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检索条件"任意字段=Conference on Visual Communications and Image Processing 2007"

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Neural network and SVM based kidney stone based medical image classification 4th

Neural network and SVM based kidney stone based medical imag...

引用

4th International conference on Computer Vision and image processing, CVIP 2019

作者： Chak, Priyanka Navadiya, Payal Parikh, Bhavya Pathak, Ketki C. Electronics and Communication Department SCET Surat India

ISBN: (纸本)9789811540141

In recent medical science, automatic diagnosis system is highly demanded Our research is designed to detect the kidney stones by using digital signal processing techniques. In this proposed method we have applied Neural network based feature extraction method along with Support Vector Machine (SVM) based linear classifier to classify normal CT- scan images and kidney stone images. All the CT scan kidney images are initially preprocessed to filter out speckle noise to enhance visual quality. In later part of the algorithm region of interest (stone part) is extracted using segmentation algorithm. Among these segmented images, the Gray level Co-occurrence Matrix (GLCM) algorithm is introduced to extract the features which are used to train the network. In medical diagnosis for early stage detection of diseases, one of the reliable classifier is Artificial Neural Network (ANN). In our proposed algorithm we have design ANN system for 12 input nodes, 10 hidden nodes and 2 output nodes. The network is trained until it targets its desired output. Only ANN based classification reduces diagnosis time and increase the efficiency with accuracy 85%. To classify the images into two class (kidney stone or normal kidney) Support Vector Machine (SVM) is used. Artificial Neural Network (ANN) with SVM based classification gives accuracy of 95% with lesser features and up to 99% with all features. © Springer Nature Singapore Pte Ltd 2020.

关键词： Support vector machines

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Learning From Paired and Unpaired Data: Alternately Trained CycleGAN for Near Infrared image Colorization

Learning From Paired and Unpaired Data: Alternately Trained ...

引用

IEEE visual communications and image processing (VCIP)

作者： Zaifeng Yang Zhenghua Chen Institute of High Performance Computing Agency for Science Technology and Research (A*STAR) Singapore Institute for Infocomm Research Agency for Science Technology and Research (A*STAR) Singapore

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

This paper presents a novel near infrared (NIR) image colorization approach for the Grand Challenge held by 2020 IEEE International conference on visual communications and image processing (VCIP). A Cycle-Consistent Generative Adversarial Network (CycleGAN) with cross-scale dense connections is developed to learn the color translation from the NIR domain to the RGB domain based on both paired and unpaired data. Due to the limited number of paired NIR-RGB images, data augmentation via cropping, scaling, contrast and mirroring operations have been adopted to increase the variations of the NIR domain. An alternating training strategy has been designed, such that CycleGAN can efficiently and alternately learn the explicit pixel-level mappings from the paired NIR-RGB data, as well as the implicit domain mappings from the unpaired ones. Based on the validation data, we have evaluated our method and compared it with conventional CycleGAN method in terms of peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and angular error (AE). The experimental results validate the proposed colorization framework.

关键词： Training image color analysis Generators Gallium nitride Decoding image edge detection Gray-scale

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A Framework for Jointly Assessing and Reducing Imaging Artefacts Automatically Using Texture Analysis and Total Variation Optimisation for Improving Perivascular Spaces Quantification in Brain Magnetic Resonance Imaging 24th

A Framework for Jointly Assessing and Reducing Imaging Artef...

引用

24th conference on Medical image Understanding and Analysis (MIUA)

作者： Bernal, Jose Valdes-Hernandez, Maria Ballerini, Lucia Escudero, Javier Jochems, Angela C. C. Clancy, Una Doubal, Fergus N. Stringer, Michael S. Thrippleton, Michael J. Touyz, Rhian M. Wardlaw, Joanna M. Univ Edinburgh Ctr Clin Brain Sci Edinburgh Midlothian Scotland Univ Edinburgh Inst Digital Commun Edinburgh Midlothian Scotland Univ Glasgow Inst Cardiovasc & Med Sci Glasgow Lanark Scotland

