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Research on Auto-focusing Method Based on Pulse Coupled Neural Network 4

Research on Auto-focusing Method Based on Pulse Coupled Neur...

引用

2021 4th international conference on advanced algorithms and Control Engineering, ICAACE 2021

作者： Chen, Taoyu Wang, Huawei Cao, Jianzhong Xi'An Institute of Optics and Precision Mechanics Chinese Academy of Sciences Xi'an Shanxi710119 China

The key to automatic focusing method based on image processing is to construct a fast and accurate focus quality evaluation function. The method in this article introduces Pulse Coupled Neural Network processing. Using the characteristics of the network, through the enhanced image, the grayscale sum can be conveniently used to characterize clarity, thereby simplifying calculations and enhancing the real-time performance of focus control. © Published under licence by IOP Publishing Ltd.

关键词： image enhancement

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A LIGHTWEIGHT RELU-BASED FEATURE FUSION FOR AERIAL SCENE CLASSIFICATION

A LIGHTWEIGHT RELU-BASED FEATURE FUSION FOR AERIAL SCENE CLA...

引用

IEEE international conference on image processing (ICIP)

作者： Arefeen, Md Adnan Nimi, Sumaiya Tabassum Uddin, Md Yusuf Sarwar Li, Zhu Univ Missouri Kansas City Dept EECS Kansas City MO 64110 USA

ISBN: (纸本)9781665441155

In this paper, we propose a transfer-learning based model construction technique for the aerial scene classification problem. The core of our technique is a layer selection strategy, named ReLU-Based Feature Fusion (RBFF), that extracts feature maps from a pretrained CNN-based single-object image classification model, namely MobileNetV2, and constructs a model for the aerial scene classification task. RBFF stacks features extracted from the batch normalization layer of a few selected blocks of MobileNetV2, where the candidate blocks are selected based on the characteristics of the ReLU activation layers present in those blocks. The feature vector is then compressed into a low-dimensional feature space using dimension reduction algorithms on which we train a low-cost SVM classifier for the classification of the aerial images. We validate our choice of selected features based on the significance of the extracted features with respect to our classification pipeline. RBFF remarkably does not involve any training of the base CNN model except for a few parameters for the classifier, which makes the technique very cost-effective for practical deployments. The constructed model despite being lightweight outperforms several recently proposed models in terms of accuracy for a number of aerial scene datasets.

关键词： Layer selection Aerial image analysis Transfer learning Feature fusion image classification

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Algorithm of infrared-polarization image fusion based on fireworks algorithm

Algorithm of infrared-polarization image fusion based on fir...

引用

2021 international conference on signal image processing and Communication, ICSIPC 2021

作者： Yang, Zhiyong Lu, Gaoxiang Cai, Wei Qiao, Danqiu Song, Junchen Xi'an Research Institute of High-tech Shannxi Xi'an710025 China

ISBN: (数字)9781510645417

ISBN: (纸本)9781510645400

Because of the shortcomings of traditional infrared-polarization image fusion algorithm, such as low intelligence and single optimization index, this paper proposes an intelligent infrared-polarization image fusion optimization algorithm based on fireworks algorithm. Based on the strong complementarity between infrared-intensity image and degree of linearpolarization (DOLP) image and the explosive optimization of fireworks algorithm, the problem model of weighted fusion algorithm is established, and the fitness function based on root mean square error (RMSE) is constructed to calculate the optimal weight of source image. In the fusion experiment of long-wave infrared-intensity image and DOLP image, this method is compared with the common fusion algorithms. The results show that this method can effectively fuse the infrared-intensity and degree of polarization information, and the evaluation indexes of standard deviation, spatial frequency, mutual information, structural similarity, peak signal-to-noise ratio and information entropy of the fusion image are better than the comparison algorithm. In the future, cooperated with the long-wave infrared-polarization imaging system, this method can be applied to improve the infrared detection ability in complex environment. © 2021 SPIE.

关键词： Explosives

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PUZZLE-CAM: IMPROVED LOCALIZATION VIA MATCHING PARTIAL AND FULL FEATURES

PUZZLE-CAM: IMPROVED LOCALIZATION VIA MATCHING PARTIAL AND F...

引用

IEEE international conference on image processing (ICIP)

作者： Jo, Sanghyun Yu, In-Jae GYNetworks Incheon South Korea Korea Adv Inst Sci & Technol Sch Comp Daejeon South Korea

ISBN: (纸本)9781665441155

Weakly-supervised semantic segmentation (WSSS) is introduced to narrow the gap for semantic segmentation performance from pixel-level supervision to image-level supervision. Most advanced approaches are based on class activation maps (CAMs) to generate pseudo-labels to train the segmentation network. The main limitation of WSSS is that the process of generating pseudo-labels from CAMs that use an image classifier is mainly focused on the most discriminative parts of the objects. To address this issue, we propose Puzzle-CAM, a process that minimizes differences between the features from separate patches and the whole image. Our method consists of a puzzle module and two regularization terms to discover the most integrated region in an object. Puzzle-CAM can activate the overall region of an object using image-level supervision without requiring extra parameters. In experiments, Puzzle-CAM outperformed previous state-of-the-art methods using the same labels for supervision on the PASCAL VOC 2012 dataset. Code associated with our experiments is available at https://***/OFRIN/PuzzleCAM.

