2018 7th European Workshop on Visual Information Processing (EUVIP)最新文献

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Automatic 3D Detection and Segmentation of Head and Neck Cancer from MRI Data 基于MRI数据的头颈部肿瘤自动三维检测与分割
2018 7th European Workshop on Visual Information Processing (EUVIP) Pub Date : 2018-09-20 DOI: 10.1109/EUVIP.2018.8611759
Baixiang Zhao, J. Soraghan, D. Grose, T. Doshi, G. D. Caterina
{"title":"Automatic 3D Detection and Segmentation of Head and Neck Cancer from MRI Data","authors":"Baixiang Zhao, J. Soraghan, D. Grose, T. Doshi, G. D. Caterina","doi":"10.1109/EUVIP.2018.8611759","DOIUrl":"https://doi.org/10.1109/EUVIP.2018.8611759","url":null,"abstract":"A novel algorithm for automatic head and neck 3D tumour segmentation from magnetic resonance imaging (MRI) is presented. The proposed algorithm pre-processes the MRI data slices to enhance quality and reduce artefacts. An intensity standardisation process is performed between slices, followed by cancer region segmentation of central slice, to get the correct intensity range and rough location of tumour regions. Fourier interpolation is applied to create isotropic 3D MRI volume. A new location-constrained 3D level set method segments the tumour from the interpolated MRI volume. The proposed algorithm is tested on real MRI data. The results show that the novel 3D tumour volume extraction algorithm has an improved dice score and F -measure when compared to the previous 2D and 3D segmentation method.","PeriodicalId":252212,"journal":{"name":"2018 7th European Workshop on Visual Information Processing (EUVIP)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125114048","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
Classification of Building Information Model (BIM) Structures with Deep Learning 基于深度学习的建筑信息模型结构分类
2018 7th European Workshop on Visual Information Processing (EUVIP) Pub Date : 2018-08-01 DOI: 10.1109/EUVIP.2018.8611701
Francesco Lomio, Ricardo J. P. C. Farinha, M. Laasonen, H. Huttunen
{"title":"Classification of Building Information Model (BIM) Structures with Deep Learning","authors":"Francesco Lomio, Ricardo J. P. C. Farinha, M. Laasonen, H. Huttunen","doi":"10.1109/EUVIP.2018.8611701","DOIUrl":"https://doi.org/10.1109/EUVIP.2018.8611701","url":null,"abstract":"In this work we study an application of machine learning to the construction industry and we use classical and modern machine learning methods to categorize images of building designs into three classes: Apartment building, Industrial building or Other. No real images are used, but only images extracted from Building Information Model (BIM) software, as these are used by the construction industry to store building designs. For this task, we compared four different methods: the first is based on classical machine learning, where Histogram of Oriented Gradients (HOG) was used for feature extraction and a Support Vector Machine (SVM) for classification; the other three methods are based on deep learning, covering common pre-trained networks as well as one designed from scratch. To validate the accuracy of the models, a database of 240 images was used. The accuracy achieved is 57% for the HOG + SVM model, and above 89% for the neural networks.","PeriodicalId":252212,"journal":{"name":"2018 7th European Workshop on Visual Information Processing (EUVIP)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130001035","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: 13
Embedded Implementation of a Deep Learning Smile Detector 深度学习微笑检测器的嵌入式实现
2018 7th European Workshop on Visual Information Processing (EUVIP) Pub Date : 2018-07-10 DOI: 10.1109/EUVIP.2018.8611783
Pedram Ghazi, A. Happonen, J. Boutellier, H. Huttunen
{"title":"Embedded Implementation of a Deep Learning Smile Detector","authors":"Pedram Ghazi, A. Happonen, J. Boutellier, H. Huttunen","doi":"10.1109/EUVIP.2018.8611783","DOIUrl":"https://doi.org/10.1109/EUVIP.2018.8611783","url":null,"abstract":"In this paper we study the real time deployment of deep learning algorithms in low resource computational environments. As the use case, we compare the accuracy and speed of neural networks for smile detection using different neural network architectures and their system level implementation on NVidia Jetson embedded platform. We also propose an asynchronous multithreading scheme for parallelizing the pipeline. Within this framework, we experimentally compare thirteen widely used network topologies. The experiments show that low complexity architectures can achieve almost equal performance as larger ones, with a fraction of computation required.","PeriodicalId":252212,"journal":{"name":"2018 7th European Workshop on Visual Information Processing (EUVIP)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116273276","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
Faster Bounding Box Annotation for Object Detection in Indoor Scenes 室内场景中更快的目标检测边界框标注
2018 7th European Workshop on Visual Information Processing (EUVIP) Pub Date : 2018-07-03 DOI: 10.1109/EUVIP.2018.8611732
Bishwo Adhikari, Jukka Peltomäki, Jussi Puura, H. Huttunen
{"title":"Faster Bounding Box Annotation for Object Detection in Indoor Scenes","authors":"Bishwo Adhikari, Jukka Peltomäki, Jussi Puura, H. Huttunen","doi":"10.1109/EUVIP.2018.8611732","DOIUrl":"https://doi.org/10.1109/EUVIP.2018.8611732","url":null,"abstract":"This paper proposes an approach for rapid bounding box annotation for object detection datasets. The procedure consists of two stages: The first step is to annotate a part of the dataset manually, and the second step proposes annotations for the remaining samples using a model trained with the first stage annotations. We experimentally study which first/second stage split minimizes to total workload. In addition, we introduce a new fully labeled object detection dataset collected from indoor scenes. Compared to other indoor datasets, our collection has more class categories, diverse backgrounds, lighting conditions, occlusions and high intra-class differences. We train deep learning based object detectors with a number of state-of-the-art models and compare them in terms of speed and accuracy. The fully annotated dataset is released freely available for the research community.","PeriodicalId":252212,"journal":{"name":"2018 7th European Workshop on Visual Information Processing (EUVIP)","volume":"170 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124733607","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: 34
Saliency Enhanced Robust Visual Tracking 显著增强鲁棒视觉跟踪
2018 7th European Workshop on Visual Information Processing (EUVIP) Pub Date : 2018-02-08 DOI: 10.1109/EUVIP.2018.8611706
Çağlar Aytekin, Francesco Cricri, Emre B. Aksu
{"title":"Saliency Enhanced Robust Visual Tracking","authors":"Çağlar Aytekin, Francesco Cricri, Emre B. Aksu","doi":"10.1109/EUVIP.2018.8611706","DOIUrl":"https://doi.org/10.1109/EUVIP.2018.8611706","url":null,"abstract":"Discrete correlation filter (DCF) based trackers have shown considerable success in visual object tracking. These trackers often make use of low to mid level features such as histogram of gradients (HoG) and mid-layer activations from convolution neural networks (CNNs). We argue that including semantically higher level information to the tracked features may provide further robustness to challenging cases such as viewpoint changes. Deep salient object detection is one example of such high level features, as it make use of semantic information to highlight the important regions in the given scene. In this work, we propose an improvement over DCF based trackers by combining saliency based and other features based filter responses. This combination is performed with an adaptive weight on the saliency based filter responses, which is automatically selected according to the temporal consistency of visual saliency. We show that our method consistently improves a baseline DCF based tracker especially in challenging cases and performs superior to the state-of-the-art. Our improved tracker operates at 9.3 fps, introducing a small computational burden over the baseline which operates at 11 fps.","PeriodicalId":252212,"journal":{"name":"2018 7th European Workshop on Visual Information Processing (EUVIP)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133698648","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: 8
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