ISBN: (纸本)9783030527914;9783030527907

Perivascular spaces are fluid-filled tubular spaces that follow the course of cerebral penetrating vessels, thought to be a key part in the brain's circulation and glymphatic drainage system. Their enlargement and abundance have been found associated with cerebral small vessel disease. Thus, their quantification is essential for establishing their relationship with neurological diseases. Previous works in the field have designed visual rating scales for assessing the presence of perivascular spaces and proposed segmentation techniques to reduce flooring and ceiling effects of qualitative visual scales, processing times, and inter-observer variability. Nonetheless, their application depends on the acquisition quality. In this paper, we propose a framework for improving perivascular spaces quantification using both texture analysis and total variation filtering. Texture features were considered for evaluating the image quality and determining automatically whether filtering was needed. We tested our work using data from a cohort of patients with mild stroke (n = 60) with different extents of small vessel disease features and image quality. Our results demonstrate the potential of our proposal for improving perivascular spaces assessments.

关键词： Perivascular spaces Cerebral small vessel disease image enhancement Imaging artefact reduction Brain magnetic resonance imaging

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FR-IQA-Permutation Entropy Deviation Index 34

FR-IQA-Permutation Entropy Deviation Index

引用

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

作者： Baqar, Mohtashim Lau, Sian Lun Ebrahim, Mansoor Sunway University Department of Computing and Information Systems Malaysia Karachi Pakistan

ISBN: (纸本)9781728137230

In this work, a new objective image quality assessment (IQA) framework, based on the working principle of permutation entropy (PE) is proposed. The framework is titled as Permutation Entropy Deviation Index (PEDI). The idea is to design an IQA framework that should be highly accurate as well as computationally efficient;making it viable to be used with different image processing applications, for visual quality assessments. The proposed model make use of the PE that helps in detecting and visualizing changes related to structures with correlation between successive samples instead of considering magnitudes of the signal. Thus, the proposed approach uses this property to efficiently predict image quality. The PE exploits the global variations in the local quality map for image quality assessment. With standard deviation as the pooling strategy, it is noted that permutation entropy between reference and distorted images can predict image quality with high measures of accuracy. Experimental results on subjective database, CSIQ, have shown that the proposed model outperforms most of existing SOTA image quality assessment models and highly correlates with subjective judgements. © 2019 IEEE.

关键词： image quality

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Stereoscopic image reflection removal based on Wasserstein Generative Adversarial Network

Stereoscopic image reflection removal based on Wasserstein G...

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IEEE visual communications and image processing (VCIP)

作者： Xiuyuan Wang Yikun Pan Daniel P.K. Lun The Hong Kong Polytechnic University Hong Kong

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

Reflection removal is a long-standing problem in computer vision. In this paper, we consider the reflection removal problem for stereoscopic images. By exploiting the depth information of stereoscopic images, a new background edge estimation algorithm based on the Wasserstein Generative Adversarial Network (WGAN) is proposed to distinguish the edges of the background image from the reflection. The background edges are then used to reconstruct the background image. We compare the proposed approach with the state-of-the- art reflection removal methods. Results show that the proposed approach can outperform the traditional single-image based methods and is comparable to the multiple-image based approach while having a much simpler imaging hardware requirement.

关键词： image edge detection Reflection Stereo image processing Training Generators image reconstruction Classification algorithms

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A Novel Approach to image Steganography Based on the image Colorization 34

A Novel Approach to Image Steganography Based on the Image C...

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

作者： Uruma, Kazunori Konishi, Katsumi Takahashi, Tomohiro Furukawa, Toshihiro Kogakuin University Tokyo Japan Hosei University Tokyo Japan Tokai University Kanagawa Japan Tokyo University of Science Tokyo Japan

ISBN: (纸本)9781728137230

This paper proposes a novel image steganography algorithm for color image. Recently, colorization-based image coding technique has been studied. In order to compress the color image effectively, this technique transform the chrominance image to a vector in a low-dimensional subspace via the colorization matrix. This paper utilizes the colorization-based image coding for steganography algorithm, where the secret data is embedded into the null space of the colorization matrix. Because the null space is high dimension enough, a large capacity data can be embedded. Numerical examples show that the proposed algorithm embeds large capacity secret data such as grayscale image into color image effectively. © 2019 IEEE.

关键词： Steganography

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Deep Convolutional Neural Network Based on Multi-Scale Feature Extraction for image Denoising

Deep Convolutional Neural Network Based on Multi-Scale Featu...

