2022 International Conference on Machine Vision and Image Processing (MVIP)最新文献_第3页

Hardware Implementation of Moving Object Detection using Adaptive Coefficient in Performing Background Subtraction Algorithm 基于自适应系数的背景减法运动目标检测硬件实现

2022 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2022-02-23 DOI: 10.1109/MVIP53647.2022.9738764

Ali Rahiminezhad, Mohammad Reza Tavakoli, Sayed Masoud Sayedi

引用次数: 1

Optimized Quantum Circuits in Quantum Image Processing Using Qiskit 利用Qiskit优化量子图像处理中的量子电路

2022 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2022-02-23 DOI: 10.1109/MVIP53647.2022.9738550

Zahra Boreiri, Alireza Norouzi Azad, N. Majd

{"title":"Optimized Quantum Circuits in Quantum Image Processing Using Qiskit","authors":"Zahra Boreiri, Alireza Norouzi Azad, N. Majd","doi":"10.1109/MVIP53647.2022.9738550","DOIUrl":"https://doi.org/10.1109/MVIP53647.2022.9738550","url":null,"abstract":"Quantum image representation is an essential component of quantum image processing and plays a critical role in quantum information processing. Flexible Representation of Quantum Images (FRQI) presents pixel colors and associated locations as a state to represent images on quantum computers. A fundamental part of the quantum image processing system is quantum image compression (QIC), which is utilized to maintain and retrieve binary images. This compression allows us to minimize the number of controlled rotation gates in the quantum circuits. This paper designed optimized quantum circuits and simulated them using Qiskit on a real quantum computer based on minimum boolean expressions to retrieve the 8×4 binary single-digit images. To demonstrate the feasibility and efficacy of quantum image representation, quantum circuits for images were developed using FRQI, and quantum image representation experiments were done on IBM Quantum Experience (IBMQ). We were able to visualize quantum information by doing the quantum measurement on the image information that we had prepared. Without utilizing this method, the number of controlled rotation gates is equal to the number of pixels in the image; however, we showed that by using the QIC algorithm, we could decrease the number of gates significantly. On these images, the maximum and minimum compression ratios of QIC are 90.63% and 68.75%, respectively.","PeriodicalId":184716,"journal":{"name":"2022 International Conference on Machine Vision and Image Processing (MVIP)","volume":"6 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":"125672596","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

Employing a Hybrid Technique to Detect Tumor in Medical Images 应用混合技术检测医学图像中的肿瘤

2022 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2022-02-23 DOI: 10.1109/MVIP53647.2022.9738739

Leyla Aqhaei

引用次数: 0

A Learning Based Contrast Specific no Reference Image Quality Assessment Algorithm 一种基于学习的对比度特定无参考图像质量评估算法

2022 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2022-02-23 DOI: 10.1109/MVIP53647.2022.9738784

Moliamadali Mahmoodpour, Abdolah Amirany, M. H. Moaiyeri, Kian Jafari

{"title":"A Learning Based Contrast Specific no Reference Image Quality Assessment Algorithm","authors":"Moliamadali Mahmoodpour, Abdolah Amirany, M. H. Moaiyeri, Kian Jafari","doi":"10.1109/MVIP53647.2022.9738784","DOIUrl":"https://doi.org/10.1109/MVIP53647.2022.9738784","url":null,"abstract":"Contrast is one of the most important visual characteristics of an image that has a significant effect in understanding an image, however, due to different imaging conditions and poor devices, quality of image in terms of contrast will degrade. although, limited methods have been used to assess the quality of a contrast distorted images. Proper image contrast enhancement can increase the perceptual quality of most contrast distorted images. In this paper, assuming that the output images of a contrast enhancing algorithms have a quality such as a reference image, a learning-based contrast-specific no reference image quality assessment method is proposed. In the proposed method in this paper the image with the closest quality to the reference image is selected using a pre-trained classification network, and then the quality assessment is performed by comparing the enhanced image and the distorted image using structural similarity (SSIM) index. The functionality of the proposed method has been validated using three well-known contrast distorted image datasets (CSIQ, CCID2014 and TID2013).","PeriodicalId":184716,"journal":{"name":"2022 International Conference on Machine Vision and Image Processing (MVIP)","volume":"16 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":"131340277","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