关键词： image segmentation Deep learning Neural Networks Weakly-supervised semantic segmentation

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NUCLEAR DENSITY DISTRIBUTION FEATURE FOR IMPROVING CERVICAL HISTOPATHOLOGICAL imageS RECOGNITION

NUCLEAR DENSITY DISTRIBUTION FEATURE FOR IMPROVING CERVICAL ...

引用

IEEE international conference on image processing (ICIP)

作者： Wang, Zhuangzhuang Yang, Mengning Lyu, Yangfan Chen, Kairun Tang, Qicheng Chongqing Univ Sch Big Data & Software Engn Chongqing Peoples R China Army Med Univ Affiliated Hosp 2 Dept Pathol Chongqing Peoples R China

ISBN: (纸本)9781665441155

Cervical carcinoma is a common type of cancer in the female reproductive system. Early detection and diagnosis can facilitate immediate treatment and prevent progression of the disease. However, in order to achieve better performance, DL-based algorithms just stack various layers with low interpretability. In this paper, a robust and reliable Nuclear Density Distribution Feature (NDDF) based on priors of the pathologists to promote the Cervical Histopathological image Classification (CHIC) is proposed. Our proposed method combines the nucleus mask segmented by U-Net with the segmentation grid-lines generated from pathology images utilizing SLIC to obtain the NDDF map, which contains information about the morphology, size, number, and spatial distribution of nuclei. The result shows that the proposed model trained with NDDF maps has better performance and accuracy than that trained on RGB images (patch-level histopathological images). More significantly, the accuracy of the two-stream network trained with RGB images and NDDF maps is steadily improved over the corresponding baselines of different complexity.

关键词： Nuclear Density Distribution Superpixel Deep Learning Histopathological image Classification

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BUILDING FOOTPRINT GENERATION BY INTEGRATING U-NET WITH DEEPENED SPACE MODULE

BUILDING FOOTPRINT GENERATION BY INTEGRATING U-NET WITH DEEP...

引用

IEEE international conference on image processing (ICIP)

作者： Chen, Jun Jiang, Yuxuan Luo, Linbo Gu, Yue Wu, Kangle China Univ Geosci Sch Automat Wuhan Peoples R China Hubei Key Lab Adv Control & Intelligent Automat C Wuhan Peoples R China Minist Educ Engn Res Ctr Intelligent Technol Geoexplorat Wuhan Peoples R China China Univ Geosci Sch Mech Engn & Elect Informat Wuhan Peoples R China

ISBN: (纸本)9781665441155

In this paper, we propose a novel and practical convolutional neural network method for building footprint generation in remote sensing images, in order to deal with the problem that the detailed information and geometric structure of ground objects in high-resolution images become more abundant, which leads to a large increase in the calculation amount. So we introduce a deepened space module, which can ignore the channels with weak target features and emphasize the effective features. It is embedded in each splicing layer in the upsampling process of U-net to achieve the effect of feature selection. By means of clipping and data enhancement, we carry out iterative training and model optimization learning on Inria aerial image label dataset, and realize the automatic generation of building footprint. Compared with FCN8s, Unet, SegNet, PSPNet, Deeplabv3+ and GLNet, experimental results show that the method we use to generate building footprint is more accurate, and in IoU, mPA, PA three indicators are better than the comparison algorithms.

关键词： deep learning semantic segmentation high resolution image

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OPENING DEEP NEURAL NETWORKS WITH GENERATIVE MODELS

OPENING DEEP NEURAL NETWORKS WITH GENERATIVE MODELS

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IEEE international conference on image processing (ICIP)

作者： Vendramini, Marcos Oliveira, Hugo Machado, Alexei dos Santos, Jefersson A. Univ Fed Minas Gerais Dept Comp Sci Belo Horizonte MG Brazil Univ Sao Paulo Inst Math & Stat Sao Paulo Brazil Univ Fed Minas Gerais Dept Anat & Imaging Belo Horizonte MG Brazil Pontificia Univ Catolica Minas Gerais Dept Comp Sci Belo Horizonte MG Brazil

ISBN: (纸本)9781665441155

image classification methods are usually trained to perform predictions taking into account a predefined group of known classes. Real-world problems, however, may not allow for a full knowledge of the input and label spaces, making failures in recognition a hazard to deep visual learning. Open set recognition methods are characterized by the ability to correctly identifying inputs of known and unknown classes. In this context, we propose GeMOS: simple and plug-and-play open set recognition modules that can be attached to pre-trained Deep Neural Networks for visual recognition. The GeMOS framework pairs pre-trained Convolutional Neural Networks with generative models for open set recognition to extract open set scores for each sample, allowing for failure recognition in object recognition tasks. We conduct a thorough evaluation of the proposed method in comparison with state-of-the-art open set algorithms, finding that GeMOS either outperforms or is statistically indistinguishable from more complex and costly models.

