2018 Digital Image Computing: Techniques and Applications (DICTA)最新文献_第10页

Railway Infrastructure Defects Recognition using Fine-grained Deep Convolutional Neural Networks 基于细粒度深度卷积神经网络的铁路基础设施缺陷识别

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615868

Huaxi Huang, Jingsong Xu, Jian Zhang, Qiang Wu, Christina Kirsch

引用次数: 7

Automatic Detection of Valves with Disaster Response Robot on Basis of Depth Camera Information 基于深度相机信息的灾害响应机器人阀门自动检测

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615796

Keishi Nishikawa, J. Ohya, T. Matsuzawa, A. Takanishi, H. Ogata, K. Hashimoto

{"title":"Automatic Detection of Valves with Disaster Response Robot on Basis of Depth Camera Information","authors":"Keishi Nishikawa, J. Ohya, T. Matsuzawa, A. Takanishi, H. Ogata, K. Hashimoto","doi":"10.1109/DICTA.2018.8615796","DOIUrl":"https://doi.org/10.1109/DICTA.2018.8615796","url":null,"abstract":"In recent years, there has been an increasing demand for disaster response robots designed for working in disaster sites such as nuclear power plants where accidents have occurred. One of the tasks the robots need to complete at these kinds of sites is turning a valve. In order to employ robots to perform this task at real sites, it is desirable that the robots have autonomy for detecting the valves to be manipulated. In this paper, we propose a method that allows a disaster response robot to detect a valve, whose parameters such as position, orientation and size are unknown, based on information captured by a depth camera mounted on the robot. In our proposed algorithm, first the target valve is detected on the basis of an RGB image captured by the depth camera, and 3D point cloud data including the target is reconstructed by combining the detection result and the depth image. Second, the reconstructed point cloud data is processed to estimate parameters describing the target. Experiments were conducted on a simulator, and the results showed that our method could accurately estimate the parameters with a minimum error of 0.0230 m in position, 0.196 % in radius, and 0.00222 degree in orientation.","PeriodicalId":130057,"journal":{"name":"2018 Digital Image Computing: Techniques and Applications (DICTA)","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131762990","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

Inter-Subject Image Registration of Clinical Neck MRI Volumes using Discrete Periodic Spline Wavelet and Free form Deformation 基于离散周期样条小波和自由变形的临床颈部MRI体间图像配准

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615825

A. Suman, Md. Asikuzzaman, A. Webb, D. Perriman, M. Pickering

{"title":"Inter-Subject Image Registration of Clinical Neck MRI Volumes using Discrete Periodic Spline Wavelet and Free form Deformation","authors":"A. Suman, Md. Asikuzzaman, A. Webb, D. Perriman, M. Pickering","doi":"10.1109/DICTA.2018.8615825","DOIUrl":"https://doi.org/10.1109/DICTA.2018.8615825","url":null,"abstract":"This paper presents a framework for inter-patient image registration which uses a multi-thresholds, multi-similarity measures and multi-transformations based on compactly supported spline and discrete periodic spline wavelets (DPSWs) using the Gauss-Newton gradient descent (GNGD) and gradient descent (GD) optimization methods. Our primary intellectual contribution is incorporating DPSWs in the transformation while another includes fusing out-of-range concept in a surface matching technique which is implemented by a multi-transformations and multi-similarity measures. In particular, as a true deformation cannot be achieved by single combination of transformation, similarity measure (SM) and optimization of a registration process, a moving image is required to be brought within the range of a registration. On the other hand, the surface matching technique involves an edge position difference (EPD) SM in which coarse to fine surfaces are matched using multiple thresholds with a spline-based free from deformation (FFD) method. The registration experiments were performed on 3D clinical neck magnetic resonance (MR) images, with the results showing that our proposed method provides good accuracy and robustness.","PeriodicalId":130057,"journal":{"name":"2018 Digital Image Computing: Techniques and Applications (DICTA)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132998156","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

Evaluation of Graph Topological Features in Digitized Mammogram for Microcalcification Cluster Classification 微钙化聚类分类的数字化乳房x线图拓扑特征评价

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615858

N. Alam, R. Zwiggelaar

引用次数: 1

Bone Age Assessment Based on Two-Stage Deep Neural Networks 基于两阶段深度神经网络的骨龄评估

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615764

Meicheng Chu, Bo Liu, F. Zhou, X. Bai, Bin Guo

引用次数: 12

Fast and Energy-Efficient Time-of-Flight Distance Sensing Method for 3D Object Tracking 一种快速节能的三维目标跟踪飞行时间距离传感方法

