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No-reference stereoscopic image quality assessment based on dilation convolution 34

No-reference stereoscopic image quality assessment based on ...

引用

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

作者： Zhao, Ping Li, Sumei Chang, Yongli Tianjin University School of Electrical and Information Engineering Tianjin China

ISBN: (纸本)9781728137230

Over the years, with the popularization of 3D technology, the demands of accurate and efficient 3D image quality evaluation (SIQA) methods are increasing constantly. Due to the wide application of CNN, CNN-based SIQA methods emerge one after another. However, current methods only consider a single scale or resolution, and some CNN-based methods directly take left and right views as an input of the network ignoring the visual fusion mechanism. In this work, a multi-scale no-reference SIQA method is proposed based on dilation convolution neural network (DCNN). Different from other CNN-based SIQA methods, the proposed one uses dilation convolution to imitate different scale of information processing fields in the human brain. Instead of left or right image, the cyclopean image generated by a new method is used as the input of the network. Moreover, the proposed multi-scale unit significantly can reduce computational parameters and computational complexity. Experimental results on two public databases show that the proposed model is superior to the state-of-The-Art no-reference SIQA methods. © 2019 IEEE.

关键词： Convolution

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image-Based End-To-End Neural Network for Dense Disparity Estimation 34

Image-Based End-To-End Neural Network for Dense Disparity Es...

引用

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

作者： Sun, Shuqiao Liu, Rongke Du, Qiuchen Sun, Shantong Kang, Shaoli Beihang University School of Electronic and Information Beijing China

ISBN: (纸本)9781728137230

Stereo matching is a challenging yet important task to various computer vision applications, e.g. 3D reconstruction, augmented reality, and autonomous vehicles. In this paper, we present a novel image-based convolutional neural network (CNN) for dense disparity estimation using stereo image pairs. In order to achieve precise and robust stereo matching, we introduce a feature extraction module that learns both local and global information. These features are then passed through an hour-glass structure to generate disparity maps from lower resolution to full resolution. We test the proposed method in several datasets including indoor scenes and synthetic scenes. Experimental results demonstrate that the proposed method outperforms the state-of-The-Art methods in several datasets. © 2019 IEEE.

关键词： Feature extraction

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Infrared-image processing for the DLR FireBIRD Mission 5th

Infrared-Image Processing for the DLR FireBIRD Mission

引用

5th Asian conference on Pattern Recognition,ACPR 2019

作者： Halle, Winfried Fischer, Christian Terzibaschian, Thomas Zell, Adina Reulke, Ralf DLR German Aerospace Center Institute of Optical Sensor Systems Berlin12489 Germany Computer Vision Humboldt-Universität zu Berlin Berlin Germany

ISBN: (纸本)9789811536502

The release of greenhouse gases and aerosols from fires has a large influence on global climate: on average, fires are responsible for up to 30% of anthropogenic CO:2 emissions. The German Aerospace Center (DLR) is operating the "FireBIRD" constellation, which consists of the two satellite missions TET-1 (Technology Test Platform), and BIROS (Bispectral Infrared Optical System) It is dedicated to scientific investigation of the issues involved as well as to early fire detection from space. The satellite and detector approach is based on proven DLR technology achieved during the BIRD (Bispectral Infrad Detection) Mission, which was launched in 2001 and was primarily used for observation of fires and volcanic activity until *** Payload of TET-1 and BIROS has spectral channels in visible (VIS), near infrared (NIR), mid wave (MIR) and a thermal infrared (TIR) channel. The paper is focused on the processing for TET- and BIROS- Fire- BIRD image data. In the FireBird standard processing chain level 1b and 2a data-products are generated automatically for all users after the data reception on ground. The so called fire-radiative-power (FRP) is one of the most important climate relevant parameters witch is estimated by using the bi-spectral method. Two characteristics of the FireBIRD sensors are unique: first, the high radiometric dynamic sensitivity for quantitative evaluation of normal temperatures and high temperature events (HTE) in the same scene. Second, the evaluation of the effective fire area in square meters independent of the recorded number of fire cluster sizes, which is given as the number of pixels per cluster. For certain users, such as firefighters, it is necessary to obtain fire data products (location and temperature) quickly and with minimal delay after detection. In such applications, data processing must take place directly on board the satellite without using a complex processing chain. The paper describes also an alternative fire-detection algorit

