2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA)最新文献_第4页

Calibration method for sparse multi-view cameras by bridging with a mobile camera 稀疏多视点相机与移动相机桥接标定方法

2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA) Pub Date : 2017-11-01 DOI: 10.1109/IPTA.2017.8310128

Hidehiko Shishido, I. Kitahara

{"title":"Calibration method for sparse multi-view cameras by bridging with a mobile camera","authors":"Hidehiko Shishido, I. Kitahara","doi":"10.1109/IPTA.2017.8310128","DOIUrl":"https://doi.org/10.1109/IPTA.2017.8310128","url":null,"abstract":"Camera calibration that estimates the projective relationship between 3D and 2D image spaces is one of the most crucial processes for such 3D image processing as 3D reconstruction and 3D tracking. A strong calibration method, which needs to place landmarks with known 3D positions, is a common technique. However, as the target space becomes large, landmark placement becomes more complicated. Although a weak-calibration method does not need known landmarks to estimate a projective transformation matrix from the correspondence information among multi-view images, the estimation precision depends on the accuracy of the correspondence. When multiple cameras are arranged sparsely, detecting sufficient corresponding points is difficult. In this research, we propose a calibration method that bridges sparse multiple cameras with mobile camera images. The mobile camera captures video images while moving among sparse multi-view cameras. The captured video resembles dense multi-view images and includes sparse multi-view images so that weak-calibration is effective. We confirmed the appropriate spacing between the images through comparative experiments of camera calibration accuracy by changing the number of bridging images and applied our proposed method to multiple capturing experiments in a large-scale space and verified its robustness.","PeriodicalId":316356,"journal":{"name":"2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133908118","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

Multimodal three-dimensional vision for wildland fires detection and analysis 多模态三维视觉野火探测与分析

2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA) Pub Date : 2017-11-01 DOI: 10.1109/IPTA.2017.8310085

M. Akhloufi, Tom Toulouse, L. Rossi, X. Maldague

引用次数: 8

Subaperture image segmentation for lossless compression 子孔径图像分割无损压缩

2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA) Pub Date : 2017-11-01 DOI: 10.1109/IPTA.2017.8310083

I. Schiopu, M. Gabbouj, Alexandros Iosifidis, B. Zeng, Shuaicheng Liu

引用次数: 8

Vehicle re-identification by fusing multiple deep neural networks 融合多个深度神经网络的车辆再识别

2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA) Pub Date : 2017-11-01 DOI: 10.1109/IPTA.2017.8310090

Chao Cui, N. Sang, Changxin Gao, Lei Zou

{"title":"Vehicle re-identification by fusing multiple deep neural networks","authors":"Chao Cui, N. Sang, Changxin Gao, Lei Zou","doi":"10.1109/IPTA.2017.8310090","DOIUrl":"https://doi.org/10.1109/IPTA.2017.8310090","url":null,"abstract":"Vehicle re-identification has become a fundamental task because of the growing explosion in the use of surveillance cameras in public security. The most widely used solution is based on license plate verification. But when facing the vehicle without a license, deck cars and other license plate information error or missing situation, vehicle searching is still a challenging problem. This paper proposed a vehicle re-identification method based on deep learning which exploit a two-branch Multi-DNN Fusion Siamese Neural Network (MFSNN) to fuses the classification outputs of color, model and pasted marks on the windshield and map them into a Euclidean space where distance can be directly used to measure the similarity of arbitrary two vehicles. In order to achieve this goal, we present a method of vehicle color identification based on Alex net, a method of vehicle model identification based on VGG net, a method of pasted marks detection and identification based on Faster R-CNN. We evaluate our MFSNN method on VehicleID dataset and in the experiment. Experiment results show that our method can achieve promising results.","PeriodicalId":316356,"journal":{"name":"2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA)","volume":"399 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132217970","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: 19

Vehicle boundary improvement and passing vehicle detection in driver assistance by flow distribution 车流分配辅助驾驶中车辆边界改善与过路车辆检测

2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA) Pub Date : 2017-11-01 DOI: 10.1109/IPTA.2017.8310126

A. Das, K. Ruppin, P. Dave, Sharfudheen Pv

{"title":"Vehicle boundary improvement and passing vehicle detection in driver assistance by flow distribution","authors":"A. Das, K. Ruppin, P. Dave, Sharfudheen Pv","doi":"10.1109/IPTA.2017.8310126","DOIUrl":"https://doi.org/10.1109/IPTA.2017.8310126","url":null,"abstract":"Research in advanced driver assistance system (ADAS) is an important step towards achieving the goal of autonomous intelligent vehicle. Vehicle detection and its distance estimation is an important solution of ADAS for forward collision warning applications. Partial occlusions of passing vehicles makes their detections tedious yet the accuracy of vehicle detection in all its forms in the scene and their corresponding distance estimation is a vital factor to deploy the solution. A small deviation in detection and distance accuracy could end up in a greater mishap in ADAS and AV (Autonomous Vehicle). The proposed framework addresses the aforementioned problems of detection of passing vehicles and perfecting distance measurement by accurate lower bound estimation through Inter and Intra-Frame Flow Correspondence (I2F2C). The proposed generic framework of 12F2C could be employed as a plug-in for the existing machine learning (ML) [1]/ deep learning (DL) [2] based algorithms for improving accuracy of distance estimation of vehicles and also improve accuracy and performance of passing vehicle detection with a detailed mathematical model of motion confidence.","PeriodicalId":316356,"journal":{"name":"2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124301891","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

