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Feature2Mass: visual Feature processing in Latent Space for Realistic Labeled Mass Generation 15th

Feature2Mass: Visual Feature Processing in Latent Space for ...

引用

15th European conference on Computer Vision (ECCV)

作者： Lee, Jae-Hyeok Kim, Seong Tae Lee, Hakmin Ro, Yong Man Korea Adv Inst Sci & Technol Sch Elect Engn Daejeon South Korea

ISBN: (纸本)9783030110246;9783030110239

This paper deals with a method for generating realistic labeled masses. Recently, there have been many attempts to apply deep learning to various bio-image computing fields including computer-aided detection and diagnosis. In order to learn deep network model to be well-behaved in bio-image computing fields, a lot of labeled data is required. However, in many bioimaging fields, the large-size of labeled dataset is scarcely available. Although a few researches have been dedicated to solving this problem through generative model, there are some problems as follows: (1) The generated bio-image does not seem realistic;(2) the variation of generated bio-image is limited;and (3) additional label annotation task is needed. In this study, we propose a realistic labeled bio-image generation method through visual feature processing in latent space. Experimental results have shown that mass images generated by the proposed method were realistic and had wide expression range of targeted mass characteristics.

关键词： Feature processing in latent space image synthesis Bio-image generation Medical mass generation

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Icon Colorization Based On Triple Conditional Generative Adversarial Networks

Icon Colorization Based On Triple Conditional Generative Adv...

引用

IEEE visual communications and image processing (VCIP)

作者： Qin-Ru Han Wen-Zhe Zhu Qing Zhu Beijing University of Technology Beijing China

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

Current automatic colorization systems have many defects such as "contour blur", "color overflow"and "color miscellaneous", especially when they are coloring the images with hollowed-out structure. We propose a model based on triple conditional generative adversarial networks, for generator we provide contour image, colored icon and colorization mask as inputs, our network has three discriminators, structure discriminator is trained to judge if the generated icon has similar contour to the input icon, color discriminator anticipates generated icon and the input icon has the similar color style, the function of mask discriminator is to distinguish whether the output has the similar colorization area to the input mask. For the evaluation, we compared with some existing colorization models, also we made a questionnaire to obtain the evaluation of generated icons from different models. The results showed that our colorization model obtain better results comparing to the other models both in generating hollowed-out and solid structure icons.

关键词： image color analysis Training Generators Generative adversarial networks Solid modeling Data models Neural networks

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A Dense-Gated U-Net for Brain Lesion Segmentation

A Dense-Gated U-Net for Brain Lesion Segmentation

引用

IEEE visual communications and image processing (VCIP)

作者： Zhongyi Ji Xiao Han Tong Lin Wenmin Wang School of Electronic and Computer Engineering Peking University Shenzhen Graduate School School of EECS Peking University Peng Cheng Laboratory International Institute of Next Generation Internet Macau University of Science and Technology

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

Brain lesion segmentation plays a crucial role in diagnosis and monitoring of disease progression. DenseNets have been widely used for medical image segmentation, but much redundancy arises in dense-connected feature maps and the training process becomes harder. In this paper, we address the brain lesion segmentation task by proposing a Dense-Gated U-Net (DGNet), which is a hybrid of Dense-gated blocks and U-Net. The main contribution lies in the dense-gated blocks that explicitly model dependencies among concatenated layers and alleviate redundancy. Based on dense-gated blocks, DGNet can achieve weighted concatenation and suppress useless features. Extensive experiments on MICCAI BraTS 2018 challenge and our collected intracranial hemorrhage dataset demonstrate that our approach outperforms a powerful backbone model and other state-of-the-art methods.

关键词： image segmentation Logic gates Lesions Training Redundancy Medical diagnostic imaging Hemorrhaging

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On Segmentation of Maxillary Sinus Membrane using Automatic Vertex Screening

On Segmentation of Maxillary Sinus Membrane using Automatic ...