关键词： Neural networks

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Fpga based video transcoding system with 2k-4k super-resolution conversion 34

Fpga based video transcoding system with 2k-4k super-resolut...

引用

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

作者： Wei, Yuzhuo Chen, Li Xie, Rong Song, Li Zhang, Xiaoyun Gao, Zhiyong Shanghai Jiao Tong University Institute of Image Communication and Network Engineering Shanghai China

ISBN: (纸本)9781728137230

We present a FPGA-based system supporting video stream transcoding with 2k full high-definition (FHD) video to 4k ultra high-definition (UHD) video super-resolution(SR) conversion. Our system focuses on building a functional pipeline with convolutional neural network (CNN) accelerator and real-Time video codec unit for converting H.264 video stream to H.265/HEVC video stream. The overall video processing system can be used as an important plug-in module in the video streaming network to improve the video stream service quality. © 2019 IEEE.

关键词： Video streaming

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Unpaired image-To-Sketch Translation Network for Sketch Synthesis 34

Unpaired Image-To-Sketch Translation Network for Sketch Synt...

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

作者： Zhang, Yue Su, Guoyao Qi, Yonggang Yang, Jie Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781728137230

image-To-sketch translation is to learn the mapping between an image and a corresponding human drawn sketch. Machine can be trained to mimic the human drawing process using a training set of aligned image-sketch pairs. However, to collect such paired data is quite expensive or even unavailable for many cases since sketches exhibit various level of abstractness and drawing preferences. Hence we present an approach for learning an image-To-sketch translation network via unpaired examples. A translation network, which can translate the representation in image latent space to sketch domain, is trained in unsupervised setting. To prevent the problem of representation shifting in cross-domain translation, a novel cycle+ consistency loss is explored. Experimental results on sketch recognition and sketch-based image retrieval demonstrate the effectiveness of our approach. © 2019 IEEE.

关键词： Drawing (graphics)

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Medical images Segmentation Using a Novel Level Set Model with Laplace Kernel Function 8th

Medical Images Segmentation Using a Novel Level Set Model wi...

引用

8th International conference on communications, Signal processing, and Systems, CSPS 2019

作者： Song, Jianhua Zhang, Zhe Zhen, Jiaqi College of Electronic Engineering Heilongjiang University Harbin China College of Physics and Information Engineering Minnan Normal University Fujian China

ISBN: (纸本)9789811394089

Medical image segmentation is a complex study due to its disadvantages such as noise, low-contrast, intensity inhomogeneity, and so on. A novel level set model was proposed in this study to segment medical images accurately. The kernel function used to determine the size of neighborhood of central pixel was modified by Laplace kernel function, which is insensitive to the choice of parameters and is more suitable for segmenting medical images. Compared with several state-of-the-art models, both visual and objective experiments can demonstrate the performance and superiority of the novel level set model. © 2020, Springer Nature Singapore Pte Ltd.

关键词： image segmentation

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Improving Compression Artifact Reduction via End-to-End Learning of Side Information

Improving Compression Artifact Reduction via End-to-End Lear...

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IEEE visual communications and image processing (VCIP)

作者： Haichuan Ma 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

We propose to improve neural network-based compression artifact reduction by transmitting side information for the neural network. The side information consists of artifact descriptors that are obtained by analyzing the original and compressed images in the encoder. In the decoder, the received descriptors are used as additional input to a well-designed conditional post-processing neural network. To reduce the transmission overhead, the entire model is optimized under the rate-distortion constraint via end-to-end learning. Experimental results show that introducing the side information greatly improves the ability of the post-processing neural network, and improves the rate-distortion performance.

