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Object Detection Model Based on Dual visual Pathway

Object Detection Model Based on Dual Visual Pathway

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International conference on Wireless communications and Signal processing (WCSP)

作者： Ruixuan Chen Xiao Yao Yifeng Zeng The College of IoT Engineering Hohai University Changzhou Jiangsu China

ISBN: (纸本)9781665450867

Object detection has made great progress with the development of deep learning. However, existing object detection algorithms still have limitations compared with human brain. In addition, the interpretability of the model becomes more and more inefficient with the increasing accuracy of the model. Most of the object detection models are considered as “black box” that cannot be explained. Considering that the visual information processing system is composed of two pathways spatially distributed in the ventral and dorsal sides of the cerebral cortex when the brain detects objects, this paper proposes object detection model based on dual visual pathway by simulating the process of detecting objects in the brain. Intra layer recursion and remote connections of different modules are added to the feedforward network to achieve feature extraction for image, since the ventral flow pathway is a recurrent network composed of loops and feedback. Experimental results carried out on PASCAL VOC show that the performance of object detection has been further improved, and the interpretability of the model has been enhanced.

关键词： Wireless communication Deep learning visualization Object detection Information processing visual systems Signal processing

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Clothing Retrieval from Channel Attention to KN Loss Learning

Clothing Retrieval from Channel Attention to KN Loss Learnin...

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IEEE visual communications and image processing (VCIP)

作者： Kuan-Hsien Liu Yu-Hsiang Wu Tsung-Jung Liu National Taichung University of Science and Technology Taichung Taiwan National Chung Hsing University Taichung Taiwan

ISBN: (纸本)9781665475938

Due to the high diversity of clothing types and names, using text to search may not find the desired clothes. Clothing retrieval via images can help users find their favorite clothes more conveniently. So, it is widely used in many online stores and platforms, and gradually replaces text search. Clothing retrieval performance is often affected due to factors such as shooting angle, occlusion, and lighting. Therefore, we propose a low-parameter clothing retrieval model without using any additional annotations or labels to aid learning. In the model, we use the classification capability of the Capsule Network with multiple residual convolution modules to learn features, and the VGG16 network is used as a clothing feature extractor to help the model learn features such as clothing color and texture. We add different attention mechanisms to the model for feature enhancement and design a new embedding method. We also adjust the loss function to sample multiple negative classes to improve the similarity clustering effect, and add weights to strengthen it. From the experimental results, one can find that our proposed method achieves better retrieval accuracy than several state-of-the-art models.

关键词： Weight measurement Training image color analysis Annotations visual communication Convolution Clothing

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Stereo image Quality Assessment Considering the Asymmetry of Statistical Information in Early visual Pathway

Stereo Image Quality Assessment Considering the Asymmetry of...

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

作者： Chang, Yongli Li, Sumei Ma, Li Jin, Jie Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China

ISBN: (纸本)9781728180687

The design of stereo image quality assessment (SIQA) methods cannot be well based on the biological theory of human vision, so the performance of many SIQA methods cannot achieve good consistency with the subjective perception. The research on the visual system tends to the dorsal and ventral pathways, which ignores the information asymmetry in the early visual pathways. It is worth noting that the ON and OFF receptive fields in retinal ganglion cells (RGCs) respond asymmetrically to the statistical features of images. Inspired by this, we propose a SIQA method based on monocular and binocular visual features, which takes into account the asymmetry of local contrast bright and dark features in early visual pathways. First, this paper extracts the response maps of ON and OFF cell in RGCs to left and right views respectively. And then the different information fusion modes of visual cortex are used to fuse the response maps information of left and right views. Final, monocular and binocular features were extracted and sent to support vector regression (SVR) for quality regression. Experimental results show that the proposed method is superior to several mainstream SIQA metrics on two publicly available databases.

关键词： stereo image quality assessment retinal ganglion cells ON and OFF receptive fields

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Knowledge-Experience Graph with Denoising Autoencoder for Zero-Shot Learning in visual Cognitive Development 27th

Knowledge-Experience Graph with Denoising Autoencoder for Ze...

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27th International conference on Neural Information processing

作者： Zhang, Xinyue Yang, Xu Liu, Zhiyong Zhang, Lu Ren, Dongchun Fan, Mingyu Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Meituan Dianping Grp Beijing 100190 Peoples R China

ISBN: (纸本)9783030638221;9783030638238

visual cognitive development is vital for intelligent robots to handle various types of visual tasks rather than predefined ones. It can transfer the classification ability from an original model to a novel task. However, the high reliance on large amounts of data hinders its development. The energy it costs to adjust to the novel tasks is also a tough problem. Thus we propose a model called knowledge-experience graph (KEG) to imitate the mechanisms of human brains. With the help of social knowledge stored in the knowledge graph, the novel classes can be easily added. The combination of the experience via denoising autoencoder (DAE) also takes the relationship in the visual space into account. With the propagation of information among the graph by graph convolutional network (GCN), KEG generates the classifier of the novel tasks effectively. Experiments show that KEG improves the classification accuracy of novel categories on zero-shot learning and accomplishes visual cognitive development to a certain extent.

