2020 International Conference on Machine Vision and Image Processing (MVIP)最新文献_第6页

An Application of Image Processing in Rug Industry 图像处理在地毯工业中的应用

2020 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2020-02-01 DOI: 10.1109/MVIP49855.2020.9116920

K. Jeddisaravi, Reza Javanmard Alitappeh, Sasan Parsafar, Leila Eslami

引用次数: 0

A pattern recognition based Holographic Graph Neuron for Persian alphabet recognition 基于模式识别的全息图神经元波斯语字母识别

2020 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2020-02-01 DOI: 10.1109/MVIP49855.2020.9116913

Vahid Hajihashemi, Mohammad Mehdi Arab Ameri, A. Alavi Gharahbagh, A. Bastanfard

引用次数: 8

Hyperspectral Anomaly Detection based on Frequency Analysis of Repeated Spatial Patterns 基于重复空间模式频率分析的高光谱异常检测

2020 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2020-02-01 DOI: 10.1109/MVIP49855.2020.9116924

A. Taghipour, H. Ghassemian

{"title":"Hyperspectral Anomaly Detection based on Frequency Analysis of Repeated Spatial Patterns","authors":"A. Taghipour, H. Ghassemian","doi":"10.1109/MVIP49855.2020.9116924","DOIUrl":"https://doi.org/10.1109/MVIP49855.2020.9116924","url":null,"abstract":"Hyperspectral anomaly detection has brought significant attention to researchers during the last decade. In the remote sensing domain, it has lots of applications like environmental monitoring, mine source detection, human safety and, etc. Different methods have been proposed to overcome the challenges of anomaly detection, which mainly are a highly accurate detection rate and also having low false alarm rates. Background complexity of the hyperspectral data causes false detection of pixels as anomalies, and several statistical and sparse based methods try to solve it. However, the computation burden of the statistical and sparse based methods make them unpractical. Also, they often do not utilize the spatial information of hyperspectral data for increasing the detection accuracy. In this paper, we proposed a frequency analysis based approach that suppresses the repeatable patterns of the scene which mainly represent the background and considers the remainder regions as anomalies. Owe to the Fourier analysis advantage, it fast and accurate detects anomalous regions. Two well-known data sets have been chosen to challenge the potency of the proposed method, and the results are compared with some state-of-the-art methods. The visual and quantitative results confirm the supremacy of the proposed method to competitors in computation complexity and detection accuracy point of view.","PeriodicalId":255375,"journal":{"name":"2020 International Conference on Machine Vision and Image Processing (MVIP)","volume":"39 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":"122025556","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 Ensemble Model For Human Posture Recognition 人体姿态识别的集成模型

2020 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2020-02-01 DOI: 10.1109/MVIP49855.2020.9116911

Bardia Esmaeili, Alireza Akhavanpour, A. Bosaghzadeh

{"title":"An Ensemble Model For Human Posture Recognition","authors":"Bardia Esmaeili, Alireza Akhavanpour, A. Bosaghzadeh","doi":"10.1109/MVIP49855.2020.9116911","DOIUrl":"https://doi.org/10.1109/MVIP49855.2020.9116911","url":null,"abstract":"Human Body Pose Estimation (HBPE) and HumanBody Posture Recognition(HBPR) have improved significantly in the past decade. Gaining access to huge amounts of data, Kinect camera, neural networks and specifically deep convolutional neural networks (deep convnets) have led to fascinating success in these fields. In this paper we propose an ensemble model for human body posture recognition. Deep convnets are the main building block and fundamental aspect of our proposed model. We leverage deep convnets in two variations to classify postures. First, we use them for an end-to-end training scenario. We perform transfer learning with Imagenet weights on deep convnets with our gathered dataset of RGB images to classify five different postures. Second, we use a pre-trained deep convnet[1] (pose estimator) for estimating human body joints in RGB images. The pre-trained pose estimator has been trained to calculate a total of 17 2D joints coordinates and we utilize these coordinates to train a decision tree-based classifier for classification among five classes. Both variations are examined with different settings. The best settings for both variations are combined together to create our proposed model. More specifically, the classification layers of both variations are stacked together and fed to a logistic regression unit for a better classification result. Transfer learning, training and experiments in this paper are based on only RGB images from our gathered dataset and human body joints coordinates extracted from these images, which conveys that our proposed model does not require depth images or any sensor. Eventually, experimental results on the images show that the proposed model has higher performance than fundamental variations. Specifically, our model is able to correctly recognize the human posture in the majority of the images that one of the two fundamental variations fails to classify. The code for the proposed model and our gathered dataset are available on github1.","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":"117199745","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

From Brain Decoding To Brain-Driven Computer Vision 从大脑解码到大脑驱动的计算机视觉

2020 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2020-02-01 DOI: 10.1109/MVIP49855.2020.9116906

