Proceedings of the ... IAPR International Conference on Pattern Recognition. International Conference on Pattern Recognition最新文献_第2页

Detection of Intracranial Hypertension using Deep Learning. 应用深度学习检测颅内高压。

Proceedings of the ... IAPR International Conference on Pattern Recognition. International Conference on Pattern Recognition Pub Date : 2016-12-01 Epub Date: 2017-04-24 DOI: 10.1109/ICPR.2016.7900010

Benjamin Quachtran, Robert Hamilton, Fabien Scalzo

{"title":"Detection of Intracranial Hypertension using Deep Learning.","authors":"Benjamin Quachtran, Robert Hamilton, Fabien Scalzo","doi":"10.1109/ICPR.2016.7900010","DOIUrl":"https://doi.org/10.1109/ICPR.2016.7900010","url":null,"abstract":"Intracranial Hypertension, a disorder characterized by elevated pressure in the brain, is typically monitored in neurointensive care and diagnosed only after elevation has occurred. This reaction-based method of treatment leaves patients at higher risk of additional complications in case of misdetection. The detection of intracranial hypertension has been the subject of many recent studies in an attempt to accurately characterize the causes of hypertension, specifically examining waveform morphology. We investigate the use of Deep Learning, a hierarchical form of machine learning, to model the relationship between hypertension and waveform morphology, giving us the ability to accurately detect presence hypertension. Data from 60 patients, showing intracranial pressure levels over a half hour time span, was used to evaluate the model. We divided each patient's recording into average normalized beats over 30 sec segments, assigning each beat a label of high (i.e. greater than 15 mmHg) or low intracranial pressure. The model was tested to predict the presence of elevated intracranial pressure. The algorithm was found to be 92.05± 2.25% accurate in detecting intracranial hypertension on our dataset.","PeriodicalId":74516,"journal":{"name":"Proceedings of the ... IAPR International Conference on Pattern Recognition. International Conference on Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/ICPR.2016.7900010","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35377867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 20

Bayesian approach to learn Bayesian networks using data and constraints 贝叶斯方法学习使用数据和约束的贝叶斯网络

Proceedings of the ... IAPR International Conference on Pattern Recognition. International Conference on Pattern Recognition Pub Date : 2016-01-01 DOI: 10.1109/ICPR.2016.7900204

Xiao-Guang Gao, Yu Yang, Zhi-gao Guo, Daqing Chen

引用次数: 1

Unsupervised Surveillance Video Retrieval Based on Human Action and Appearance 基于人的动作和外表的无监督监控视频检索

Proceedings of the ... IAPR International Conference on Pattern Recognition. International Conference on Pattern Recognition Pub Date : 2014-08-24 DOI: 10.1109/ICPR.2014.792

D. Gómez, H. Kjellström

{"title":"Unsupervised Surveillance Video Retrieval Based on Human Action and Appearance","authors":"D. Gómez, H. Kjellström","doi":"10.1109/ICPR.2014.792","DOIUrl":"https://doi.org/10.1109/ICPR.2014.792","url":null,"abstract":"Forensic video analysis is the offline analysis of video aimed at understanding what happened in a scene in the past. Two of its key tasks are the recognition of specific actions, e.g., walking or fighting, and the search for specific persons, also referred to as re-identification. Although these tasks have traditionally been performed manually in forensic investigations, the current growing number of cameras and recorded video leads to the need for automated analysis. In this paper we propose an unsupervised retrieval system for surveillance videos based on human action and appearance. Given a query window, the system retrieves people performing the same action as the one in the query, the same person performing any action, or the same person performing the same action. We use an adaptive search algorithm that focuses the analysis on relevant frames based on the inter-frame difference of foreground masks. Then, for each analyzed frame, a pedestrian detector is used to extract windows containing each pedestrian in the scene. For each detection, we use optical flow features to represent its action and color features to represent its appearance. These extracted features are used to compute the probability that the detection matches the query according to the specified criterion. The algorithm is fully unsupervised, i.e., no training or constraints on the appearance, actions or number of actions that will appear in the test video are made. The proposed algorithm is tested on a surveillance video with different people performing different actions, providing satisfactory retrieval performance.","PeriodicalId":74516,"journal":{"name":"Proceedings of the ... IAPR International Conference on Pattern Recognition. International Conference on Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76262271","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: 15

Learning to Rank the Severity of Unrepaired Cleft Lip Nasal Deformity on 3D Mesh Data. 基于三维网格数据的未修复唇裂鼻畸形严重程度排序学习。

