2020 International Conference on Machine Vision and Image Processing (MVIP)最新文献_第2页

Improving Persian Digit Recognition by Combining Deep Neural Networks and SVM and Using PCA 结合深度神经网络和支持向量机及PCA改进波斯语数字识别

2020 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2020-02-01 DOI: 10.1109/MVIP49855.2020.9116893

Amir. M Mousavi. H, A. Bossaghzadeh

引用次数: 7

Performance Improvement of Gaussian Filter using SIMD Technology 利用SIMD技术改进高斯滤波器的性能

2020 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2020-02-01 DOI: 10.1109/MVIP49855.2020.9116883

Maryam Moradifar, A. Shahbahrami

{"title":"Performance Improvement of Gaussian Filter using SIMD Technology","authors":"Maryam Moradifar, A. Shahbahrami","doi":"10.1109/MVIP49855.2020.9116883","DOIUrl":"https://doi.org/10.1109/MVIP49855.2020.9116883","url":null,"abstract":"Denoising is an important process before applying other post-processing techniques on medical images. To obtain better quality images many denoising approaches have been introduced. Gaussian filter is a spatial domain filter, which is proper to blur and to remove noise from images. Since the Gaussian filter modifies the input signal by convolution with a Gaussian function it is a computationally intensive algorithm. Hence to enhance the performance of the algorithm, it is better to perform two 1-D convolution operations instead of one 2-D convolution operation and then parallelize it. In this paper in order to increase the performance of 1-D convolution operation, we exploit both Data- and Thread-Level Parallelism using parallel programming models such as Intrinsic Programming Model, Compiler's Automatic Vectorization and Open Multi-Processing. The experimental results were shown that the performance of our implementations is much higher than other approaches Performance improvements of Multi-threaded version of all implementations are significantly improved compared to single-core implementations, and a speedup of 52.33x obtained over the optimal scalar implementation.","PeriodicalId":255375,"journal":{"name":"2020 International Conference on Machine Vision and Image Processing (MVIP)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115932878","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: 4

Gaussian Soft Margin Angular Loss for Face Recognition 人脸识别的高斯软边缘角损失

2020 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2020-02-01 DOI: 10.1109/MVIP49855.2020.9116917

Bahman Rouhani, Alireza Samadzadeh, M. Rahmati, A. Nickabadi

{"title":"Gaussian Soft Margin Angular Loss for Face Recognition","authors":"Bahman Rouhani, Alireza Samadzadeh, M. Rahmati, A. Nickabadi","doi":"10.1109/MVIP49855.2020.9116917","DOIUrl":"https://doi.org/10.1109/MVIP49855.2020.9116917","url":null,"abstract":"Advances in deep learning has lead to drastic improvements in face recognition. A key part of these deep models is their loss function. Consequently developing an efficient and suitable loss function has been an important topic in face recognition in the recent years. Angular-margin-based losses achieve an acceptable performance and inter-class separability. However they are held back by their enforcement of hard margins on all the samples of the training dataset, regardless of whether these samples actually differ from all the other classes enough to enforce a margin. It can be argued that in a large enough dataset with many different settings and age gaps, some faces will look similar to the faces of other classes. In an intuitive and expressive embedding, we expect some faces to be embedded near similar classes with a small margin. Thus we propose a loss function that while maximizing the inter-class distance and intra-class compactness, allows for the samples which naturally reside further from class center to have a smaller margin. We implement an extremely light and fast to train model using MobileNets and achieve accuracy comparable to state of the art method.","PeriodicalId":255375,"journal":{"name":"2020 International Conference on Machine Vision and Image Processing (MVIP)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114484321","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

A Defect Image Enhancement Approach for Detection of Defective Area in CFRPs Through Local Defect Resonance 基于局部缺陷共振的CFRPs缺陷图像增强检测方法

2020 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2020-02-01 DOI: 10.1109/MVIP49855.2020.9116901

