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Averting from Convolutional Neural Networks for Chest X-Ray image Classification

Averting from Convolutional Neural Networks for Chest X-Ray ...

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2021 ieee International conference on Signal processing, Information, Communication and Systems, SPICSCON 2021

作者： Changawala, Vrushank Sharma, Keshav Paunwala, Mita C.K. Pithawala College of Engg. & Tech. Computer Engineering Dept. Surat India C.K. Pithawala College of Engg. & Tech. Electronics & Communications Dept. Surat India

ISBN: (纸本)9781665478205

This paper attempts to survey newer approaches that do not use convolutional neural networks (CNNs) conventionally to the evolving field of medical image classification. While analyzing, firstly, an all feed-forward architecture MLP-Mixer and secondly, the inverted convolutional kernels coined as Involution with the baseline ResNets, both models yield comparable results in detecting Covid19 and pneumonia using Chest X-ray images. On top of that, merging Involution kernels into ResNet architectures can produce promising performance while training on roughly 40% fewer parameters. This paper further compares these two architectures with various CNN-based models. We hope this survey further helps the research community to utilize the capabilities of these newly introduced architectures in the medical field. [Code: https://***/Vrushank264/Averting-from-CNNs] © 2021 ieee.

关键词： COVID-19

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ON-AFN: Generating image caption based on the fusion of residual attention and ordered memory module 2

ON-AFN: Generating image caption based on the fusion of resi...

引用

2nd ieee Asia-Pacific conference on image processing, Electronics and Computers, IPEC 2021

作者： Lu, Chenchen Qu, Zhaowei Wang, Xiaoru Zhang, Heng Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System and Network Culture Beijing China

ISBN: (纸本)9781728190181

In this paper, we propose a novel and effective Fused Network, which is based on the residual attention mechanism and ordered memory module, a framework for image captioning that enables computer to produce the more accurate natural language description. Inspired by the recent advanced studies, our approach creatively divided our caption survey into two subtasks, one is how to efficiently extract visual features in the image, and the other is how to accurately express the semantic information in the features, and then propose excellent solutions for these tasks. In other words, Ordered Memory Attention Fusion Network is a comprehensive captioning model with the support of residual attention network and ordered memory module. Specially in this model, we innovatively employ the residual attention learning mechanism to optimize the extraction of features, and generate a more accurate and comprehensive natural language caption by applying the ordered memory module. We validated our model through extensive experiments on COCO Captioning benchmark, experiments show that it is conducive to generate the caption with higher accuracy and sets the new state-of-the-art by a significant margin. © 2021 ieee.

关键词： Semantics

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All-in-Focus Imaging from Event Focal Stack

All-in-Focus Imaging from Event Focal Stack

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conference on Computer Vision and Pattern Recognition (CVPR)

作者： Hanyue Lou Minggui Teng Yixin Yang Boxin Shi National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University National Engineering Research Center of Visual Technology School of Computer Science Peking University

Traditional focal stack methods require multiple shots to capture images focused at different distances of the same scene, which cannot be applied to dynamic scenes well. Generating a high-quality all-in-focus image from a single shot is challenging, due to the highly ill-posed nature of the single-image defocus and deblurring problem. In this paper, to restore an all-in-focus image, we propose the event focal stack which is defined as event streams captured during a continuous focal sweep. Given an RGB image focused at an arbitrary distance, we explore the high temporal resolution of event streams, from which we automatically select refocusing timestamps and reconstruct corresponding refocused images with events to form a focal stack. Guided by the neighbouring events around the selected timestamps, we can merge the focal stack with proper weights and restore a sharp all-in-focus image. Experimental results on both synthetic and real datasets show superior performance over state-of-the-art methods.

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Complementary Intrinsics from Neural Radiance Fields and CNNs for Outdoor Scene Relighting

Complementary Intrinsics from Neural Radiance Fields and CNN...