关键词： image coding Neural networks Decoding Training Feature extraction Computational modeling Transform coding

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From Low to Super Resolution and Beyond

From Low to Super Resolution and Beyond

引用

IEEE visual communications and image processing (VCIP)

作者： Chi-Wah Kok Wing-Shan Tam CVX Semiconductor Pty Ltd Adelaide South Australia Australia

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

The tutorial starts with an introduction of digital image interpolation, and single image super-resolution. It continues with the definition of various image interpolation performance measurement indices, including both objective and subjective indices. The core of this tutorial is the application of covariance based interpolation to achieve high visual quality image interpolation and single image super-resolution results. Layer on layer, the covariance based edge-directed image interpolation techniques that makes use of stochastic image model without explicit edge map, to iterative covariance correction based image interpolation. The edge based interpolation incorporated human visual system to achieve visually pleasant high resolution interpolation results. On each layer, the pros and cons of each image model and interpolation technique, solutions to alleviate the interpolation visual artifacts of each techniques, and innovative modification to overcome limitations of traditional edge-directed image interpolation techniques are presented in this tutorial, which includes: spatial adaptive pixel intensity estimation, pixel intensity correction, error propagation mitigation, covariance windows adaptation, and iterative covariance correction. The tutorial will extend from theoretical and analytical discussions to detail implementation using MATLAB. The audience shall be able to bring home with implementation details, as well as the performance and complexity of the interpolation algorithms discussed in this tutorial.

关键词： Interpolation Tutorials Matlab Digital images Australia image edge detection Consumer electronics

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NIR image colorization with graph-convolutional neural networks

NIR image colorization with graph-convolutional neural netwo...

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IEEE visual communications and image processing (VCIP)

作者： Diego Valsesia Giulia Fracastoro Enrico Magli Politecnico di Torino Italy

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

Colorization of near-infrared (NIR) images is a challenging problem due to the different material properties at the infared wavelenghts, thus reducing the correlation with visible images. In this paper, we study how graph-convolutional neural networks allow exploiting a more powerful inductive bias than standard CNNs, in the form of non-local self-similiarity. Its impact is evaluated by showing how training with mean squared error only as loss leads to poor results with a standard CNN, while the graph-convolutional network produces significantly sharper and more realistic colorizations.

关键词： Convolution conferences Standards Pattern recognition Computer vision Computer architecture Task analysis

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A Fungus Detection System for Greenhouses Using Wireless visual Sensor Networks and Machine Learning

A Fungus Detection System for Greenhouses Using Wireless Vis...

引用

IEEE Global communications conference (IEEE GLOBECOM)

作者： Ali, Asmaa Hassanein, Hossam S. Queens Univ Sch Comp Kingston ON Canada

ISBN: (纸本)9781728109602

Greenhouses are proliferating across Canada. Greenhouse crop production requires considerable attention. The only way to maintain the production growth is by controlling the greenhouse atmosphere and monitoring the plants so that they remain healthy in the greenhouse. In this paper, we utilize a Wireless visual Sensor Network (WVSN) with machine learning and image processing to observe any deficiency, pest, or disease presenting on the leaves of the plants. We distribute camera sensors throughout the greenhouse. Each camera sensor node captures an image from inside the greenhouse and use machine learning and image processing techniques to detect the presence of fungus. When a fungus is detected, the camera sensor node sends a message to the sensor node via the wireless sensor network to measure the humidity and then send a message to the actuator to re-set accordingly. This paper demonstrates how Hough forest machine learning and image processing can he successful in detecting fungus present on crop plant leaves from the images taken from camera sensors in the greenhouse. Cross-validation was applied to measure the performance of the system. The results are highly promising. There was a 94% success rate in detecting the fungus.

关键词： Wireless visual Sensor Network Machine Learning Hough Forest image processing

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