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DCM: A Dense-Attention Context Module For Semantic Segmentation

DCM: A Dense-Attention Context Module For Semantic Segmentat...

引用

IEEE International conference on image processing

作者： Shenghua Li Quan Zhou Jia Liu Jie Wang Yawen Fan Xiaofu Wu Longin Jan Latecki National Engineering Research Center of Communications and Networking Nanjing University of Posts & Telecommunications P.R. China Department of Computer and Information Sciences Temple University Philadelphia USA

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

For image semantic segmentation, a fully convolutional network is usually employed as the encoder to abstract visual features of the input image. A meticulously designed decoder is used to decoding the final feature map of the backbone. The output resolution of backbones which are designed for image classification task is too low to match segmentation task. Most existing methods for obtaining the final high-resolution feature map can not fully utilize the information of different layers of the backbone. To adequately extract the information of a single layer, the multi-scale context information of different layers, and the global information of backbone, we present a new attention-augmented module named Dense-attention Context Module (DCM), which is used to connect the common backbones and the other decoding heads. The experiments show the promising results of our method on Cityscapes dataset.

关键词： Convolution Feature extraction Semantics Data mining image segmentation Kernel Decoding

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An Improved Fuzzy Algorithmic Approach Applying on Medical image to Improve the Contrast

An Improved Fuzzy Algorithmic Approach Applying on Medical I...

引用

International Wireless communications and Mobile Computing conference, IWCMC

作者： Min Wu Mathematics and Computer Institute Guangdong Ocean University Zhanjiang China

ISBN: (数字)9781728131290

ISBN: (纸本)9781728131306

Noise led from nodes of medical imaging system and underexposure or overexposure will make the image be in a low contrast. In this paper we try to enhance the image contrast with a method of digital image processing. We present an improved fuzzy algorithmic approach on the basis of the conventional Pas. S. K algorithm. Processed by this improved algorithm, the imperceptible information can be avoided to lose when the planar space of original image is transformed to the fuzzy space. And special experiment has been carried out to demonstrate the effect of this approach applying on the X-ray roentgenoscopy image of animal carotid, which is obtained by a homemade instrument and convert to digital signal with an image-collecting card. We find that both images processed with the conventional algorithm and the improved algorithm can provide more details than the original one. While percent of the zero gray pixels of the image processed with the conventional algorithm is much more (to reach 12.3%) than that with the improved algorithm (6.2%) by analyzing with visual C++ image process program, indicating a more loss of information caused by the forcible process of making some image elements equal to zero. Thus it can be concluded that the improved algorithm seems to be more distinct than the conventional one.

关键词： Biomedical imaging image enhancement image quality Histograms Biomedical image processing Fuzzy sets Manganese

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Learning Matching Behavior Differences for Compressing Vehicle Re-identification Models

Learning Matching Behavior Differences for Compressing Vehic...

引用

IEEE visual communications and image processing (VCIP)

作者： Yi Xie Jianqing Zhu Huanqiang Zeng Canhui Cai Lixin Zheng College of Engineering Huaqiao University China

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

Vehicle re-identification matching vehicles captured by different cameras has great potential in the field of public security. However, recent vehicle re-identification approaches exploit complex networks, causing large computations in their testing phases. In this paper, we propose a matching behavior difference learning (MBDL) method to compress vehicle re-identification models for saving testing computations. In order to represent the matching behavior evolution across two different layers of a deep network, a matching behavior difference (MBD) matrix is designed. Then, our MBDL method minimizes the L1 loss function among MBD matrixes from a small student network and a complex teacher network, ensuring the student network use less computations to simulate the teacher network's matching behaviors. During the testing phase, only the small student network is utilized so that testing computations can be significantly reduced. Experiments on VeRi776 and VehicleID datasets show that MBDL outperforms many state-of-the-art approaches in terms of accuracy and testing time performance.

关键词： Training Testing Probes image coding Loss measurement Computational modeling Trajectory

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Improving Person Re-Identification Performance Using Body Mask Via Cross-Learning Strategy 34

Improving Person Re-Identification Performance Using Body Ma...

引用

34th IEEE International conference on visual communications and image processing, VCIP 2019

作者： Wu, Junyi Yao, Lingxiang Huang, Yan Xu, Jingsong Wu, Qiang Huang, Liqin Fuzhou University College of Physics and Information Fuzhou China University of Technology Sydney School of Electrical and Data Engineering Sydney Australia

ISBN: (纸本)9781728137230

The task of person re-identification (re-id) is to find the same pedestrian across non-overlapping cameras. Normally, the performance of person re-id can be affected by background clutters. However, existing segmentation algorithms are hard to obtain perfect foreground person images. To effectively leverage the body (foreground) cue, and in the meantime pay attention to discriminative information in the background (e.g., companion or vehicle), we propose to use a cross-learning strategy to take both foreground and other discriminative information into account. In addition, since currently existing foreground segmentation result always involves noise, we use Label Smoothing Regularization (LSR) to strengthen the generalization capability during our learning process. In experiments, we pick up two state-of-The-Art person re-id methods to verify the effectiveness of our proposed cross-learning strategy. Our experiments are carried out on two publicly available person re-id datasets. Obvious performance improvements can be observed on both datasets. © 2019 IEEE.