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conference on Computer Vision and Pattern Recognition (CVPR)

作者： Siqi Yang Xuanning Cui Yongjie Zhu Jiajun Tang Si Li Zhaofei Yu Boxin Shi National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University National Engineering Research Center of Visual Technology School of Computer Science Peking University Institute for Artificial Intelligence Peking University AI Innovation Center School of Computer Science Peking University School of Artificial Intelligence Beijing University of Posts and Telecommunications

Relighting an outdoor scene is challenging due to the diverse illuminations and salient cast shadows. Intrinsic image decomposition on outdoor photo collections could partly solve this problem by weakly supervised labels with albedo and normal consistency from multiview stereo. With neural radiance fields (NeRF), editing the appearance code could produce more realistic results without interpreting the outdoor scene image formation explicitly. This paper proposes to complement the intrinsic estimation from volume rendering using NeRF and from inversing the photometric image formation model using convolutional neural networks (CNNs). The former produces richer and more reliable pseudo labels (cast shadows and sky appearances in addition to albedo and normal) for training the latter to predict interpretable and editable lighting parameters via a single-image prediction pipeline. We demonstrate the advantages of our method for both intrinsic image decomposition and relighting for various real outdoor scenes.

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Multi-Task Joint 3D Swin Transformer Learning for Segmentation and Classification of Hyperspectral Medicine images

Multi-Task Joint 3D Swin Transformer Learning for Segmentati...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Dong Zhang Meijun Sun College of Intelligence and Computing Tianjin University Tianjin China Institute of Traditional Chinese Medicine Tianjin University of Traditional Chinese Medicine Tianjin China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Hyperspectral images had made many applications in the medical field with their rich spectral information. However, there were currently problems with feature extraction based on hyperspectral images, especially in extracting contextual feature information from spectral bands, and a single convolutional kernel may restrict the receptive field and inadequately capture the sequential properties of the data. Meanwhile, due to the large data volume of hyperspectral images, current medical hyperspectral images focus more on individual segmentation or classification. This paper proposed a 3D swin transformer with multi-task joint learning framework, to simultaneously learn multiple tasks for hyperspectral tongue images. Based on the 3D swin transformer model, the framework regards cross-band context feature learning of hyperspectral images as a sequence-to-sequence prediction process. The 3D transformer encoder used as the basic framework for shared feature extraction, and set up corresponding decoders for prediction according to different visual tasks such as segmentation and classification. We conducted simultaneous segmentation and classification tasks of the tongue coating region on the hyperspectral image of tongue images. The results showed that the proposed model had good results in single tasks of segmentation and classification, and performed better than other multi-task convolutional neural network models.

关键词： Training image segmentation visualization Tongue Three-dimensional displays Transformers Feature extraction Multitasking Hyperspectral imaging Biomedical imaging

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Depth Recovery from Single Monocular image in 3D Reconstruction and visual Precise Positioning 4

Depth Recovery from Single Monocular Image in 3D Reconstruct...

引用

4th International conference on Information Communication and Signal processing, ICICSP 2021

作者： Bao, Jianjun Wang, Haibo Fu, Xinzhu Luo, Ke Shen, Xiaolin Tiandi Changzhou Automation Co. Ltd CCTEG Changzhou Research Institute Changzhou China Jiangsu Urban and Rural Construction College Changzhou China

ISBN: (纸本)9781665407571

The depth information of the image is essential for accurate positioning. However, for the scale of the model, stricter standards are inevitable. This paper proposed Lower Layer Efficient Neural Network (LL-ENet) on the problems of requirements with light model, mostly using on the mobile devices. Firstly, The model reconstruct encoder by changing the convolution for less calculation, then classify the depth for efficient predictive ability. Finally, the result by experience shows that LL-EN et has a better effect on depth recovery with single monocular image in the outdoor scenes. Moreover the further experience shows that the proposed LL-ENet has better performance than other algorithms. © 2021 ieee.

关键词： image reconstruction

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A Preprocessing Method of Facial Expression image under Different Illumination 13

A Preprocessing Method of Facial Expression Image under Diff...

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13th ieee International conference on Communication Software and Networks (ICCSN)

作者： Hu, Yiyun Zeng, Xiaoping Huang, Zhiyong Dong, Xiong Chongqing Univ Sch Microelect & Commun Engn Chongqing Peoples R China

ISBN: (纸本)9781665431828

In this work, we propose an image processing method which combines the limited contrast adaptive histogram equalization (CLAHE) with Gamma transform to solve the illumination problem in facial expression recognition. We apply this algorithm to professional illumination datasets (Extended Yale B) and get better visual results, compared with using CLAHE and Gamma correction separately. Moreover, we use a convolution neural network (CNN) that pre-trained on FER2013 datasets to evaluate the effect of this method in facial expression recognition. We use this preprocessing algorithm to enhance the CK+ and Oulu expression datasets, and get accuracy of 89.24% and 70.24% respectively. Compared with the datasets that have not been pre-processed, it has provided an increase in classification accuracy of 7% on the Oulu datasets.

