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Improving Generative Adversarial Networks with Adaptive Control Learning 33

Improving Generative Adversarial Networks with Adaptive Cont...

引用

33rd IEEE International conference on visual communications and image processing (IEEE VCIP)

作者： Ma, Xiaohan Jin, Rize Sohn, Kyung-Ah Paik, JoonYoung Sun, Jing Chung, Tae-Sun Ajou Univ Dept Software Suwon South Korea Tianjin Polytech Univ Sch Comp Sci & Software Engn Tianjin Peoples R China

ISBN: (纸本)9781538644584

Generative adversarial networks (GANs) are well known both for being unstable to train and for the problem of mode collapse, particularly when trained on data collections containing a diverse set of visual objects. This study introduces an adaptive hyper-parameter learning procedure for GANs as an alternative to the existing static approach. The proposed procedure is designed to mitigate the impact of instability and saturation in the original by dynamically adjusting the ratio of the training steps of both the generator and discriminator. To accomplish this, we track and analyze stable training curves of relatively narrow datasets and use them as the target fitting lines when training more diverse data collections. Experimental results show that the proposed model improves the stability and generates more realistic images.

关键词： Generative adversarial networks image synthesis adaptive algorithm

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Attribute-and-Identity Correspondence Network for Clothes Search 33

Attribute-and-Identity Correspondence Network for Clothes Se...

引用

33rd IEEE International conference on visual communications and image processing (IEEE VCIP)

作者： Yuan, Jiahui Yang, Jie Luo, Zhonghua Wen, Wei Samsung Res Inst China Beijing SRC B Beijing Peoples R China

ISBN: (纸本)9781538644584

Clothes search is a challenging task, because the uncontrolled capture condition and massive styles of clothing images make the accurate retrieval difficult. To address the problem, we design a novel Attribute-and-Identity Correspondence Network (AICN) to obtain more robust features for clothes retrieval. The attribute information of clothes includes category, color, shape and so forth. The images of the same clothing pictured under different pose or background conditions have the same identity. Specifically, the AICN is comprised of two sub-branches: the attribute one and the identity one, and the two branches share the low level features. The former exploits attribute information to learn the discriminative feature, while the latter is to handle the variations of images with the same identity. Furthermore, we design a compactness loss to force the feature spaces of the two branches to become close, since their semantic spaces have overlap intuitively. Experiments on the benchmark and practical dataset demonstrate the effectiveness and priority of our method.

关键词： visual Similarity image Retrieval Deep Learning Feature Learning Clothes Search

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Red Blood Cell and Sickle Cell Detection from Microscopic Blood images of Sickle Cell Anemic Patient

Red Blood Cell and Sickle Cell Detection from Microscopic Bl...

引用

International conference on Wireless communications, Signal processing and Networking (WiSPNET)

作者： Chayashree Patgiri Amrita Ganguly Department of Electrical Engineering Assam Engineering College Guwahati 13 India

ISBN: (数字)9781538692790

ISBN: (纸本)9781538692806

Sickle cell anemia (SCA) is a type of haemolytic anemia which is the most commonly inherited blood disorder. Detection of red blood cell and sickle cell from sickle cell anemic person is a very challenging task. Besides traditional visual inspection of microscopic images, various methods have been developed which are based on image processing technique for faster and more accurate diagnosis of SCA. In this paper normal and abnormal red blood cells have been detected using Niblack's thresholding technique from microscopic blood images of sickle cell anemic patient. The process involves preprocessing of microscopic blood smear and segmenting the preprocessed image using Niblack's thresholding algorithm. Then using geometrical features of blood cells a metric (form factor) is calculated to classify normal red blood cells and abnormal cells.

关键词： image segmentation Microscopy Red blood cells Biomedical imaging Diseases Feature extraction

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Fast Non-local Means Denoising for MR image Sequences 12

Fast Non-local Means Denoising for MR Image Sequences

引用

12th International conference on Signal processing and communications (SPCOM)

作者： Bhujle, Hemalata Vadavadagi, Basavaraj SDM Coll Engn & Technol Dharwad Karnataka India

