2022 International Conference on Machine Vision and Image Processing (MVIP)最新文献_第2页

Feature Line Based Feature Reduction of Polarimetric-Contextual Feature Cube for Polarimetric SAR Classification 基于特征线的极化-上下文特征立方特征约简的极化SAR分类

2022 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2022-02-23 DOI: 10.1109/MVIP53647.2022.9738772

M. Imani

{"title":"Feature Line Based Feature Reduction of Polarimetric-Contextual Feature Cube for Polarimetric SAR Classification","authors":"M. Imani","doi":"10.1109/MVIP53647.2022.9738772","DOIUrl":"https://doi.org/10.1109/MVIP53647.2022.9738772","url":null,"abstract":"Extraction of discriminative features is an efficient step in any classification problem such as synthetic aperture radar (SAR) images classification. Polarimetric SAR (PolSAR) images with rich spatial features in two first dimensions and polarimetric characteristics in the third dimension are rich source of information for providing classification maps from the ground surface. By applying the spatial operators such as morphological filters by reconstruction, data dimensionality of the PolSAR is increased and needs feature reduction. In this work, median-mean and feature line embedding (MMFLE) is proposed for dimensionality reduction of the polarimetric-contextual cube in PolSAR images. MMFLE is stable with respect to outliers by utilizing the median-mean line metric. By an appropriate definition of scatter matrices, MMFLE maximizes the class separability. In addition, MMFLE is specially a superior feature reduction method when a small training set is available because it uses the feature line metric to model the data variations and generate virtual samples. With 10 training samples per class, MMFLE achieves 94.15% and 83.01% overall classification accuracy, respectively in Flevoland and SanFranciso PolSAR datasets acquired by AIRSAR.","PeriodicalId":184716,"journal":{"name":"2022 International Conference on Machine Vision and Image Processing (MVIP)","volume":"135 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116176199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Novel Gaussian Mixture-based Video Coding for Fixed Background Video Streaming 基于高斯混合的固定背景视频流视频编码

2022 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2022-02-23 DOI: 10.1109/MVIP53647.2022.9738789

Mohammadreza Ghafari, A. Amirkhani, E. Rashno, Shirin Ghanbari

{"title":"Novel Gaussian Mixture-based Video Coding for Fixed Background Video Streaming","authors":"Mohammadreza Ghafari, A. Amirkhani, E. Rashno, Shirin Ghanbari","doi":"10.1109/MVIP53647.2022.9738789","DOIUrl":"https://doi.org/10.1109/MVIP53647.2022.9738789","url":null,"abstract":"In recent years, tremendous advances have been made in Artificial Intelligence (AI) algorithms in the field of image processing. Despite these advances, video compression using AI algorithms has always faced major challenges. These challenges often lie in two areas of higher processing load in comparison with traditional video compression methods, as well as lower visual quality in video content. Careful study and solution of these two challenges is the main motivation of this article that by focusing on them, we have introduced a new video compression based on AI. Since the challenge of processing load is often present in online systems, we have examined our AI video encoder in video streaming applications. One of the most popular applications of video streaming is traffic cameras and video surveillance in road environments which here we called it CCTVs. Our idea in this type of system goes back to fixed background images, where always occupied the bandwidth not efficiently, and the streaming video is related to duplicate background images. Our AI-based video encoder detects fixed background and caches it at the client-side by the background subtraction method. By separating the background image from the moving objects, it is only enough to send the moving objects to the destination, which can save a lot of network bandwidth. Our experimental results show that, in exchange for an acceptable reduction in visual quality assessment, the video compression processing load will be drastically reduced.","PeriodicalId":184716,"journal":{"name":"2022 International Conference on Machine Vision and Image Processing (MVIP)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114919309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

I-GANs for Synthetical Infrared Images Generation 用于合成红外图像生成的i - gan

2022 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2022-02-23 DOI: 10.1109/MVIP53647.2022.9738551