引用

IEEE visual communications and image processing (VCIP)

作者： Kang Rong Li Tai-Chiu Hsung Andy W.K. Yeung Michael M. Bornstein Guangdong University of Technology Guangzhou People's Republic of China The University of Hong Kong People's Republic of China

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

The purpose of this study is to develop an automatic technique to segment the membrane of the maxillary sinus with morphological changes (e.g. thickened membrane and cysts) for the detection of abnormalities. The first step is to segment the sinus bone cavity in the CBCT image using fuzzy C-mean algorithm. Then, the vertices of inner bone walls of sinus in the mesh model are screened with vertex normal direction and angular based mean-distance filtering. The resulted vertices are then used to generate the bony sinus cavity mesh model by using Poisson surface reconstruction. Finally, the sinus membrane morphological changes are segmented by subtracting the air sinus segmentation from the reconstructed bony sinus cavity. The proposed method has been applied on 5 maxillary sinuses with mucosal thickening and has demonstrated that it can segment thin membrane thickening (

关键词： Bones Surface morphology Surface reconstruction image segmentation Three-dimensional displays Mathematical model Dentistry

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Fast image Deblurring Based on Dual-Exposure Prior 34

Fast Image Deblurring Based on Dual-Exposure Prior

引用

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

作者： Zhang, Xu Hu, Hai-Miao Chen, Jialin Beihang University Beijing Key Laboratory of Digital Media School of Computer Science and Engineering Beijing100191 China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China

ISBN: (纸本)9781728137230

image deblurring is an important task for practical applications. However, it remains a challenge due to the serious camera shake. In this paper, we propose a prior named Dual-Exposure Prior (DEP) according to the observation that image captured with a relatively short exposure time may achieve sharp edges. Based on the DEP, the blur kernels of the blurry images can be accurately estimated. An effective optimization between blur kernel estimation and intermediate image restoration is established by using the L2-regularized DEP in the maximum a posterior framework. Each sub-problem of the optimization is convex, it can be solved in close-form with fast Fourier transforms acceleration. The experiments based on real and synthesized blurry images show that the proposed image deblurring algorithm can outperform the state-of-The-Art methods in terms of subjective and objective quality. Moreover, the proposed algorithm can be implemented with significantly low computational complexity. © 2019 IEEE.

关键词： Fast Fourier transforms

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A review of data preprocessing modules in digital image forensics methods using deep learning

A review of data preprocessing modules in digital image fore...

引用

IEEE visual communications and image processing (VCIP)

作者： Alexandre Berthet Jean-Luc Dugelay Eurecom

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

Access to technologies like mobile phones contributes to the significant increase in the volume of digital visual data (images and videos). In addition, photo editing software is becoming increasingly powerful and easy to use. In some cases, these tools can be utilized to produce forgeries with the objective to change the semantic meaning of a photo or a video (e.g. fake news). Digital image forensics (DIF) includes two main objectives: the detection (and localization) of forgery and the identification of the origin of the acquisition (i.e. sensor identification). Since 2005, many classical methods for DIF have been designed, implemented and tested on several databases. Meantime, innovative approaches based on deep learning have emerged in other fields and have surpassed traditional techniques. In the context of DIF, deep learning methods mainly use convolutional neural networks (CNN) associated with significant preprocessing modules. This is an active domain and two possible ways to operate preprocessing have been studied: prior to the network or incorporated into it. None of the various studies on the digital image forensics provide a comprehensive overview of the preprocessing techniques used with deep learning methods. Therefore, the core objective of this article is to review the preprocessing modules associated with CNN models.

关键词： Deep learning Forgery Cameras Histograms Discrete cosine transforms Digital images Feature extraction

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Fast compressed sensing recovery using generative models and sparse deviations modeling

Fast compressed sensing recovery using generative models and...

引用

IEEE visual communications and image processing (VCIP)

作者： Lei Cai Yuli Fu Youjun Xiang Tao Zhu Xianfeng Li Huanqiang Zeng School of Electronic and Information Engineering South China University of Technology Guangzhou China School of Information Science and Engineering Huaqiao University Xiamen China

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

This paper develops an algorithm to effectively explore the advantages of both sparse vector recovery methods and generative model-based recovery methods for solving compressed sensing recovery problem. The proposed algorithm mainly consists of two steps. In the first step, a network-based projected gradient descent (NPGD) is introduced to solve a non-convex optimization problem, obtaining a preliminary recovery of the original signal. Then with the obtained preliminary recovery, a l 1 norm regularized optimization problem is solved by optimizing for sparse deviation vectors. Experimental results on two bench-mark datasets for image compressed sensing clearly demonstrate that the proposed recovery algorithm can bring about high computation speed, while decreasing the reconstruction error continuously with increasing the number of measurements.