Proceedings of the ... IAPR International Conference on Pattern Recognition. International Conference on Pattern Recognition Pub Date : 2014-08-01 DOI: 10.1109/ICPR.2014.88

Jia Wu, Raymond Tse, Linda G Shapiro

引用次数: 9

A New Approach of Arc Skeletonization for Tree-Like Objects Using Minimum Cost Path. 一种基于最小代价路径的树形物体弧骨架化新方法。

Proceedings of the ... IAPR International Conference on Pattern Recognition. International Conference on Pattern Recognition Pub Date : 2014-08-01 DOI: 10.1109/ICPR.2014.172

Dakai Jin, Krishna S Iyer, Eric A Hoffman, Punam K Saha

{"title":"A New Approach of Arc Skeletonization for Tree-Like Objects Using Minimum Cost Path.","authors":"Dakai Jin, Krishna S Iyer, Eric A Hoffman, Punam K Saha","doi":"10.1109/ICPR.2014.172","DOIUrl":"https://doi.org/10.1109/ICPR.2014.172","url":null,"abstract":"Traditional arc skeletonization algorithms using the principle of Blum's transform, often, produce unwanted spurious branches due to boundary irregularities and digital effects on objects and other artifacts. This paper presents a new robust approach of extracting arc skeletons for three-dimensional (3-D) elongated fuzzy objects, which avoids spurious branches without requiring post-pruning. Starting from a root voxel, the method iteratively expands the skeleton by adding a new branch in each iteration that connects the farthest voxel to the current skeleton using a minimum-cost geodesic path. The path-cost function is formulated using a novel measure of local significance factor defined by fuzzy distance transform field, which forces the path to stick to the centerline of the object. The algorithm terminates when dilated skeletal branches fill the entire object volume or the current farthest voxel fails to generate a meaningful branch. Accuracy of the algorithm has been evaluated using computer-generated blurred and noisy phantoms with known skeletons. Performance of the method in terms of false and missing skeletal branches, as defined by human expert, has been examined using in vivo CT imaging of human intrathoracic airways. Experimental results from both experiments have established the superiority of the new method as compared to a widely used conventional method in terms of accuracy of medialness as well as robustness of true and false skeletal branches.","PeriodicalId":74516,"journal":{"name":"Proceedings of the ... IAPR International Conference on Pattern Recognition. International Conference on Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/ICPR.2014.172","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33003518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Watershed Merge Tree Classification for Electron Microscopy Image Segmentation. 分水岭合并树分类用于电子显微镜图像分割。

Proceedings of the ... IAPR International Conference on Pattern Recognition. International Conference on Pattern Recognition Pub Date : 2012-11-01

Ting Liu, Elizabeth Jurrus, Mojtaba Seyedhosseini, Mark Ellisman, Tolga Tasdizen

引用次数: 0

3D shape isometric correspondence by spectral assignment. 光谱分配的三维形状等距对应。

Proceedings of the ... IAPR International Conference on Pattern Recognition. International Conference on Pattern Recognition Pub Date : 2012-01-01

Xiang Pan, Linda Shapiro

引用次数: 0

Edge Based Binarization for Video Text Images 基于边缘的视频文本图像二值化

Proceedings of the ... IAPR International Conference on Pattern Recognition. International Conference on Pattern Recognition Pub Date : 2010-08-23 DOI: 10.1109/ICPR.2010.41

Zhou Zhiwei, Liu Linlin, T. C. Lim

引用次数: 37

Locally Deformable Shape Model to Improve 3D Level Set based Esophagus Segmentation. 局部变形形状模型改进基于水平集的食道三维分割。

Proceedings of the ... IAPR International Conference on Pattern Recognition. International Conference on Pattern Recognition Pub Date : 2010-08-23 DOI: 10.1109/ICPR.2010.962

Sila Kurugol, Necmiye Ozay, Jennifer G Dy, Gregory C Sharp, Dana H Brooks

引用次数: 7

Content Adaptive Hash Lookups for Near-Duplicate Image Search by Full or Partial Image Queries 内容自适应哈希查找近重复图像搜索的全部或部分图像查询

Proceedings of the ... IAPR International Conference on Pattern Recognition. International Conference on Pattern Recognition Pub Date : 2010-08-23 DOI: 10.1109/ICPR.2010.391

Harmanci Oztan, R. HaritaogluIsmail

引用次数: 1