Saman Hadi, Reza P. R. Hasanzadeh, M. Kersemans

{"title":"A Defect Image Enhancement Approach for Detection of Defective Area in CFRPs Through Local Defect Resonance","authors":"Saman Hadi, Reza P. R. Hasanzadeh, M. Kersemans","doi":"10.1109/MVIP49855.2020.9116901","DOIUrl":"https://doi.org/10.1109/MVIP49855.2020.9116901","url":null,"abstract":"Nowadays composite materials such as carbon fiber reinforced polymers (CFRP)s have been widely used in industrial applications. But, they are susceptible to impact damage and subsequent fatigue cracking and delamination which in long term lead to some negative consequences such as erosion and also breaking the material. Due to the inability to visually observe such defects and also the high sensitivity of industrial components to invasive inspections, non-destructive testing (NDT) techniques are used to deal with the aforementioned problems. In this regards, an ultrasound-based NDT technique called Local defect resonance (LDR) leads to remarkable results for detecting various types of defects in CFRPs. In LDR technique, high frequency acoustical vibrations are used to get a localized resonant activation of a defective region such that these excitation frequencies lead to a significant increase of the vibration amplitude in the defective area relative to the sound area. The problem which arises is that in order to properly localize the defect, the defect resonance frequency must be known which is practically impossible. In this paper, a new defect imaging methodology is proposed, which can localize the defects without any prior knowledge about their location and resonance frequencies. Experiments are performed on a CFRP sample with flat bottom hole (FBH) defects and the proposed method has been quantitatively validated through the experiments by using the signal-to-noise ratio (SNR) criterion. The results show the superiority of our method over some well-known algorithms.","PeriodicalId":255375,"journal":{"name":"2020 International Conference on Machine Vision and Image Processing (MVIP)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124801679","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

A Retina-Inspired Multiresolution Analysis Framework for Pansharpening 视网膜启发的泛锐化多分辨率分析框架

2020 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2020-02-01 DOI: 10.1109/MVIP49855.2020.9116884

Mehran Maneshi, H. Ghassemian, Ghassem Khademi, M. Imani

{"title":"A Retina-Inspired Multiresolution Analysis Framework for Pansharpening","authors":"Mehran Maneshi, H. Ghassemian, Ghassem Khademi, M. Imani","doi":"10.1109/MVIP49855.2020.9116884","DOIUrl":"https://doi.org/10.1109/MVIP49855.2020.9116884","url":null,"abstract":"Technical limitations on the satellite sensors make a trade-off between the spectral and spatial resolution in remotely sensed images. To deal with this issue, pansharpening has been emerged to prepare a single image with the high spatial and spectral resolution, simultaneously. This paper presents a pansharpening approach based on the retina-inspired model and the multiresolution analysis (MRA) framework. The retina- inspired model is simplified by the difference of Gaussian (DoG) operator, and we apply it to the panchromatic image to extract the spatial details. Furthermore, the injection gains in the MRA framework are calculated through an iterative process where the gains at each iteration are updated based on the fusion result obtained from its previous iteration. To investigate the performance of the proposed model, it is compared with some classical pansharpening approaches with two data sets captured by the GeoEye-1 and Pléiades satellite imagery sensors. The experimental results show the proposed retina-inspired pansharpening method acts well in injecting the spatial information along with reducing the spectral distortion.","PeriodicalId":255375,"journal":{"name":"2020 International Conference on Machine Vision and Image Processing (MVIP)","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129626960","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: 4

Iranian License Plate Recognition using Deep Learning 伊朗车牌识别使用深度学习

2020 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2020-02-01 DOI: 10.1109/MVIP49855.2020.9116897

Atefeh Ranjkesh Rashtehroudi, A. Shahbahrami, Alireza Akoushideh

引用次数: 5

Using imaging Photoplethysmography (iPPG) Signal for Blood Pressure Estimation 利用成像光容积脉搏波(iPPG)信号估计血压