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IEEE visual communications and image processing (VCIP)

作者： Jing Zhang Liu Sang Zekang Wan Yuchen Wang Yunsong Li State Key Laboratory of Integrated Service Networks Xidian University Xi’an China

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

With the development of deep learning, many methods on image denoising have been proposed processing images on a fixed scale or multi-scale which is usually implemented by convolution or deconvolution. However, excessive scaling may lose image detail information, and the deeper the convolutional network the easier to lose network gradient. Diamond Denoising Network (DmDN) is proposed in this paper, which mainly based on a fixed scale and meanwhile considering the multi-scale feature information by using the Diamond-Shaped (DS) module to deal with the problems above. Experimental results show that DmDN is effective in image denoising.

关键词： Feature extraction Noise reduction image denoising Noise level image reconstruction Diamond Training

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Noise-Aware Texture-Preserving Low-Light Enhancement

Noise-Aware Texture-Preserving Low-Light Enhancement

引用

IEEE visual communications and image processing (VCIP)

作者： Zohreh Azizi Xuejing Lei C.-C. Jay Kuo Media Communications Lab University of Southern California Los Angeles USA

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

A simple and effective low-light image enhancement method based on a noise-aware texture-preserving retinex model is proposed in this work. The new method, called NATLE, attempts to strike a balance between noise removal and natural texture preservation through a lowcomplexity solution. Its cost function includes an estimated piece-wise smooth illumination map and a noise-free texture-preserving reflectance map. Afterwards, illumination is adjusted to form the enhanced image together with the reflectance map. Extensive experiments are conducted on common low-light image enhancement datasets to demonstrate the superior performance of NATLE.

关键词： Lighting Noise reduction Noise measurement image color analysis image edge detection Optimization Estimation

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27th International conference on Neural Information processing, ICONIP 2020

27th International Conference on Neural Information Processi...

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27th International conference on Neural Information processing, ICONIP 2020

ISBN: (纸本)9783030638221

The proceedings contain 376 papers. The special focus in this conference is on Neural Information processing. The topics include: DF-PLSTM-FCN: A Method for Unmanned Driving Based on Dual-Fusions and Parallel LSTM-FCN;learning Discrete Sentence Representations via Construction & Decomposition;sparse Hierarchical Modeling of Deep Contextual Attention for Document-Level Neural Machine Translation;improving Mongolian-Chinese Machine Translation with Automatic Post-editing;exploration on the Generation of Chinese Palindrome Poetry;error Heuristic Based Text-Only Error Correction Method for Automatic Speech Recognition;detecting Online Fake Reviews via Hierarchical Neural Networks and Multivariate Features;deep Cardiovascular Disease Prediction with Risk Factors Powered Bi-attention;a Hybrid Self-Attention Model for Pedestrians Detection;coarse-to-Fine Attention Network via Opinion Approximate Representation for Aspect-Level Sentiment Classification;CARU: A Content-Adaptive Recurrent Unit for the Transition of Hidden State in NLP;automatic Parameter Selection of Granual Self-organizing Map for Microblog Summarization;A Token-Wise CNN-Based Method for Sentence Compression;a Neural Framework for English-Hindi Cross-Lingual Natural Language Inference;WC2FEst-Net: Wavelet-Based Coarse-to-Fine Head Pose Estimation from a Single image;unsupervised Tongue Segmentation Using Reference Labels;u-Net Neural Network Optimization Method Based on Deconvolution Algorithm;a Feature Fusion Network for Multi-modal Mesoscale Eddy Detection;triple Attention Network for Clothing Parsing;temporal Smoothing for 3D Human Pose Estimation and Localization for Occluded People;Take a NAP: Non-Autoregressive Prediction for Pedestrian Trajectories;simultaneous Inpainting and Colorization via Tensor Completion;REXUP: I REason, I EXtract, I UPdate with Structured Compositional Reasoning for visual Question Answering;res2U-Net: image Inpainting via Multi-scale Backbone and Channel Attention;drawing Drea

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27th International conference on Neural Information processing, ICONIP 2020

27th International Conference on Neural Information Processi...

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27th International conference on Neural Information processing, ICONIP 2020

ISBN: (纸本)9783030638191

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