2022 International Conference on Machine Vision and Image Processing (MVIP)最新文献

A New Algorithm for Hand Gesture Recognition in Color Videos for Operating System Commands 基于操作系统命令的彩色视频手势识别新算法

2022 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2022-02-23 DOI: 10.1109/MVIP53647.2022.9738775

Maziyar Grami

{"title":"A New Algorithm for Hand Gesture Recognition in Color Videos for Operating System Commands","authors":"Maziyar Grami","doi":"10.1109/MVIP53647.2022.9738775","DOIUrl":"https://doi.org/10.1109/MVIP53647.2022.9738775","url":null,"abstract":"In recent years, human-computer interaction and machine vision have become two of the favorite research areas in computer science. This paper is research on hand motion and hand gesture recognition using image processing techniques to control some system commands. Different hand motion and gesture recognition methods have been considered by researchers for use in computer systems, video game consoles, and mobile devices. In such cases, hand motion or hand gesture type is detected by tracking the hands' image or by matching the image with gestures storing in the database, after the first position of the hand is identified. In this paper, we provide an efficient way for recognizing and tracking the sequences of image frames. The main objective of this paper is to provide an efficient method for hand recognition in crowded environments, without any restrictions. The frames can be received from a video file. In the first frame, the location of the hand is recognized using color analysis, then it would be compared to the next frames. Detected movements are applied for controlling commands in an operating system. So far, an effective way for this kind of problem has not been suggested. In this study, we have tried to propose an efficient algorithm that is less dependent on background and lighting conditions. We tested the proposed method on several captured videos using the Image Processing Toolbox of MATLAB. These videos are captured in very crowded environments. The presented results showed that in different lighting conditions and various noises, the proposed method works almost without mistakes.","PeriodicalId":184716,"journal":{"name":"2022 International Conference on Machine Vision and Image Processing (MVIP)","volume":"134 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":"124150765","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

Compressed Sensing MRI Reconstruction Using Improved U-net based on Deep Generative Adversarial Networks 基于深度生成对抗网络的改进U-net压缩感知MRI重构

2022 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2022-02-23 DOI: 10.1109/MVIP53647.2022.9738554

Seyed Amir Mousavi, M. Ahmadzadeh, Ehsan Yazdian

{"title":"Compressed Sensing MRI Reconstruction Using Improved U-net based on Deep Generative Adversarial Networks","authors":"Seyed Amir Mousavi, M. Ahmadzadeh, Ehsan Yazdian","doi":"10.1109/MVIP53647.2022.9738554","DOIUrl":"https://doi.org/10.1109/MVIP53647.2022.9738554","url":null,"abstract":"Magnetic Resonance Imaging as non-invasive imaging can produce detailed anatomical images. MRI is a time- consuming imaging technique. Several imaging techniques, like parallel imaging, have been suggested to enhance imaging speed. Compressive Sensing MRI utilizes the sparsity of MR images to reconstruct MR images with under-sampled k-space data. It has already been shown that convolutional neural networks work better than sparsity-based approaches in image quality and reconstruction speed. In this paper, a novel method based on very deep CNN for the reconstruction of MR images is proposed using Generative Adversarial Networks. Generative and discriminative networks are designed with improved ResNet architecture. Using improved architecture has led to deepening generative and discriminative networks, reducing aliasing artifacts, more accurate reconstruction of edges, and better reconstruction of tissues. Compared to DLMRI and DAGAN methods, we demonstrate the proposed method outperforms the conventional methods and deep learning-based approaches. Assessment is made on several datasets such as the brain, heart, and prostate. Reconstruction of brain data with a Cartesian mask of 30% in the proposed method has improved the SSIM criteria up to 0.99. Also, image reconstruction time is approximately 20 ms on GPU, which is suitable for real-time applications.","PeriodicalId":184716,"journal":{"name":"2022 International Conference on Machine Vision and Image Processing (MVIP)","volume":"33 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":"132035585","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

Image Registration Based on Redundant Keypoint Elimination SARSIFT Algorithm and MROGH Descriptor 基于冗余关键点消除SARSIFT算法和MROGH描述符的图像配准

2022 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2022-02-23 DOI: 10.1109/MVIP53647.2022.9738737

Zahra Hossein-Nejad, M. Nasri

引用次数: 0

A Machine Vision Based Method for Extracting Visual Features of Froth in Copper Floatation Process 基于机器视觉的铜浮选过程泡沫视觉特征提取方法

2022 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2022-02-23 DOI: 10.1109/MVIP53647.2022.9738765

Abbas Barhoun, A. M. Khiavi, Alireza Sokhandan Sorkhabi, H. S. Aghdasi, Behzad Kargari

引用次数: 0

Transfer Learning on Semantic Segmentation for Sugar Crystal Analysis 糖晶分析语义分割的迁移学习

2022 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2022-02-23 DOI: 10.1109/MVIP53647.2022.9738778

Zohoor Hayali, G. Akbarizadeh

引用次数: 1

Improvement of Human Tracking Based on an Accurate Estimation of Feet or Head Position 基于足部或头部位置精确估计的人体跟踪改进

2022 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2022-02-23 DOI: 10.1109/MVIP53647.2022.9738750

Ali Dadgar, Y. Baleghi, M. Ezoji

引用次数: 1

A Secure Hybrid Permissioned Blockchain and Deep Learning Platform for CT Image Classification 用于CT图像分类的安全混合许可区块链和深度学习平台

2022 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2022-02-23 DOI: 10.1109/MVIP53647.2022.9738736