A Hybrid of Inference and Stacked Classifiers to Indoor Scenes Classification of RGB-D Images 基于推理和堆叠混合分类器的RGB-D图像室内场景分类

2022 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2022-02-23 DOI: 10.1109/MVIP53647.2022.9738755

Shokouh S. Ahmadi, Hassan Khotanlou

引用次数: 1

The Detection of Blastocyst Embryo In Vitro Fertilization (IVF) 体外受精(IVF)中囊胚的检测

2022 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2022-02-23 DOI: 10.1109/MVIP53647.2022.9738768

Kimiya Samie Dehkordi, M. Ebrahimi Moghaddam

引用次数: 0

FFDR: Design and implementation framework for face detection based on raspberry pi 基于树莓派的人脸检测框架的设计与实现

2022 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2022-02-23 DOI: 10.1109/MVIP53647.2022.9738788

Dhafer Alhajim, G. Akbarizadeh, K. Ansari-Asl

引用次数: 0

An Augmented Reality Framework for Eye Muscle Education 眼肌肉教育的增强现实框架

2022 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2022-02-23 DOI: 10.1109/MVIP53647.2022.9738780

Asiyeh Bahaloo, Arman Ali Mohammadi, Mohammad Reza Mohammadi, M. Soryani

引用次数: 0

Automated Cell Tracking Using Adaptive Multi-stage Kalman Filter In Time-laps Images 在时间圈图像中使用自适应多阶段卡尔曼滤波的自动细胞跟踪

2022 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2022-02-23 DOI: 10.1109/MVIP53647.2022.9738793

Hane Naghshbandi, Yaser Baleghi Damavandi

{"title":"Automated Cell Tracking Using Adaptive Multi-stage Kalman Filter In Time-laps Images","authors":"Hane Naghshbandi, Yaser Baleghi Damavandi","doi":"10.1109/MVIP53647.2022.9738793","DOIUrl":"https://doi.org/10.1109/MVIP53647.2022.9738793","url":null,"abstract":"Segmenting living cells and tracking their movement in microscopy images are significant in biological studies and have played a crucial role in disease diagnosis, targeted therapy, drug delivery, and many other medical applications. Due to a large amount of time-lapse image data, automated image analysis can be a proper alternative to manual analysis, which is unreasonably time-consuming. However, Low-resolution microscopic images, unpredictable cell behavior, and multiple cell divisions make automated cell tracking challenging. In this paper, we propose a novel multi-object tracking approach guided by a two-stage adaptive Kalman forecast. Cell segmentation is performed using an edge detector combined with various morphological operations. The tracking section includes two general stages. At first, a Kalman filter with a constant speed is used to estimate the position of each cell in consecutive frames. The primary Kalman filter was able to detect a significant percentage of cells, but the high rate of cell division and migration of cells in or out of the field of view has caused errors in the final result. In the next stage, a secondary Kalman filter with modified parameters extracted from the results of initial tracking is proposed to estimate the position of cells in each frame, decrease errors, and improve the tracking results. Experimental results indicate that our method is 94.37% accurate in segmenting cells. The validity of the whole method has been conducted by comparing the results of the proposed method with manual tracking results, which demonstrates its efficiency.","PeriodicalId":184716,"journal":{"name":"2022 International Conference on Machine Vision and Image Processing (MVIP)","volume":"57 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":"115209996","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

Computer-aided Brain Age Estimation via Ensemble Learning of 3D Convolutional Neural Networks 基于三维卷积神经网络集成学习的计算机辅助脑年龄估计

2022 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2022-02-23 DOI: 10.1109/MVIP53647.2022.9738758

Ali Bahari Malayeri, Mohammad Mahdi Moradi, Kian Jafari Dinani

引用次数: 0