Weeds detection in UAV imagery using SLIC and the hough transform 基于SLIC和hough变换的无人机图像杂草检测

2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA) Pub Date : 2017-11-01 DOI: 10.1109/IPTA.2017.8310102

M. D. Bah, A. Hafiane, R. Canals

引用次数: 43

Genre linked automated assessment and feedback of photographs based on visual aesthetics 基于视觉美学的类型关联的照片自动评估和反馈

2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA) Pub Date : 2017-11-01 DOI: 10.1109/IPTA.2017.8310080

Pavan Sudheendra, D. Jayagopi

引用次数: 0

A novel synthetic dataset for research in overlapped fingerprint separation 一种新的用于重叠指纹分离研究的合成数据集

2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA) Pub Date : 2017-11-01 DOI: 10.1109/IPTA.2017.8310137

B. Stojanovic, Oge Marques, A. Neskovic

引用次数: 2

A comparison of CNN-based face and head detectors for real-time video surveillance applications 基于cnn的人脸和头部检测器在实时视频监控应用中的比较

2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA) Pub Date : 2017-11-01 DOI: 10.1109/IPTA.2017.8310113

Le Thanh Nguyen-Meidine, Eric Granger, M. Kiran, Louis-Antoine Blais-Morin

{"title":"A comparison of CNN-based face and head detectors for real-time video surveillance applications","authors":"Le Thanh Nguyen-Meidine, Eric Granger, M. Kiran, Louis-Antoine Blais-Morin","doi":"10.1109/IPTA.2017.8310113","DOIUrl":"https://doi.org/10.1109/IPTA.2017.8310113","url":null,"abstract":"Detecting faces and heads appearing in video feeds are challenging tasks in real-world video surveillance applications due to variations in appearance, occlusions and complex backgrounds. Recently, several CNN architectures have been proposed to increase the accuracy of detectors, although their computational complexity can be an issue, especially for realtime applications, where faces and heads must be detected live using high-resolution cameras. This paper compares the accuracy and complexity of state-of-the-art CNN architectures that are suitable for face and head detection. Single pass and region-based architectures are reviewed and compared empirically to baseline techniques according to accuracy and to time and memory complexity on images from several challenging datasets. The viability of these architectures is analyzed with real-time video surveillance applications in mind. Results suggest that, although CNN architectures can achieve a very high level of accuracy compared to traditional detectors, their computational cost can represent a limitation for many practical real-time applications.","PeriodicalId":316356,"journal":{"name":"2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114240076","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: 37

Multi-view visual saliency-based MRI classification for alzheimer's disease diagnosis 基于多视图视觉显著性的MRI分类在阿尔茨海默病诊断中的应用

2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA) Pub Date : 2017-11-01 DOI: 10.1109/IPTA.2017.8310118

O. B. Ahmed, F. Lecellier, M. Paccalin, C. Fernandez-Maloigne

{"title":"Multi-view visual saliency-based MRI classification for alzheimer's disease diagnosis","authors":"O. B. Ahmed, F. Lecellier, M. Paccalin, C. Fernandez-Maloigne","doi":"10.1109/IPTA.2017.8310118","DOIUrl":"https://doi.org/10.1109/IPTA.2017.8310118","url":null,"abstract":"Visual inspection is the first step performed by clinicians during evaluation of medical images in image-based diagnosis. This behavior can be automated using computational saliency models. In this paper, we investigate the potential role of visual saliency for computer-aided diagnosis of Alzheimer's disease (AD). We propose a multi-view saliency-based framework to detect abnormalities from structural Magnitude Resonance Imaging (MRI) and classify subjects in a Multiple Kernel Learning (MKL) framework. The obtained saliency maps are able to detect relevant brain areas for early AD diagnosis. The effectiveness of the proposed approach was evaluated on structural MRI of 509 subjects from the ADNI dataset. We achieved accuracy of 88.98% (specificity of 94.4% and a sensitivity of 83.46%) and 81.31% (specificity of 84.22% and a sensitivity of 74.21%) classification and for respectively AD versus Normal Control(NC) and NC versus Mild Cognitive Impairment (MCI). For the most challenging classification task (AD versus MCI), we reached an accuracy of 79.8%, a specificity of 79.93% and a sensitivity of 64.02%.","PeriodicalId":316356,"journal":{"name":"2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123960979","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: 9