M. Noei, Mohammadreza Parvizimosaed, Aliakbar Saleh Bigdeli, Mohammadmostafa Yalpanian

{"title":"A Secure Hybrid Permissioned Blockchain and Deep Learning Platform for CT Image Classification","authors":"M. Noei, Mohammadreza Parvizimosaed, Aliakbar Saleh Bigdeli, Mohammadmostafa Yalpanian","doi":"10.1109/MVIP53647.2022.9738736","DOIUrl":"https://doi.org/10.1109/MVIP53647.2022.9738736","url":null,"abstract":"Pneumonia is a life-threatening and prevalent disease and needs to be diagnosed within a short time because of the lungs' fluid flow. Late detection of the disease may result in the patient’s death. Thus, advanced diagnosis is a critical factor besides the disease progress. In addition to advanced diagnosis, the privacy of datasets is important for organizations. Due to the great value of datasets, hospitals do not want to share their datasets, but they want to share their trained network weights. Therefore, in this paper, we combine deep learning and blockchain to implement the blockchain as distributed storage. Using permission blockchain, weights are broadcasted among other hospitals securely. Because of the security, the dataset is shared with five hospitals equally. Each hospital trains its network model and sends its weights to the blockchain. The goal is to broadcast the aggregated weights among hospitals securely and have good enough results because the whole dataset is not implemented to train a network. The dataset contains 5856 images, and hospitals implement a residual neural network with 28 layers. The results show that hospitals can increase the accuracy of their model using shared weights compared to a model without using shared weights.","PeriodicalId":184716,"journal":{"name":"2022 International Conference on Machine Vision and Image Processing (MVIP)","volume":"3 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":"123935381","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

Spatial Quality Assessment of Pansharpened Images Based on Gray Level Co-Occurrence Matrix 基于灰度共生矩阵的泛锐化图像空间质量评价

2022 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2022-02-23 DOI: 10.1109/MVIP53647.2022.9738763

S. Aghapour Maleki, H. Ghassemian

{"title":"Spatial Quality Assessment of Pansharpened Images Based on Gray Level Co-Occurrence Matrix","authors":"S. Aghapour Maleki, H. Ghassemian","doi":"10.1109/MVIP53647.2022.9738763","DOIUrl":"https://doi.org/10.1109/MVIP53647.2022.9738763","url":null,"abstract":"Assessing the quality of pansharpened images is a critical issue in order to obtain a quantitative score to represent the quality and compare the performance of different fusion methods. Most of the introduced metrics for pansharpened image quality assessment, evaluate the spectral content of the image, while in different applications of remote sensing like detection and identification of image objects, spatial quality has an important role. In the current study, a new index for spatial quality assessment is introduced that extracts gray level co-occurrence matrix (GLCM) from distorted and reference images and compares the similarities of these features. The tempere image database 2013 (TID2013) that provides reference and different types of distorted images with subjective scores of each image is used as the desired database. To solve the high computational complexity of obtaining GLCM features, the fast GLCM method is employed. In this way, 16 different features are extracted. To select the features that have the most consistency with the human visual system (HVS), the forward floating search method is used as a feature selection method and five features are obtained as the final features to form the desired index. Experimental results show the efficiency of the proposed method in determining the spatial quality of fused images compared with that of the available quality assessment metrics.","PeriodicalId":184716,"journal":{"name":"2022 International Conference on Machine Vision and Image Processing (MVIP)","volume":"42 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":"125272447","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

Fast Multi Focus Image Fusion Using Determinant 基于行列式的快速多焦点图像融合

2022 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2022-02-23 DOI: 10.1109/MVIP53647.2022.9738555

Mostafa Amin-Naji, A. Aghagolzadeh, Hami Mahdavinataj

{"title":"Fast Multi Focus Image Fusion Using Determinant","authors":"Mostafa Amin-Naji, A. Aghagolzadeh, Hami Mahdavinataj","doi":"10.1109/MVIP53647.2022.9738555","DOIUrl":"https://doi.org/10.1109/MVIP53647.2022.9738555","url":null,"abstract":"This paper presents fast pixel-wise multi-focus image fusion in the spatial domain without bells and whistles. The proposed method just uses the determinant of the sliding windows from the input images as a metric to create a pixel-wise decision map. The sliding windows of 15 pixels with the stride of 7 pixels are passed through the input images. Then it creates a pixel-wise decision map for fusion multi-focus images. Also, some simple tricks like global image threshold using Otsu’s method and removal of small objects by morphological closing operation are used to refine the pixel-wise decision map. This method is high-speed and can fuse a pair of 512x512 multi-focus images around 0.05 seconds (50 milliseconds) in our hardware. We compared it with 22 prominent methods in the transform domain, spatial domain, and deep learning based methods that their source codes are available, and our method is faster than all of them. We conducted the objective and subjective experiments on the Lytro dataset, and our method can compete with their results. The proposed method may not have the best fusion quality among state-of-the-art methods, but to the best of our knowledge, this is the fastest pixel-wise method and very suitable for real-time image processing. All material and source code will be available in https://github.com/mostafaaminnaji/FastDetFuse and http://imagefusion.ir.","PeriodicalId":184716,"journal":{"name":"2022 International Conference on Machine Vision and Image Processing (MVIP)","volume":"10 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":"126981756","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

JPEG Steganalysis Using the Relations Between DCT Coefficients 利用DCT系数之间的关系进行JPEG隐写分析

2022 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2022-02-23 DOI: 10.1109/MVIP53647.2022.9738785

Seyedeh Maryam Seyed Khalilollahi, Azadeh Mansouri

引用次数: 0