关键词： GCN Zero-shot learning Cross-task learning Cognitive development image classification Denoising autoencoder

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image Augmentation for Improving Automated Eligibility-Classification for Cervical Precancer Ablation Treatment 4th

Image Augmentation for Improving Automated Eligibility-Class...

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4th International conference on Recent Trends in image processing and Pattern Recognition, RTIP2R 2021

作者： Guo, Peng Xue, Zhiyun Jeronimo, Jose Gage, Julia C. Desai, Kanan T. Befano, Brian García, Francisco Schiffman, Mark Antani, Sameer National Library of Medicine BethesdaMD20894 United States National Cancer Institutes RockvilleMD20850 United States Information Management Services Inc. RockvilleMD20850 United States Pima County TucsonAZ85701 United States

ISBN: (纸本)9783031070044

Cervical tissue ablation is an effective treatment approach for excising high-grade precancerous lesions, which are a direct precursor to invasive cervical cancer. However, not all women are eligible for this ablative treatment due to their cervical characteristics. In our previous study, we presented a deep learning network that used pyramidal features to determine if a cervix is eligible for ablative treatment based on visual characteristics presented in the image. Our method demonstrated promising performance and valid visualization in the task of "treatability classification". In this work, we propose using an image augmenter followed by a customized classification convolutional neural network (CNN) to overcome the challenges due to insufficient training data. We build the image augmenter using a CycleGAN model that is trained using three different datasets to ensure that the augmented images contain clinically significant morphological features. A gynecologic oncologist with more than 20 years of experience validated the augmented images. These are mixed into the set of original images to train our customized CNN. We note a performance improvement of 3.3% (to 89.8%) in treatability classification. We believe that a similar technique can also be applied to other automatic image classification applications for cervical cancer screening. © 2022, This is a U.S. government work and not under copyright protection in the U.S.;foreign copyright protection may apply.

关键词： Classification (of information)

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6th International conference on Microelectronics, Electromagnetics, and Telecommunications, ICMEET 2021

6th International Conference on Microelectronics, Electromag...

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6th International conference on Microelectronics, Electromagnetics, and Telecommunications, ICMEET 2021

ISBN: (纸本)9789811685538

The proceedings contain 50 papers. The special focus in this conference is on Microelectronics, Electromagnetics, and Telecommunications. The topics include: Optical Letter Recognition for Roman-Text;intelligent Noise Detection and Correction with Kriging on Fundus images of Diabetic Retinopathy;a Nanoplasmonic Ultra-wideband Antenna for Wireless communications;antenna Array Synthesis of Shaped Beam Using Deterministic Method;a Novel Semi-blind Digital image Watermarking Using Fire Fly Algorithm;Real-Time image Enhancement Using DCT Techniques for Video Surveillance;performance Analysis of Automatic Modulation Recognition Using Convolutional Neural Network;design of Wearable Microstrip Patch Antenna for Biomedical Application with a Metamaterial;estimation of Gender Using Convolutional Neural Network;automatic Modulation Recognition of Analog Modulation Signals Using Convolutional Neural Network;taxonomy on Breast Cancer Analysis Using Neural Networks;a Novel Cuckoo Search with Levy Distribution-Optimized Density-Based Clustering Model on MapReduce for Big Data Environment;drowsiness Detection System for Drivers Using 68 Coordinate System;human Action Recognition in Videos Using Deep Neural Network;Performance Analysis of Underwater Acoustic Communication System with Massive MIMO-OFDM;360° Video Summarization: Research Scope and Trends;Construing Crop Health Dynamics Using UAV-RGB based SpaceTech Analytics and image processing;review of Different Binarization Techniques Used in Different Areas of image Analysis;An Improved Unsharp Masking (UM) Filter with GL Mask;on Performance Improvement of Wireless Push Systems Via Smart Antennas;classification of Non-fluctuating Radar Target Using ReliefF Feature Selection Algorithm;Design of Arrayed Rectangular Probe Patch Antenna at 6.2 GHz for 5G Small Cell Applications;visual Words based Static Indian Sign Language Alphabet Recognition using KAZE Descriptors;preface.