ISBN: (纸本)9781538638217

Denoising algorithms are used for the enhancement of magnetic resonance (MR) images. MR images possess more structural details compared to normal images which have to be preserved for better diagnosis. Non-local means (NLM) filter is proved to be the best in preserving edges, however demands higher computations. From diagnostic perspective, the denoising algorithms should be computationally fast and accurate. The aim of this paper is to improve the accuracy and computational efficiency of unbiased NLM filter for MR image sequences. In this work we propose to do so by useful alternative of NLM technique in-conjunction with principal components for Rician noise. The variants of PCA based denoising techniques developed so far compute PCA locally for each image, thus decreasing computational efficiency. In this work we propose to compute PCA only once globally for each shot. A modified preprocessing step of shot boundary detection is employed to segregate 3D MR sequences in various shots based on its content similarity. Further denoising is carried out in non-local means framework with reduced dimensionality. We compare results with the existing NLM based MR denoising techniques and show that the proposed method is competitive in terms of attaining higher accuracy and computational efficiency. The performance of the proposed algorithm is evaluated with PSNR, SSIM and visual perception.

关键词： Non-local means denoising Rician shot boundary

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Stereoscopic image Quality Assessment Based on both Distortion and Disparity 33

Stereoscopic Image Quality Assessment Based on both Distorti...

引用

33rd IEEE International conference on visual communications and image processing (IEEE VCIP)

作者： Niu, Yuzhen Zhong, Yini Ke, Xiao Shi, Yiqing Fuzhou Univ Coll Math & Comp Sci Fuzhou Fujian Peoples R China

ISBN: (纸本)9781538644584

Understanding the characteristics of high-quality stereoscopic 3D (S3D) images has great significance for S3D image classification, quality assessment, and quality enhancement. Existing works assess the quality of an S3D image from a single perspective and use databases with subjective opinion scores obtained by conducting subjective experiments in labs. So the performance of these works in real applications is unclear and questionable. In this paper, we propose an S3D image quality assessment index based on two important factors, namely DisTortion and DisParity (DTDP). Distortion reflects the extent of distortion of each view of an S3D image, respectively. Disparity is a distinguishing factor for an S3D image as compared with a monocular image. We design some features to represent these two factors and use a random forest regression (RFR) to learn the mapping between the features and subjective opinion scores. The database we choose is the NVIDIA 3D VISION LIVE Highest Rated database which is from real application. The images and the subjective opinion scores are directly come from the website viewers all over the world. Experimental results demonstrate a superior performance of the proposed DTDP index as compared to the existing methods.

关键词： Stereoscopic 3D distortion visual comfort image quality assessment disparity

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SIQD: Surveillance image Quality Database and Performance Evaluation for Objective Algorithms 33

SIQD: Surveillance Image Quality Database and Performance Ev...

引用

33rd IEEE International conference on visual communications and image processing (IEEE VCIP)

作者： Zhu, Wenhan Zhai, Guangtao Yao, Chen Yang, Xiaokang Shanghai Jiao Tong Univ Insti Image Commu & Infor Proce Shanghai Peoples R China Minist Publ Secur Res Inst 3 Shanghai Peoples R China

ISBN: (纸本)9781538644584

The surveillance camera is an important security device for police to maintain public order and provides clues to trace suspects. The quality of surveillance videos/images may be degraded during acquisition, compression and communication. It is difficult to acquire key information, such as human faces, with the bad quality surveillance videos/images. So the research of the quality assessment of surveillance images is quite necessary. In this paper, we perform a study on subjective quality evaluation of surveillance images and investigate whether the existing objective quality measures can be applied to the surveillance images. Concretely, we establish a new surveillance image quality database (SIQD) including 500 surveillance images with different degrees of quality through subjective study. Next, we investigate the prediction performance of the existing popular image quality assessment (IQA) algorithms on the SIQD database, which include eleven NR methods and four sharpness models. Experimental results demonstrate that the present objective models do not work well and quality measures having high correlation with human visual perception are high needed. Some meaningful findings are also given, which may enlighten the design of objective surveillance IQA models. The SIQD will be made publicly available to facilitate further surveillance IQA researches.

关键词： Surveillance image subjective image quality assessment no-reference image distortion performance comparison

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Confusion Weighted Loss for Ambiguous Classification 33

Confusion Weighted Loss for Ambiguous Classification

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33rd IEEE International conference on visual communications and image processing (IEEE VCIP)

作者： Lei, Yu Dong, Yuan Xiong, Fengye Bai, Hongliang Yuan, Hao Beijing Univ Posts & Telecommun Beijing Peoples R China Beijing FaceAll Co Beijing Peoples R China

ISBN: (纸本)9781538644584

The Convolution Neural Network(CNN) has achieved great performance in image classification, partially due to the deeper and deeper structure. While its complexity brings more challenge to the practical application. So we can design a more efficient loss function to get better results without a more complex network. In this paper, we proposed a weighted softmax loss function called confusion weighted loss to learn the relationship among the confusing categories. Firstly, we generate a similarity matrix based on the confusion matrix to illustrate the relationship among the categories. Then, we propose a clustering algorithm to find out the confusing categories. Finally, to learn more information among them, we design a weighted matrix used in our loss function. Experiments on different datasets demonstrate the effectiveness of our method.