关键词： Optimization image reconstruction Compressed sensing Measurement uncertainty Computational modeling Sensors Noise measurement

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DEN: Disentanglement and Enhancement Networks for Low Illumination images

DEN: Disentanglement and Enhancement Networks for Low Illumi...

引用

IEEE visual communications and image processing (VCIP)

作者： Nelson Chong Ngee Bow Vu-Hoang Tran Punchok Kerdsiri Yuen Peng Loh Ching-Chun Huang Multimedia University Cyberjaya Selangor Malaysia Ho Chi Minh City University of Technology and Education Ho Chi Minh City Vietnam DCOE digital center of excellence PTT Exploration and Production PCL Bangkok Thailand National Chiao Tung University Hsinchu Taiwan

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

Though learning-based low-light enhancement methods have achieved significant success, existing methods are still sensitive to noise and unnatural appearance. The problems may come from the lack of structural awareness and the confusion between noise and texture. Thus, we present a low-light image enhancement method that consists of an image disentanglement network and an illumination boosting network. The disentanglement network is first used to decompose the input image into image details and image illumination. The extracted illumination part then goes through a multi-branch enhancement network designed to improve the dynamic range of the image. The multi-branch network extracts multi-level image features and enhances them via numerous subnets. These enhanced features are then fused to generate the enhanced illumination part. Finally, the denoised image details and the enhanced illumination are entangled to produce the normal-light image. Experimental results show that our method can produce visually pleasing images in many public datasets.

关键词： Lighting Feature extraction Reflectivity image enhancement Finite element analysis Convolution Tuning

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A night-time outdoor data set for low-light enhancement

A night-time outdoor data set for low-light enhancement

引用

IEEE visual communications and image processing (VCIP)

作者： Yudong Zhou Ronggang Wang Yang Zhao School of Electronic and Computer Engineering Shenzhen Graduate School Peking University Shenzhen China School of Computers and Information Hefei University of Technology Hefei China

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

Low light Enhancement has been a hot topic in recent years, and many deep neural network (DNN)-based methods have achieved remarkable performance. However, the rapid development of DNNs also raises the urgent requirement of high-quality training sets, especially supervised night-time data sets. In this paper, we establish a night-time outdoor data set (NOD 1 ) that contains 1214 groups of images. We also generate appropriate and high-quality reference images for each group based on multi-exposure fusion strategy, which not only focuses on dark areas but also provides details for over-exposed areas in low light images. Furthermore, a simple but efficient network is presented as the baseline of NOD. Experimental results on NOD and other data sets show the generalizability and effectiveness of the proposed data set and baseline model.

关键词： Training Lighting image color analysis Feature extraction Dynamic range Task analysis image enhancement

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Asymmetric Supervised Deep Autoencoder for Depth image based 3D Model Retrieval 34

Asymmetric Supervised Deep Autoencoder for Depth Image based...

引用

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

作者： Siddiqua, Ayesha Fan, Guoliang Oklahoma State University School of Electrical and Computer Engineerin StillwaterOK United States

ISBN: (纸本)9781728137230

In this paper, we propose a new asymmetric supervised deep autoencoder approach to retrieve 3D shapes based on depth images. The asymmetric supervised autoencoder is trained with real and synthetic depth images together. The novelty of this research lies in the asymmetric structure of a supervised deep autoencoder. The proposed asymmetric deep supervised autoencoder deals with the incompleteness and ambiguity present in the depth images by balancing reconstruction and classification capabilities in a unified way with mixed depth images. We investigate the relationship between the encoder layers and decoder layers, and claim that an asymmetric structure of a supervised deep autoencoder reduces the chance of overfitting by 8% and is capable of extracting more robust features with respect to the variance of input than that of a symmetric structure. The experimental results on the NYUD2 and ModelNet10 datasets demonstrate that the proposed supervised method outperforms the recent approaches for cross modal 3D model retrieval. © 2019 IEEE.

关键词： Supervised learning

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