关键词： Open Set Recognition image Classification Deep Visual Learning Out-of-Distribution Detection

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Garbage Classification System with YOLOV5 Based on image Recognition 6

Garbage Classification System with YOLOV5 Based on Image Rec...

引用

6th international conference on signal and image processing, ICSIP 2021

作者： Yang, Guanhao Jin, Jintao Lei, Qujiang Wang, Yi Zhou, Jiangkun Sun, Zhe Li, Xiuhao Wang, Weijun Intelligent Robot and Equipment Center Guangzhou Institute of Advanced Technology Chinese Academy of Sciences Guangzhou511458 China University of Chinese Academy of Sciences Beijing100039 China

ISBN: (纸本)9780738133737

At present, the main technology of garbage identification and classification is the use of traditional machine vision algorithm or the use of sensors for screening and identification of garbage, in garbage sorting, the first accurate identification and classification of garbage is very necessary. By collecting various types of garbage pictures and building detection data sets, we adopt the garbage identification and detection algorithm based on YOLO-V5 and use data enhancement to improve the robustness of the model, to achieve fast and accurate identification of different types of garbage. Experimental results show that this method has high accuracy, short time consumption, and good robustness. © 2021 IEEE.

关键词： Computer vision

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R3L: CONNECTING DEEP REINFORCEMENT LEARNING TO RECURRENT NEURAL NETWORKS FOR image DENOISING VIA RESIDUAL RECOVERY

R3L: CONNECTING DEEP REINFORCEMENT LEARNING TO RECURRENT NEU...

引用

IEEE international conference on image processing (ICIP)

作者： Zhang, Rongkai Zhu, Jiang Zha, Zhiyuan Dauwels, Justin Wen, Bihan Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore Delft Univ Technol Dept Microelect Delft Netherlands

ISBN: (纸本)9781665441155

State-of-the-art image denoisers exploit various types of deep neural networks via deterministic training. Alternatively, very recent works utilize deep reinforcement learning for restoring images with diverse or unknown corruptions. Though deep reinforcement learning can generate effective policy networks for operator selection or architecture search in image restoration, how it is connected to the classic deterministic training in solving inverse problems remains unclear. In this work, we propose a novel image denoising scheme via Residual Recovery using Reinforcement Learning, dubbed R3L. We show that R3L is equivalent to a deep recurrent neural network that is trained using a stochastic reward, in contrast to many popular denoisers using supervised learning with deterministic losses. To benchmark the effectiveness of reinforcement learning in R3L, we train a recurrent neural network with the same architecture for residual recovery using the deterministic loss, thus to analyze how the two different training strategies affect the denoising performance. With such a unified benchmarking system, we demonstrate that the proposed R3L has better generalizability and robustness in image denoising when the estimated noise level varies, comparing to its counterparts using deterministic training, as well as various state-of-the-art image denoising algorithms.

关键词： Recurrent Neural Network Deep Reinforcement Learning image Denoising Residual Recovery

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5th EAI international conference on advanced Hybrid Information processing, ADHIP 2021

5th EAI International Conference on Advanced Hybrid Informat...

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5th EAI international conference on advanced Hybrid Information processing, ADHIP 2021

ISBN: (纸本)9783030945503

The proceedings contain 94 papers. The special focus in this conference is on advanced Hybrid Information processing. The topics include: A Dynamic Monitoring Method for Marathon Athletes Based on Wireless Sensor Network;research on Key Technologies of Analysis of User Emotion Fluctuation Characteristics in Wireless Network Based on Social Information processing;Variable Bandwidth Receiving Method of Civil Aircraft Radar signal Based on FPGA Technology;recognition of Aerobics Movement Posture Based on Multisensor Movement Monitoring;a Real-Time Detection Algorithm for Abnormal Users in Multi Relationship Social Networks Based on Deep Neural Network;research on Personalized Recommendation Algorithm Based on Mobile Social Network Data;building Construction Information Real-Time Sharing Method Based on 3D Scanning Technology and Social Network Analysis;a Mining Algorithm for Relevance of Business Administration Based on Complex Social Information Network;classification Method of Regional Differentiation Characteristics of Enterprise Management;design of Fractal image Coding Compression and Transmission Model Based on Wavelet Transform;research on Price Stickiness of Consumer Goods Based on Real-Time Social Information Flow;an Algorithm of Employment Resource Allocation for College Students Based on Social Network Mining;an Improved Mobilenetv3-Yolov5 Infrared Target Detection Algorithm Based on Attention Distillation;A Novel Method of Combined Window Function Construction Filter Applied to F-OFDM System;Passive Electromagnetic Field Positioning Method Based on BP Neural Network in Underwater 3-D Space;optimization of Cross-border E-commerce Marketing Strategy During the Pandemic;deep Residual Network with Transfer Learning for High Spatial Resolution Remote Sensing Scenes Classification;research on E-commerce Logistics Transportation Route Planning Method Based on Recurrent Neural Network.

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