Mohammad Mahdi Moradi, R. Ghaderi

{"title":"I-GANs for Synthetical Infrared Images Generation","authors":"Mohammad Mahdi Moradi, R. Ghaderi","doi":"10.1109/MVIP53647.2022.9738551","DOIUrl":"https://doi.org/10.1109/MVIP53647.2022.9738551","url":null,"abstract":"Due to the insensitivity of infrared images to changes in light intensity and weather conditions, these images are used in many surveillance systems and different fields. However, despite all the applications and benefits of these images, not enough data is available in many applications due to the high cost, time-consuming, and complicated data preparation. Two deep neural networks based on Conditional Generative Adversarial Networks are introduced to solve this problem and produce synthetical infrared images. One of these models is only for problems where the pair to pair visible and infrared images are available, and as a result, the mapping between these two domains will be learned. Given that in many of the problems we face unpaired data, another network is proposed in which the goal is to obtain a mapping from visible to infrared images so that the distribution of synthetical infrared images is indistinguishable from the real ones. Two publicly available datasets have been used to train and test the proposed models. Results properly demonstrate that the evaluation of the proposed system in regard to peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) has improved by 4.6199% and 3.9196%, respectively, compared to previous models.","PeriodicalId":184716,"journal":{"name":"2022 International Conference on Machine Vision and Image Processing (MVIP)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121999455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Unbiased Variable Windows Size Impulse Noise Filter using Genetic Algorithm 基于遗传算法的无偏变窗大小脉冲噪声滤波

2022 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2022-02-23 DOI: 10.1109/MVIP53647.2022.9738757

Mehdi Sadeghibakhi, Seyed Majid Khorashadizadeh, Reza Behboodi, A. Latif

引用次数: 0

A Low Area and Low Power Pulse Width Modulation Based Digital Pixel Sensor 基于低面积低功率脉宽调制的数字像素传感器

2022 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2022-02-23 DOI: 10.1109/MVIP53647.2022.9738738

E. Talebi, S. Sayedi

引用次数: 0

Evaluation of the Image Processing Technique in Interpretation of Polar Plot Characteristics of Transformer Frequency Response 变压器频率响应极坐标特征解释中的图像处理技术评价

2022 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2022-02-23 DOI: 10.1109/MVIP53647.2022.9738771

Ahmad Vosoughi, Mohammad Hamed Samimi

{"title":"Evaluation of the Image Processing Technique in Interpretation of Polar Plot Characteristics of Transformer Frequency Response","authors":"Ahmad Vosoughi, Mohammad Hamed Samimi","doi":"10.1109/MVIP53647.2022.9738771","DOIUrl":"https://doi.org/10.1109/MVIP53647.2022.9738771","url":null,"abstract":"Frequency response analysis (FRA) is one of the most efficient methods that can diagnose the mechanical faults of power transformers. Digital image processing of the FRA polar plot characteristics has been recently proposed in the literature for the interpretation of power transformers Frequency response. The important advantage of this method is using the phase angle of the FRA trace in addition to its amplitude for the analysis. The digital image processing techniques implemented on the FRA polar plot detect the fault by extracting and analyzing different features of the image by using texture analysis. In this study, the performance of this method is investigated on real windings to examine its ability in detecting the fault extent and type. This step is mandatory since the method is new in the FRA field and has been investigated only in simulation cases. Three different faults, including axial displacement, disk space variation, and radial deformation, are implemented in the experimental setup for the study. Results of implementation of this approach show that this approach neither can determine the fault extent nor the fault type. Therefore, essential changes need to be implemented in the method before applying it in the field.","PeriodicalId":184716,"journal":{"name":"2022 International Conference on Machine Vision and Image Processing (MVIP)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114707165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Designing an Improved Deep Learning-Based Classifier for Breast Cancer Identification in Histopathology Images 设计一种改进的基于深度学习的乳腺癌组织病理学图像识别分类器

2022 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2022-02-23 DOI: 10.1109/MVIP53647.2022.9738774

Amirreza BabaAhmadi, Sahar Khalafi, Fatemeh Malekipour Esfahani

{"title":"Designing an Improved Deep Learning-Based Classifier for Breast Cancer Identification in Histopathology Images","authors":"Amirreza BabaAhmadi, Sahar Khalafi, Fatemeh Malekipour Esfahani","doi":"10.1109/MVIP53647.2022.9738774","DOIUrl":"https://doi.org/10.1109/MVIP53647.2022.9738774","url":null,"abstract":"Cancer is a rampant phenomenon caused by uncontrollable cells that grow and spread throughout the body. Invasive Ductal Carcinoma 1 is the most common type of breast cancer, which can be fatal for females if not detected early. As a result, prompt diagnosis is critical to maximizing surveillance rates and, in the meantime, minimizing long-term mortality rates. Nowadays, modern computer vision and deep learning techniques have transformed the medical image analysis arena. Computer vision application in medical image analysis has provided us with remarkable results, enhanced accuracy, and reduced costs. The main purpose of designing a new algorithm to detect unusual patches of breast images, was to acquire both high accuracy and low computational cost, simultaneously. Therefore, a novel architecture has been designed by utilizing Xception and MobileNetV2.This new algorithm achieves 93.4% balanced accuracy and 94.8% for F1-Score, which outperforms previously published algorithms for identifying IDC histopathology images that use deep learning techniques.","PeriodicalId":184716,"journal":{"name":"2022 International Conference on Machine Vision and Image Processing (MVIP)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125132645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