Honeye Rahmani, S. Taheri, Elahé Yargholi

引用次数: 2

A New Image Dataset for Document Corner Localization 一种新的文档角定位图像数据集

2020 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2020-02-01 DOI: 10.1109/MVIP49855.2020.9116896

Shima Baniadam Dizaj, M. Soheili, Azadeh Mansouri

引用次数: 2

Simultaneous Vehicle Detection and Classification Model based on Deep YOLO Networks 基于深度YOLO网络的车辆同步检测与分类模型

2020 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2020-02-01 DOI: 10.1109/MVIP49855.2020.9116922

A. Ghoreyshi, Alireza Akhavanpour, A. Bossaghzadeh

{"title":"Simultaneous Vehicle Detection and Classification Model based on Deep YOLO Networks","authors":"A. Ghoreyshi, Alireza Akhavanpour, A. Bossaghzadeh","doi":"10.1109/MVIP49855.2020.9116922","DOIUrl":"https://doi.org/10.1109/MVIP49855.2020.9116922","url":null,"abstract":"Due to the rapid growth of vehicles, traffic monitoring and tracking systems in the last decade, vehicle detection and extracting information such as vehicle type and model and car plate recognition are the important issues of the day and many efforts have been made using different methods to identify vehicles. Given the high number of vehicles and similarities of classes, it is a difficult task to find an accurate and rapid approach to differentiate available classes and classify them. In this article we propose a new approach which simultaneously detects vehicles and identifies their type. Two models are trained in this paper. The first model is based on CNN networks to extract features and detect vehicle models, and the second model which is the main contribution of this article is based on YOLO (You Only Look Once) algorithm and SSD to detect vehicle location in the image. To train and test this approach, we collect 150,000 images of 115 domestic and foreign vehicle classes from Iranian websites. Hence, the images have large variations in image size, illumination and pose. The experimental results on the accrued dataset shows that the proposed method is able to correctly classify 91% of the vehicles in uncontrolled conditions.","PeriodicalId":255375,"journal":{"name":"2020 International Conference on Machine Vision and Image Processing (MVIP)","volume":"190 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":"131919958","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: 6

Removing mixture of Gaussian and Impulse noise of images using sparse coding 利用稀疏编码去除图像的高斯和脉冲混合噪声

2020 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2020-02-01 DOI: 10.1109/MVIP49855.2020.9116879

Mahsa Malekzadeh, S. Meshgini, R. Afrouzian, A. Farzamnia, S. Sheykhivand

引用次数: 3

RILP: Robust Iranian License Plate Recognition Designed for Complex Conditions RILP:鲁棒伊朗车牌识别设计的复杂条件

2020 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2020-02-01 DOI: 10.1109/MVIP49855.2020.9116910

Alireza Samadzadeh, Amir Mehdi Shayan, Bahman Rouhani, A. Nickabadi, M. Rahmati

{"title":"RILP: Robust Iranian License Plate Recognition Designed for Complex Conditions","authors":"Alireza Samadzadeh, Amir Mehdi Shayan, Bahman Rouhani, A. Nickabadi, M. Rahmati","doi":"10.1109/MVIP49855.2020.9116910","DOIUrl":"https://doi.org/10.1109/MVIP49855.2020.9116910","url":null,"abstract":"This study introduces RILP, a novel approach to create a modular platform for autonomous license plate recognition (LPR). The proposed method consists of three stages of neural networks connected in a modular fashion. The first stage is the detection of license plates (LP); after that, RILP proceeds to detect text regions and performs character segmentations. Finally, to get to the LP number, optical character recognition (OCR) is done via a neural network previously trained for recognition of Persian characters. A robust LPR platform is a vital tool in modern cities for a variety of applications such as autonomous terrific management, surveillance, gateway control and etc. In order to be deployed in real-world conditions, LPR platforms should be practical and adaptive; in other words, easily trainable. RILP has paid attention to this matter as it can be effortlessly trained for any national LP. Only the final module of this approach requires training, which can be done with a simple dataset of the characters used in the LP of the desired country. This gives RILP tremendous portability to be deployed in any country for a wide variety of applications. The proposed platform was designed specifically for complex conditions. Therefore, a very complex and challenging dataset of Iranian LPs was created for a comprehensive evaluation of RILP, consisting of over 350 images of challenging natural conditions. RILP was evaluated with another publicly available dataset, as well as real footage of a local security camera. Evaluations yielded satisfying recognition accuracy up to 95% with a response time of 66 ms/LP. RILP proved to be robust and reliable enough, yielding satisfactory results in a reasonable time, while used in challenging conditions.","PeriodicalId":255375,"journal":{"name":"2020 International Conference on Machine Vision and Image Processing (MVIP)","volume":"360 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":"133645311","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

High dynamic range image reconstruction using multi-exposure Wavelet HDRCNN 基于多曝光小波HDRCNN的高动态范围图像重建

2020 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2020-02-01 DOI: 10.1109/MVIP49855.2020.9116898

A. Omrani, M. Soheili, M. Kelarestaghi

引用次数: 6