deep backbone that can extract more semantic details, reduce the computation cost and be easy to deploy at the same time. We call our new backbone as Cascaded Mobile Network, which is proved to be very useful. Our proposed model achieves 72.1 mIOU on the CityScapes val, and 69.5 on CamVid. We achieve good balance between speed and accuracy.

关键词： Feature extraction Semantics Real-time systems image segmentation Knowledge engineering Convolution Training

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A Discrete Cosine Model of Light Field Sampling for Improving Rendering Quality of Views

A Discrete Cosine Model of Light Field Sampling for Improvin...

引用

IEEE visual communications and image processing (VCIP)

作者： Ying Wei Changjian Zhu You Yang Yan Liu School of Electron. Engineering Guangxi Normal Univ Guilin China School of Electron. and Inform. Comm Huazhong Univ. of Sci. and Tech Wuhan China

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

A number of theories have been proposed for reducing sampling rate of light field. But these theories still need a great many of samples (images) to obtain sufficient geometric information. In this paper, we utilize the sparse representation of light field in Discrete Cosine Transform domain to present a Discrete Cosine Sparse Basis (DCSB). Thus, we can find out the zeros of DCSB to reduce sampling requirement of light field for alias-free rendering. Finally, experimental results demonstrate the effectiveness of our approach without lose information.

关键词： image reconstruction Discrete cosine transforms Two dimensional displays Rendering (computer graphics) Graphics Art Three-dimensional displays

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Deep Learning-Based Nonlinear Transform for HEVC Intra Coding

Deep Learning-Based Nonlinear Transform for HEVC Intra Codin...

引用

IEEE visual communications and image processing (VCIP)

作者： Kun Yang Dong Liu Feng Wu CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei China

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

In the hybrid video coding framework, transform is adopted to exploit the dependency within the input signal. In this paper, we propose a deep learning-based nonlinear transform for intra coding. Specifically, we incorporate the directional information into the residual domain. Then, a convolutional neural network model is designed to achieve better decorrelation and energy compaction than the conventional discrete cosine transform. This work has two main contributions. First, we propose to use the intra prediction signal to reduce the directionality in the residual. Second, we present a novel loss function to characterize the efficiency of the transform during the training. To evaluate the compression performance of the proposed transform, we implement it into the High Efficiency Video Coding reference software. Experimental results demonstrate that the proposed method achieves up to 1.79% BD-rate reduction for natural videos.

关键词： Transforms Discrete cosine transforms Neural networks Transform coding Video coding image coding Decoding

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Reduced-Reference Stereoscopic image Quality Assessment Based on Entropy of Gradient Primitives

Reduced-Reference Stereoscopic Image Quality Assessment Base...

引用

IEEE International conference on Signal and image processing (ICSIP)

作者： Jian Ma Xinxin Zhao Youxin Xu School of Internet Anhui University School of Electronics and Communications Engineering Anhui xinhua University Marvell Technology Group Ltd.

ISBN: (数字)9781728168968

ISBN: (纸本)9781728168975

Stereoscopic image quality assessment (SIQA) has always been challenging due to the remarkable distinction between human monocular and binocular vision. This paper proposes a novel gradient-based dictionary learning method for SIQA, which effectively integrates the gradients are sparser than the image itself. Specifically, we first compute the gradient maps of each view image of stereopair by applying contrast sensitivity of human visual system (HVS) and neighborhood gradient information to weight the gradient magnitudes in a locally adaptive manner. Afterwards, the binocular perceptual information of gradient (GBPI) is represented by the distribution statistics of visual primitives in gradient maps of left and right views' images, which are extracted by sparse representation. Furthermore, the entropy of gradient maps of each views' images are utilized to represent monocular cue. Their mutual information is used to represent binocular cue. The difference of the reference and distorted images' GBPIs is taken as quality-ware features. Finally, the kernel ridge regressing (KRR) is utilized to simulate a nonlinear relationship between the quality-ware features and human opinions. The performance of the proposed metric is evaluated over the LIVE 3D phase II asymmetric datasets, and shown to be competitive with the state-of-the-art SIQA algorithms.

关键词： image quality visualization Three-dimensional displays Stereo image processing Nonlinear distortion visual systems Entropy

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News image Steganography: A Novel Architecture Facilitates the Fake News Identification

News Image Steganography: A Novel Architecture Facilitates t...

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IEEE visual communications and image processing (VCIP)

作者： Jizhe Zhou Chi-Man Pun Yu Tong University of Macao Macao China vivo AI Lab vivo Mobile Communication Co. Ltd ShenZhen China

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

A larger portion of fake news quotes untampered images from other sources with ulterior motives rather than conducting image forgery. Such elaborate engraftments keep the inconsistency between images and text reports stealthy, thereby, palm off the spurious for the genuine. This paper proposes an architecture named News image Steganography (NIS) to reveal the aforementioned inconsistency through image steganography based on GAN. Extractive summarization about a news image is generated based on its source texts, and a learned steganographic algorithm encodes and decodes the summarization of the image in a manner that approaches perceptual invisibility. Once an encoded image is quoted, its source summarization can be decoded and further presented as the ground truth to verify the quoting news. The pairwise encoder and decoder endow images of the capability to carry along their imperceptible summarization. Our NIS reveals the underlying inconsistency, thereby, according to our experiments and investigations, contributes to the identification accuracy of fake news that engrafts untampered images.

关键词： Decoding Training Perturbation methods Gallium nitride Forgery Printers Media

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