2020 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2020-02-01 DOI: 10.1109/MVIP49855.2020.9116902

Reza Heydari Goudarzi, Seyedeh Somayyeh Mousavi, M. Charmi

{"title":"Using imaging Photoplethysmography (iPPG) Signal for Blood Pressure Estimation","authors":"Reza Heydari Goudarzi, Seyedeh Somayyeh Mousavi, M. Charmi","doi":"10.1109/MVIP49855.2020.9116902","DOIUrl":"https://doi.org/10.1109/MVIP49855.2020.9116902","url":null,"abstract":"Blood Pressure (BP) is one of the most important vital signs of the human body, which its value provides valuable physiological information about cardiac function for physicians. In recent years, many types of research have been done in the field of BP estimation using Photoplethysmography (PPG) signal. On the other hand, the results of the studies on Heart Rate (HR) and Respiration Rate (RR) calculations have been reported using the imaging Photoplethysmography (iPPG) signal. The iPPG signal is a kind of PPG signal that is recorded in a non-contact method using a camera.This paper is among the first studies to provide a new algorithm for estimating BP values using the only iPPG signal and with a non-contact method. The validity of the proposed algorithm was evaluated in a gathered database with 40 people. The algorithm in estimation of the Diastolic Blood Pressure (DBP) was able to achieve mean error of −0.2 and standard deviation of 6.41 mmHg and in estimation of the Systolic Blood Pressure (SBP) was able to achieve mean error of 0.45 and standard deviation of 12.39 mmHg.","PeriodicalId":255375,"journal":{"name":"2020 International Conference on Machine Vision and Image Processing (MVIP)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126898810","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: 11

Brain Tumor Segmentation in MRI Images Using a Hybrid Deep Network Based on Patch and Pixel 基于Patch和Pixel混合深度网络的MRI图像脑肿瘤分割

2020 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2020-02-01 DOI: 10.1109/MVIP49855.2020.9116880

F. Derikvand, Hassan Khotanlou

引用次数: 4

Monitoring Wrist and Fingers Range of Motion using Leap Motion Camera for Physical Rehabilitation 使用Leap运动相机监测手腕和手指的运动范围，用于物理康复

2020 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2020-02-01 DOI: 10.1109/MVIP49855.2020.9116876

M. Kavian, A. Nadian-Ghomsheh

引用次数: 5

Recognizing Persian Automobile license plates under adverse rainy conditions 在恶劣的雨天条件下识别波斯汽车牌照

2020 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2020-02-01 DOI: 10.1109/MVIP49855.2020.9116886

Hossein Rezaei, Maryam Haghshenas, Mahboobehsadat Yasini

{"title":"Recognizing Persian Automobile license plates under adverse rainy conditions","authors":"Hossein Rezaei, Maryam Haghshenas, Mahboobehsadat Yasini","doi":"10.1109/MVIP49855.2020.9116886","DOIUrl":"https://doi.org/10.1109/MVIP49855.2020.9116886","url":null,"abstract":"This study is focused on identifying Persian license plate of Iranian cars in different rain conditions, with different distances and lighting, with simple and complex backgrounds and different angles of stationary cars. A method that is applicable to automated license plate identification systems, which is a type of intelligent transportation system. Systems that have been localized due to the variety of appearance of car license plates in different countries are currently being researched in many countries. Among the important challenges in identifying a vehicle license plate are inappropriate conditions such as adverse weather conditions such as rainy weather, snow, fog and dust, which make it difficult to identify license plates. The proposed method, which is a simple yet efficient method, employs many image processing techniques and morphology operations, and the results of implementing the proposed algorithm in MATLAB 2019b software on 420 Color image of car under low rainfall conditions, moderate rainfall and severe rainfall and storm show accuracy of 81%, 61.5% and 10.5% accuracy in identifying plaque IDs and their separation, respectively.","PeriodicalId":255375,"journal":{"name":"2020 International Conference on Machine Vision and Image Processing (MVIP)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126103559","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