关键词： image segmentation

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FastSCCNet: Fast Mode Decision in VVC Screen Content Coding via Fully Convolutional Network

FastSCCNet: Fast Mode Decision in VVC Screen Content Coding ...

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IEEE visual communications and image processing (VCIP)

作者： Sik-Ho Tsang Ngai-Wing Kwong Yui-Lam Chan The Hong Kong Polytechnic University Kowloon Hong Kong SAR

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

Screen content coding have been supported recently in Versatile Video Coding (VVC) to improve the coding efficiency of screen content videos by adopting new coding modes which are dedicated to screen content video compression. Two new coding modes called Intra Block Copy (IBC) and Palette (PLT) are introduced. However, the flexible quad-tree plus multi-type tree (QTMT) coding structure for coding unit (CU) partitioning in VVC makes the fast algorithm of the SCC particularly challenging. To efficiently reduce the computational complexity of SCC in VVC, we propose a deep learning based fast prediction network, namely FastSCCNet, where a fully convolutional network (FCN) is designed. CUs are classified into natural content block (NCB) and screen content block (SCB). With the use of FCN, only one shot inference is needed to classify the block types of the current CU and all corresponding sub-CUs. After block classification, different subsets of coding modes are assigned according to the block type, to accelerate the encoding process. Compared with the conventional SCC in VVC, our proposed FastSCCNet reduced the encoding time by 29.88% on average, with negligible bitrate increase under all-intra configuration. To the best of our knowledge, it is the first approach to tackle the computational complexity reduction for SCC in VVC.

关键词： Encoding Convolutional codes image color analysis Computational complexity Tools image segmentation Copper

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No-Reference Objective Quality Assessment Method of Display Products

No-Reference Objective Quality Assessment Method of Display ...

引用

IEEE visual communications and image processing (VCIP)

作者： Huiqing Zhang Donghao Li Lifang Wu Zhifang Xia Faculty of Information Technology Beijing University of Technology Beijing China Key Laboratory of Artificial Intelligence Ministry of Education Shanghai China Engineering Research Center of Digital Community Ministry of Education Beijing China

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

Recent years have witnessed the spread of electronic devices especially the mobile phones, which have become almost the necessities in people's daily lives. An effective and efficient technique for blindly assessing the quality of display products is greatly helpful to improve the experiences of users, such as displaying the pictures or texts in a more comfortable manner. In this paper, we put forward a novel no-reference (NR) quality metric of display products, dubbed as NQMDP. First, we have established a new subjective photo quality database, in which 50 photos shown on three different types of display products were captured to constitute a total of 150 photos and then scored by more than 40 inexperienced observers. Second, 19 effective image features are extracted by using six different influencing factors (including complexity, contrast, sharpness, brightness, colorfulness and naturalness) on the quality of display products and then were learned with the support vector regressor (SVR) to estimate the objective quality score of each photo. Results of experiments show that our proposed method has obtained better performance than the state-of-the-art algorithms.

关键词： image color analysis Observers Feature extraction Brightness Databases visualization Complexity theory

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Deep learning based in-loop filter for video coding 34

Deep learning based in-loop filter for video coding

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34th IEEE International conference on visual communications and image processing, VCIP 2019

作者： Yu, Yue Yang, Xiuzhi Chen, Jian Huang, Bo Wu, Junyi Fuzhou University IMCL College of Physics and Information Engineering Fuzhou China

ISBN: (纸本)9781728137230

In lossy video coding, in-loop filter can improve the image reconstruction by reducing compression artifacts and distortions. Recently, several CNN-based in-loop filtering algorithms are proposed to improve HEVC. However, something deficient is still limiting their capability to further boost the coding efficiency. Specifically, things like prediction residuals and coding-unit boundaries, which is quite relevant to the compression artifacts, were usually neglected;moreover, the loss functions adopted in these methods usually haven't been regularized properly. To improve these inadequacies, we propose a deep learning based in-loop filter for HEVC to improve its rate-distortion performance. Firstly, prediction residuals, the compression of which is actually where the encoding noise directly results from, are skillfully considered to help filtering. Then, we unify a block-based measurement-reconstruction process and a neighborhood-based filtering process into one end-To-end all-convolutional architecture to catch the relevance between compression noise and the pixel's related position to the coding block boundaries. Finally, for network training, total-variation model is adopted to derive the loss function by MAP (maximum a posteriori) estimation for better regularization. Experimentally, our proposed in-loop filtering method brings average 6.5% and 4.3% BD-rate reduction under AI and RA configuration respectively. © 2019 IEEE.