关键词： Confusion Matrix image Classification Convolutional Neural Networks Clustering Loss Function

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Light Field image Sparse Coding via CNN-Based EPI Super-Resolution 33

Light Field Image Sparse Coding via CNN-Based EPI Super-Reso...

引用

33rd IEEE International conference on visual communications and image processing (IEEE VCIP)

作者： Zhao, Jinbo An, Ping Huang, Xinpeng Shan, Liang Ma, Ran Shanghai Univ Sch Commun & Informat Engn Shanghai Inst Adv Commun & Data Sci Shanghai 200444 Peoples R China

ISBN: (纸本)9781538644584

This paper proposes a novel light field (LF) image compression scheme by super resolving the epipolar plane image (EPI) via convolutional neural network (CNN). In the scheme, we first decompose the LF image into sub-aperture images (SAIs), and only one quarter of them are compressed on the encoding side to reduce the bitrate. On the decoding side, we use these selected SAIs to reconstruct the entire LF by taking advantage of the special structure of EPI. The low-resolution EPIs generated from the sparse SAIs are super resolved by using deep residual network and the output high-resolution EPIs are used to rebuild the dense SAIs. Experimental results show the superior performance of our scheme, which achieve 1.46 dB quality improvement and 35.85 percent bit rate reduction on average compared with the typical pseudo-sequence-based coding method.

关键词： light field compression sparse coding EPI super-resolution deep learning convolutional neural network

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Compressively Sensed Multi-View image Reconstruction Using Joint Optimization Modeling 33

Compressively Sensed Multi-View Image Reconstruction Using J...

引用

33rd IEEE International conference on visual communications and image processing (IEEE VCIP)

作者： Zhu, Jiale Wang, Jin Zhu, Qing Beijing Univ Technol Fac Informat Technol Beijing Key Lab Internet Culture & Digital Dissem Beijing Peoples R China

ISBN: (纸本)9781538644584

Utilizing both intra and inter views correlation plays a key role to improve compressive sensing reconstruction of multi-view images. For this goal, this paper presents a joint optimization model (JOM) for compressively-sensed multi-view image reconstruction, which jointly optimizes an adaptive disparity compensated residual total variation (ARTV) and a multi-image nonlocal low-rank tensor (MNLRT). To exploit the inter-view correlation efficiently, the ARTV method adaptively forms suitable dynamic image set to help reconstruct the current one. Different from previous work, the MNLRT regularization uses tensor rather than 2D matrix to exploit nonlocal low-rank property, which keeps intrinsic geometrical structures of image patches. An efficient algorithm is further proposed to solve the joint optimization problem via Split-Bregman based technique. Extensive experimental results demonstrate our method outperforms state-of-the-arts algorithms with almost 1.5 dB gain in terms of PSNR, while obtaining dramatically improved visual quality for edge area, especially at low sampling rates.

关键词： compressed sensing total variation multi-view image nonlocal low-rank tensor disparity compensation

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Roads Detection of Aerial image with FCN-CRF Model 33

Roads Detection of Aerial Image with FCN-CRF Model

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33rd IEEE International conference on visual communications and image processing (IEEE VCIP)

作者： Rao, Yunbo Liu, Wei Pu, Jiansu Deng, Jianhua Wang, Qifei Univ Elect Sci & Technol China Sch Informat & Software Engn Chengdu Sichuan Peoples R China Univ Calif Berkeley EECS Berkeley CA 94720 USA

ISBN: (纸本)9781538644584

this paper describes a deep learning based model for roads detection in Aerial image. In general, standard CNN networks would have less ability for tiny objects detection in remote sensing image. With this regard, we propose a novel fully convolutional network, which utilizes deconvolution layers and feature map fussing to take as input intensity and pixel-wise labeling. Moreover, the class prediction are used as the input to Condition Random Field (CRF) for the final pixel prediction. The Batch Normalization (BN) algorithm and two stages training strategy were used in our model to reduce the time cost of model training. Several experimental results conducted in Massach-useets. Road dataset demonstrate the superiority of our model with respect to accuracy and time cost.

关键词： Road detection Aerial image FCN CRF

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