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615790

H. Plank, G. Holweg, C. Steger, N. Druml

引用次数: 0

Impact of MRI Protocols on Alzheimer's Disease Detection MRI方案对阿尔茨海默病检测的影响

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615774

Saruar Alam, Len Hamey, K. Ho-Shon

{"title":"Impact of MRI Protocols on Alzheimer's Disease Detection","authors":"Saruar Alam, Len Hamey, K. Ho-Shon","doi":"10.1109/DICTA.2018.8615774","DOIUrl":"https://doi.org/10.1109/DICTA.2018.8615774","url":null,"abstract":"Alzheimer's disease (AD) can be detected using magnetic resonance imaging (MRI) based features and supervised classifiers. The subcortical and ventricular volumes change for AD patients. These volumes can be extracted from MRI by tools such as FreeSurfer and the multi-atlas-based likelihood fusion (MALF) algorithm. Studies use MRI from many medical imaging centers. However, individual centers typically use distinctive MRI protocols for brain scanning. The protocol differences include different scanner models with various operating parameters. Some scanner models have different field strengths. A key factor in classifying multicentric MR subject images having different protocols is how different scanner models affect the extraction of feature, and the subsequent classification performance of a supervised classifier. We have investigated the classification performance of FreeSurfer and MALF based volume features together with Radial Basis Function Support Vector Machine and Extreme Learning Machine across different imaging protocols. We have also investigated for both FreeSurfer and MALF, which brain regions are most effective for the detection of the disease under different protocols. Our study result indicates marginal differences in classification performance across scanner models with the same or different field strengths when differentiating AD, Mild Cognitive Impairment, and Normal Controls. We have also observed differences in ranking order of the most effective brain regions.","PeriodicalId":130057,"journal":{"name":"2018 Digital Image Computing: Techniques and Applications (DICTA)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114705779","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

Multi-Class Recognition using Noisy Training Data with a Self-Learning Approach 基于自学习方法的噪声训练数据多类识别

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615864

Amir Ghahremani, E. Bondarev, P. D. With

{"title":"Multi-Class Recognition using Noisy Training Data with a Self-Learning Approach","authors":"Amir Ghahremani, E. Bondarev, P. D. With","doi":"10.1109/DICTA.2018.8615864","DOIUrl":"https://doi.org/10.1109/DICTA.2018.8615864","url":null,"abstract":"Exploiting ConvNets for object classification systems requires extensive labor work, since these networks require to be trained by sufficiently large and accurately labeled datasets. We propose a novel self-learning approach, which is able to generate a reliable multi-class object classification model from a low-quality dataset that is disturbed with a high level of inter-class noise samples. This approach iteratively purifies the noisy training datasets for each class and updates the classification model. The iterations continue until the model and its parameters reach sufficient quality. The self-learning approach based on ConvNets is evaluated for a maritime surveillance use case, where vessels need to be classified into eight different types. The experimental results on the evaluation dataset show that the proposed approach improves the F1 score approximately by 5%, 8% and 25% at the end of the third iteration, while the initial training datasets contain 40%, 50% and 60% inter-class noise samples (erroneously classified labels of vessels), respectively. Additionally, the purification performance is highly dependent on inter- and inter-class similarities between training samples for higher noise levels. It was also found that the mean Average Precision (mAP) does not degrade so much, whereas other performance parameters show larger variation.","PeriodicalId":130057,"journal":{"name":"2018 Digital Image Computing: Techniques and Applications (DICTA)","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121081832","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

Memory Optimized Deep Dense Network for Image Super-resolution 内存优化深度密集网络图像超分辨率

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615829

Jialiang Shen, Yucheng Wang, Jian Zhang

引用次数: 0

Information Enhancement for Travelogues via a Hybrid Clustering Model 基于混合聚类模型的游记信息增强

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615849

Lu Zhang, Jingsong Xu, Jian Zhang, Yongshun Gong

{"title":"Information Enhancement for Travelogues via a Hybrid Clustering Model","authors":"Lu Zhang, Jingsong Xu, Jian Zhang, Yongshun Gong","doi":"10.1109/DICTA.2018.8615849","DOIUrl":"https://doi.org/10.1109/DICTA.2018.8615849","url":null,"abstract":"Travelogues consist of textual information shared by tourists through web forums or other social media which often lack illustrations (images). In image sharing websites like Flicker, users can post images with rich textual information: ‘title’, ‘tag’ and ‘description’. The topics of travelogues usually revolve around beautiful sceneries. Corresponding landscape images recommended to these travelogues can enhance the vividness of reading. However, it is difficult to fuse such information because the text attached to each image has diverse meanings/views. In this paper, we propose an unsupervised Hybrid Multiple Kernel K-means (HMKKM) model to link images and travelogues through multiple views. Multi-view matrices are built to reveal the correlations between several respects. For further improving the performance, we add a regularisation based on textual similarity. To evaluate the effectiveness of the proposed method, a dataset is constructed from TripAdvisor and Flicker to find the related images for each travelogue. Experiment results demonstrate the superiority of the proposed model by comparison with other baselines.","PeriodicalId":130057,"journal":{"name":"2018 Digital Image Computing: Techniques and Applications (DICTA)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130223627","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