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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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Progressive semantic image synthesis via generative adversarial network 34

Progressive semantic image synthesis via generative adversar...

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

作者： Yue, Ke Li, Yidong Li, Huifang Beijing Jiaotong University School of Computer and Information Technology Beijing100044 China

ISBN: (纸本)9781728137230

Semantic image synthesis via text description is a desirable and challenging task, which requires more protection of the text irrelevant content in the original image. Existing methods directly modify the original image, which become more difficult when encountering high resolution image, and the generated images are also blurred and lack in detail. This paper presents a novel network architecture to progressively manipulate an image starting from low-resolution, while introducing the original image of corresponding size at different stages with our proposed union module to avoid losing of detail. And the progressive design of the network allows us to modify the image from coarse into fine. Compared with the previous methods, our new method can successfully manipulate a high resolution image and generate a new image with background protection and fine details. The experimental results on CUB-200-2011 dataset show that the proposed approach outperforms existing methods in terms of image detail, background protection and high resolution generation. © 2019 IEEE.

关键词： Generative adversarial networks

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The Hough-Based Multibeamlet Transform

The Hough-Based Multibeamlet Transform

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IEEE visual communications and image processing (VCIP)

作者： Agnieszka Lisowska Institute of Computer Science University of Silesia Katowice Poland

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

There are plenty of geometrical multiresolution transforms devoted to efficient edge representation. However, they have two drawbacks. The first one is that such transforms represent mono edge models. And the second one is that they are often based on approximations which are optimal according to the Mean Square Error what does not necessarily lead to optimal edge approximation. In this paper the multibeamlet transform based on the Hough transform is proposed. This transform is defined to properly detect multiedges present in images. Next, the method of image approximation with the use of the multibeamlet transform is described. Additionally, the modified bottom-up tree pruning algorithm is presented in order to properly approximate images with the use of multibeamlets. As follows from the performed experiments, this approach leads to image approximations with better quality than the state-of-the-art geometrical multiresolution transforms.

关键词： Transforms image edge detection Approximation algorithms image resolution image color analysis PSNR MONOS devices

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image segmentation based graph-cut approach to fast color image coding via graph fourier transform 34

Image segmentation based graph-cut approach to fast color im...

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

作者： Abiko, Kaito Uruma, Kazuniri Sugawara, Mamoru Hangai, Seiichiro Hamamoto, Takayuki Tokyo University of Science Tokyo Japan Kogakuin University Tokyo Japan

ISBN: (纸本)9781728137230

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. © 2019 IEEE.

关键词： Pixels

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Deep Learning Based EBCOT Source Symbol Prediction Technique for JPEG2000 image Compression Architecture

Deep Learning Based EBCOT Source Symbol Prediction Technique...

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IEEE visual communications and image processing (VCIP)

作者： I-Hsiang Wang Jian-Jiun Ding Graduate Institute of Communication Engineering National Taiwan University Taipei Taiwan

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

In this work, an efficient and robust learning-based JPEG2000 architecture is proposed. It uses machine learning techniques for predicting and encoding the decision bit in the embedded block coding with optimized truncation (EBCOT) process. First, we apply non-locally weighted ridge regression to predict the quantized wavelet coefficients in the LL subband. Then, during the EBCOT process, we perform inter/intra subband prediction and inter/intra bit plane symbol prediction to estimate the activity of the decision bit using the deep learning architecture. Then, the binary prediction result is treated as an additional context and the decision bit is eventually coded using an advanced context-based adaptive binary arithmetic coder. Simulations show that the proposed framework provides the same visual quality as conventional codecs with as much as 30% bitrate savings.

关键词： Transform coding image coding Feature extraction Deep learning Training Prediction algorithms Indexes

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HDR Deghosting Using Motion-Registration-Free Fusion in the Luminance Gradient Domain

HDR Deghosting Using Motion-Registration-Free Fusion in the ...

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IEEE visual communications and image processing (VCIP)

作者： Cheng-Yeh Liou Cheng-Yen Chuang Chia-Han Huang Yi-Chang Lu Graduate Institute of Electronics Engineering National Taiwan University Taipei Taiwan

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

For most of the existing high dynamic range (HDR) deghosting flows, they require a time-consuming motion registration step to generate ghost-free HDR results. Since the motion registration step usually becomes the bottleneck of the entire flow, in this paper, we propose a novel H DR deghosting flow which does not require any motion registration process. By taking channel properties into account, the luminance and chrominance channels are fused differently in the proposed flow. Our motion-registration-free fusion could generate high-quality HDR results swiftly even if the original Low Dynamic Range (LDR) images contain objects with large foreground motions.

关键词： image color analysis Dynamic range Transforms image sequences image reconstruction Graphics processing units Cameras

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Stereoscopic image Quality Assessment Weighted Guidance by Disparity Map Using Convolutional Neural Network 34

Stereoscopic Image Quality Assessment Weighted Guidance by D...