An Empirical Study of the Performance of Different Optimizers in the Deep Neural Networks 深度神经网络中不同优化器性能的实证研究

2022 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2022-02-23 DOI: 10.1109/MVIP53647.2022.9738743

A. Zohrevand, Z. Imani

{"title":"An Empirical Study of the Performance of Different Optimizers in the Deep Neural Networks","authors":"A. Zohrevand, Z. Imani","doi":"10.1109/MVIP53647.2022.9738743","DOIUrl":"https://doi.org/10.1109/MVIP53647.2022.9738743","url":null,"abstract":"In recent years, the Stochastic Gradient Descent (SGD) has been commonly used as an optimizer in the Conventional Neural Network (CNN) models. While many researchers have adopted CNN models to classify tasks, to the best of our knowledge, different optimizers developed for CNN have not been thoroughly studied and analyzed in the training CNNs. In this paper, attempts have been made to investigate the effects of the various optimizers on the performance of CNN. Two sets of experiments are conducted. First, for the classification of the records on the CIFAR10, MNIST, and Fashion MNIST datasets, a well-known CNN called VGG11 is trained from scratch by four different kinds of optimizers including SGD, Adam, Adadelta, and AdaGrad. Second, by the same four optimizers, a popular CNN architecture called AlexNet is fine-tuned to classify the Persian handwritten words. In both experiments, the results showed that Adam and AdaGrad have a relatively similar behavior and higher performance in comparison to the other two optimizers in terms of training cost and recognition accuracy. Also, the effect of different values of the initial learning rate on the performance of the Adam optimizer is investigated experimentally. The result revealed that lower values lead to converges more rapidly.","PeriodicalId":184716,"journal":{"name":"2022 International Conference on Machine Vision and Image Processing (MVIP)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129564160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

DABA-Net: Deep Acceleration-Based AutoEncoder Network for Violence Detection in Surveillance Cameras DABA-Net:基于深度加速的自动编码器网络用于监控摄像机中的暴力检测

2022 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2022-02-23 DOI: 10.1109/MVIP53647.2022.9738791

Tahereh Zarrat Ehsan, M. Nahvi, Seyed Mehdi Mohtavipour

{"title":"DABA-Net: Deep Acceleration-Based AutoEncoder Network for Violence Detection in Surveillance Cameras","authors":"Tahereh Zarrat Ehsan, M. Nahvi, Seyed Mehdi Mohtavipour","doi":"10.1109/MVIP53647.2022.9738791","DOIUrl":"https://doi.org/10.1109/MVIP53647.2022.9738791","url":null,"abstract":"Violent crime is one of the main reasons for death and mental disorder among adults worldwide. It increases the emotional distress of families and communities, such as depression, anxiety, and post-traumatic stress disorder. Automatic violence detection in surveillance cameras is an important research area to prevent physical and mental harm. Previous human behavior classifiers are based on learning both normal and violent patterns to categorize new unknown samples. There are few large datasets with various violent actions, so they could not provide sufficient generality in unseen situations. This paper introduces a novel unsupervised network based on motion acceleration patterns to derive and abstract discriminative features from input samples. This network is constructed from an AutoEncoder architecture, and it is required only to use normal samples in the training phase. The classification has been performed using a one-class classifier to specify violent and normal actions. Obtained results on Hockey and Movie datasets showed that the proposed network achieved outstanding accuracy and generality compared to the state-of-the-art violence detection methods.","PeriodicalId":184716,"journal":{"name":"2022 International Conference on Machine Vision and Image Processing (MVIP)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117008885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Video Denoising using Temporal Coherency of Video Frames and Sparse Representation 基于视频帧时间相干和稀疏表示的视频去噪

2022 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2022-02-23 DOI: 10.1109/MVIP53647.2022.9738770

Azadeh Torkashvand, A. Behrad

引用次数: 0