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The Enhancement of Underexposed images with Blurred Reflectance

The Enhancement of Underexposed Images with Blurred Reflecta...

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

作者： Zhou, Jinchao Wan, Renjie Li, Haoliang Kot, Alex C. Beihang Univ Sch Comp Sci & Engn Beijing Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore

ISBN: (纸本)9781728180687

The images captured in the low-light conditions always suffer from low visibility. Enhancing the visibility of the low-light image is of broad application to various computer vision tasks. Based on the classical Retinex model, previous methods assume the reflectance components as a well-exposed image. In this paper, we introduce the blurring distortion into the Retinex model to cover more general and challenging scenarios. We further propose a two-stage framework to extract the reflectance images and remove the blurring distortion separately. Specifically, we optimize the whole network by embedding a mechanism robust to the pixel misalignment in the training dataset. The experimental results show that our proposed method achieves promising results.

关键词： Low-light image blur illumination reflectance

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6th International conference on Microelectronics, Electromagnetics, and Telecommunications, ICMEET 2021

6th International Conference on Microelectronics, Electromag...

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6th International conference on Microelectronics, Electromagnetics, and Telecommunications, ICMEET 2021

ISBN: (纸本)9789811685491

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Edge-IoT Architecture-Based visualization Service Platform for RTO Real-Time Monitoring

Edge-IoT Architecture-Based Visualization Service Platform f...

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International conference on Advanced Communication Technology (ICACT)

作者： Hoon-Min Park Dal-Hwan Yoon Hyun-Min Jung Dae-Hee Lee Tae-Yeung Lim Hyun Kim Myung-Ki Jang Emsolution Co. Ltd Suwon Korea Department of Electronic Engineering Semyung University Korea Devicenet Co. Ltd Korea

ISBN: (数字)9791188428137

ISBN: (纸本)9798331507602

In this study, we present the Edge IoT Architecture (Architecture) for analyzing the real-time state information of the regenerative thermal oxidizer (RTO) and implement a visualization method. The Edge-IoT architecture system interfaces with the programmable logic control (PLC) that controls the monitoring of RTO operations and transmits real-time data to a remote server, analyzes the channel data received through the IoT to build a database, and visualizes the characteristics of database (DB) analysis. IoT architecture implementation for fault diagnosis and maintenance of RTO designs IoT circuits on PLC control panels that control monitoring. Compatibility and information security are considered to facilitate remote maintenance. The received data is segmented by each channel and analyzed by a visualization algorithm to detect anomalies in the segmented data. At this time, the visualized data is judged to be normal, insufficient, or warning, depending on the threshold.

关键词： image edge detection Data visualization Signal processing algorithms Signal processing Real-time systems Maintenance Thermal analysis visual databases Servers Monitoring

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Single image Dehazing with Optimal Color Channels and Nonlinear Transformation 8

Single Image Dehazing with Optimal Color Channels and Nonlin...

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8th IEEE International conference on communications and Electronics (IEEE ICCE)

作者： Le Thi Thanh Thanh, Dang N. H. Nguyen Ngoc Hien Erkan, Ugur Prasath, V. B. Surya Ho Chi Minh City Univ Transport Dept Basic Sci Ho Chi Minh City Vietnam Univ Econ Ho Chi Minh City Sch Business Informat Technol Dept Informat Technol Ho Chi Minh City Vietnam Dong Thap Univ Ctr Occupat Skills Dev Cao Lanh Dong Thap Vietnam Karamanoglu Mehmetbey Univ Dept Comp Engn TR-70200 Karaman Turkey Cincinnati Childrens Hosp Med Ctr Div Biomed Informat Cincinnati OH 45229 USA Univ Cincinnati Dept Pediat Cincinnati OH USA Univ Cincinnati Coll Med Dept Biotned Informat Cincinnati OH 45267 USA Univ Cincinnati Dept Elect Engn & Comp Sci Cincinnati OH 45221 USA

ISBN: (纸本)9781728154718

image dehazing is an important problem and it is useful as a preprocessing step in various automatic image analysis systems. The goal of image dehazing is the quality improvement of digital images by removing haze across the scene. In the present work, we consider an automatic image dehazing approach that is based on optimal color channels and nonlinear transformations. The proposed dehazing approach can remove haze fast and effectively with features preservation. In our experiments, we compare the image dehazing results with related image dehazing methods from the literature. visual assessments, as well as quantitative assessments, are also done to show the improvements obtained by the dehazing model across different natural images. Obtained experimental results indicate that the dehazing approach proposed hem performs better than other dehazing models in terms of overall better visual quality and higher blind image quality metric values.

关键词： Dehazing Defogging image Restoration image processing Histogram Equalization HSV Gamma Correction

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