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

作者： Ding, Yixiu Li, Sumei Chang, Yongli Tianjin University Dept. Electrical and Information Engineering Tianjin China

ISBN: (纸本)9781728137230

In this paper, we propose a new two-column dense Convolutional Neural Network (CNN) for stereoscopic image quality assessment. The input of one column is the cyclopean image which conforms to the binocular combination and rival mechanism in our brain. The input of other column is the disparity map which provides some compensation information for the cyclopean image. More importantly, we employ the features of disparity map to guide and weight the feature maps obtained from the cyclopean image, which is implemented by modifying the structure of Squeeze and Excitation block. This weighting strategy recalibrates the importance of feature maps extracted from cyclopean image. At the end of CNN, we combine the outputs from the two-column through 'Concat', and then process them to get the final quality score of the stereoscopic image. Experimental results demonstrate that the proposed method can achieve high consistent alignment with subjective assessment. © 2019 IEEE.

关键词： Convolution

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Control mechanism of eliminate noise and improve visual perception of the image

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AIP conference Proceedings 2023年第1期2789卷

作者： Saida Beknazarova Shaxislam Joldasov Ozoda Abdullayeva Dilshod Mamasoatov Tashkent University of Information Technologies named after Muhammad Al-Khwarizmi Tashkent Uzbekistan Namangan Engineering-Construction Institute Namangan Uzbekistan Uzbekistan University of Journalism and Mass Communications Tashkent Uzbekistan

The article describes the method of noise elimination is linear averaging over neighboring pixels in a spatial neighborhood. In this case, the arithmetic mean of its neighbors is used as the new value of the central pixel. The larger the window size, the stronger the averaging occurs. Linear averaging partially impairs the perception of the image and complicates its further analysis, since it blurs the contours of objects. Adaptive filters are used to preserve the contours of objects in the image, which can change the filter coefficients in accordance with the features of the processed image. Most adaptive filters are local (implement "windowing"). For each position of the sliding processing window, either the filter mask counts are recalculated or the window configuration is changed. The disadvantage of adaptive filters is that the synthesis of these filters requires precise knowledge of some static characteristics of signals and noise, which is difficult in conditions of uncertainty in the field of application of video sensor-based computing devices.

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Blind panoramic image quality assessment via the asymmetric mechanism of human brain 34

Blind panoramic image quality assessment via the asymmetric ...

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

作者： Xia, Yumeng Wang, Yongfang Peng, Ye Shanghai University School of Communication and Information Engineering Shanghai200444 China

ISBN: (纸本)9781728137230

Most existing objective quality evaluation metrics for panoramic images are typically derived from peak signal to noise ratio (PSNR) or structural similarity (SSIM). However, they need pristine panoramic images and are not highly consistent with human perception. To address the problem, we propose a novel blind panoramic image quality assessment (PIQA) method to predict the visual quality of panoramic images based on asymmetric mechanism of human brain. In the proposed method, the high-frequency feature is extracted by panoramic-weighted local binary pattern and relative total variation is used to clip the high-frequency information in panoramic images, then panoramic-weighted statistic feature can represent the lowfrequency feature. Finally, support vector regression (SVR) is adopted to build a quality predictor from feature space to quality score space. The experimental results on the public subjective dataset demonstrate the superiority of our proposed metric compared with state-of-The-Art objective PIQA methods. © 2019 IEEE.

关键词： Local binary pattern

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Learned image and video compression with deep neural networks

Learned image and video compression with deep neural network...

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IEEE visual communications and image processing (VCIP)

作者： Dong Xu Guo Lu Ren Yang Radu Timofte School of Electrical and Information Engineering The University of Sydney Australia

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

This tutorial aims at reviewing the recent progress in the deep learning based data compression, including image compression and video compression. In the past years, deep learning techniques have been successfully applied to a large number of computer vision and image processing tasks. However, for the data compression task, the traditional approaches (i.e., block based motion estimation and motion compensation, etc.) are still widely employed in the mainstream codecs. Considering the powerful representation capability, it is possible to improve the data compression performance by employing the advanced deep learning technologies. To this end, deep leaning based compression approaches have recently received significant attention from both academia and industry in the field of computer vision and image/video compression. In this tutorial, we will introduce the related deep learning techniques for image compression and video compression. Specifically, in this tutorial, we will first introduce the basic pipeline for the traditional codecs, such as JPEG, H.264 and HEVC. Then, we will discuss the common network architectures for visual data compression and analyse different learning based entropy models. Based on these techniques, we will describe several widely used end-to-end optimized frameworks for visual data compression. In summary, our tutorial will cover both the traditional data coding techniques and the popular learning based visual data compression algorithms, which will help the audiences with different backgrounds learn the recent progresses in this emerging research area.

关键词： image coding Data compression Computer vision Video compression Deep learning Tutorials conferences

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