关键词： image coding

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Information-theoretic non-uniformity correction of infrared image sequences

Information-theoretic non-uniformity correction of infrared ...

引用

IEEE Signal processing and communications Applications (SIU)

作者： Fatih Nar Atilla Özgür Ankara Yildirim Beyazit University Ankara Turkey Mathematics and Logistics Jacobs University Bremen Germany

ISBN: (数字)9781728172064

ISBN: (纸本)9781728172071

In infrared (IR) image sequences, non-uniform imaging artifacts can appear due to sensor characteristics and operating temperatures. These non-uniformity artifacts degrade the performance of scene analysis algorithms such as target detection, target recognition, and target tracking as well as human expert visual analysis. In this study, we propose a software based non-uniformity correction (NUC) approach based on optimization of a mutual information (MI) driven cost function. Proposed method, namely information-theoretic NUC (IT-NUC), has two steps. First, shutter is closed for a short period of time and non-uniform pattern is extracted using the acquired image sequence. This first step is done for once and only repeated when camera needs to be calibrated. In the second step, extracted pattern is removed from image sequences that are acquired while shutter is opened. This step is executed in an online manner via an optimization approach. Analysis show that proposed method provides satisfactory NUC capabilities using commodity hardware in real-time.

关键词： Software algorithms image sequences Photonics Cost function Cameras Software Mutual information

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Chain Code-Based Occupancy Map Coding for Video-Based Point Cloud Compression

Chain Code-Based Occupancy Map Coding for Video-Based Point ...

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IEEE visual communications and image processing (VCIP)

作者： Runyu Yang Ning Yan Li Li 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 video-based point cloud compression (V-PCC), occupancy map video is utilized to indicate whether a 2-D pixel corresponds to a valid 3-D point or not. In the current design of V-PCC, the occupancy map video is directly compressed losslessly with High Efficiency Video Coding (HEVC). However, the coding tools in HEVC are specifically designed for natural images, thus unsuitable for the occupancy map. In this paper, we present a novel quadtree-based scheme for lossless occupancy map coding. In this scheme, the occupancy map is firstly divided into several coding tree units (CTUs). Then, the CTU is divided into coding units (CUs) recursively using a quadtree. The quadtree partition is terminated when one of the three conditions is satisfied. Firstly, all the pixels have the same value. Secondly, the pixels in the CU only have two kinds of values and they can be separated by a continuous edge whose endpoints lie on the side of the CU. The continuous edge is then coded using chain code. Thirdly, the CU reaches the minimum size. This scheme simplifies the design of block partitioning in HEVC and designs simpler yet more effective coding tools. Experimental results show significant reduction of bit-rate and complexity compared with the occupancy map coding scheme in V-PCC. In addition, this scheme is also very efficient to compress the semantic map.

关键词： Encoding Three-dimensional displays image coding Semantics Markov processes Geometry Copper

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U-Net with Graph Based Smoothing Regularizer for Small Vessel Segmentation on Fundus image 1

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26th International conference on Neural Information processing (ICONIP) of the Asia-Pacific-Neural-Network-Society (APNNS)

作者： Hakim, Lukman Yudistira, Novanto Kavitha, Muthusubash Kurita, Takio Hiroshima Univ Dept Informat Engn 1-4-1 Kagamiyama Higashihiroshima 7398527 Japan

ISBN: (数字)9783030368029

ISBN: (纸本)9783030368029;9783030368012

The detection of retinal blood vessels, especially the changes of small vessel condition is the most important indicator to identify the vascular network of the human body. Existing techniques focused mainly on shape of the large vessels, which is not appropriate for the disconnected small and isolated vessels. Paying attention to the low contrast small blood vessel in fundus region, first time we proposed to combine graph based smoothing regularizer with the loss function in the U-net framework. The proposed regularizer treated the image as two graphs by calculating the graph laplacians on vessel regions and the background regions on the image. The potential of the proposed graph based smoothing regularizer in reconstructing small vessel is compared over the classical U-net with or without regularizer. Numerical and visual results shows that our developed regularizer proved its effectiveness in segmenting the small vessels and reconnecting the fragmented retinal blood vessels.

关键词： Retinal blood vessel Graph based smoothing Regularizer Graph laplacians

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