关键词： CLAHE illumination facial expression recognition

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HFRRU-Net: Hybrid Features Ringed Residual U-Net for image Splicing Forgery Detection

HFRRU-Net: Hybrid Features Ringed Residual U-Net for Image S...

引用

Data Driven Control and Learning Systems (DDCLS)

作者： Mengjing Sun Haijiang Zhu College of Information & Technology Beijing University of Chemical Technology Beijing China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

With the rapid development of computers and the internet, digital image forgery detection has become one of the important research hot topics in the field of computer vision. In this article, we propose a dual stream network to detect image tampering and locate forged areas. Our network includes the ringed residual U-Net, RGB stream and DCT stream, which is named as a hybrid feature ringed residual U-Net (HFRRU-Net) because of combining frequency domain features with RGB features. Compared to the existing works, HFRRU-Net is an end-to-end training approach without image preprocessing or post-processing, and this network may speed up the training and prediction process. In the experments, the HFRRU-Net is evaluated on five public image forgery datasets, CASIA v1.0, Columbia, In-The- Wild, Nist2016, and Realistic. The results indicate that the F1 scores of the HFRRU-Net outperforms other methods in four datasets. And visual comparisons further demonstrate the proposed method.

关键词： Training Learning systems visualization Digital images Splicing Streaming media Feature extraction

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Video captioning in compressed video 6

Video captioning in compressed video

引用

6th International conference on image, Vision and Computing, ICIVC 2021

作者： Zhu, Mingjian Zhejiang University School of Engineering Westlake University Institute of Advanced Technology Westlake Institute for Advanced Study Hangzhou China

ISBN: (纸本)9781665443685

Existing approaches in video captioning concentrate on exploring global frame features in the uncompressed videos, while the free of charge and critical saliency information already encoded in the compressed videos is generally neglected. We propose a video captioning method which operates directly on the stored compressed videos. To learn a discriminative visual representation for video captioning, we design a residuals-assisted encoder (RAE), which spots regions of interest in I-frames under the assistance of the residuals frames. First, we obtain the spatial attention weights by extracting features of residuals as the saliency value of each location in I-frame and design a spatial attention module to refine the attention weights. We further propose a temporal gate module to determine how much the attended features contribute to the caption generation, which enables the model to resist the disturbance of some noisy signals in the compressed videos. Finally, Long Short-Term Memory is utilized to decode the visual representations into descriptions. We evaluate our method on MSR-VTT dataset and demonstrate the effectiveness of our approach. © 2021 ieee.

关键词： Video signal processing

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Computer-aided Selection of the Optimal Gamma Correction Parameter for image Enhancement in Video Analytics Systems 11

Computer-aided Selection of the Optimal Gamma Correction Par...

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11th ieee International conference on Intelligent Data Acquisition and Advanced Computing Systems - Technology and Applications (IDAACS)

作者： Golovin, Oleksandr NAS Ukraine VM Glushkov Inst Cybernet Glushkov Ave 40 UA-03187 Kiev Ukraine

ISBN: (纸本)9781665426053

Video analytics systems are rapidly evolving, and the effectiveness of their work depends on the quality of operations at the initial level of the entire processing process, namely the quality of segmentation of objects in the scene and their recognition. Successful performance of these procedures is primarily due to image quality, which depends on many factors: technical parameters of video sensors, low or uneven lighting, changes in lighting levels of the scene due to weather conditions, time changes in illumination, or changes in scenarios in the scene. A novel method for determining the optimal value of a gamma correction parameter, which ensures the selection of the image of the best quality in automatic mode is represented in the paper. The method is based on the use of gamma correction, which reflects properties of a human visual system, effectively reduces the negative impact of changes in scene illumination and due to simple adjustment and effective implementation is widely used in practice. The technique of selection in an automatic mode of the optimum value of the gamma parameter at which the corrected image reaches the maximum quality is developed.

关键词： gamma correction image enhancement video analytics system gamma parameter histogram computer vision segmentation

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