2018 Digital Image Computing: Techniques and Applications (DICTA)最新文献_第3页

In Situ Cane Toad Recognition 就地蔗蟾蜍识别

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615780

D. Konovalov, Simindokht Jahangard, L. Schwarzkopf

引用次数: 5

Left Ventricle Volume Measuring using Echocardiography Sequences 超声心动图序列测量左心室容积

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615766

Yi Guo, S. Green, L. Park, Lauren Rispen

引用次数: 1

Cluster-Based Crowd Movement Behavior Detection 基于集群的人群运动行为检测

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615809

Meng Yang, Lida Rashidi, A. S. Rao, S. Rajasegarar, Mohadeseh Ganji, M. Palaniswami, C. Leckie

{"title":"Cluster-Based Crowd Movement Behavior Detection","authors":"Meng Yang, Lida Rashidi, A. S. Rao, S. Rajasegarar, Mohadeseh Ganji, M. Palaniswami, C. Leckie","doi":"10.1109/DICTA.2018.8615809","DOIUrl":"https://doi.org/10.1109/DICTA.2018.8615809","url":null,"abstract":"Crowd behaviour monitoring and prediction is an important research topic in video surveillance that has gained increasing attention. In this paper, we propose a novel architecture for crowd event detection, which comprises methods for object detection, clustering of various groups of objects, characterizing the movement patterns of the various groups of objects, detecting group events, and finding the change point of group events. In our proposed framework, we use clusters to represent the groups of objects/people present in the scene. We then extract the movement patterns of the various groups of objects over the video sequence to detect movement patterns. We define several crowd events and propose a methodology to detect the change point of the group events over time. We evaluated our scheme using six video sequences from benchmark datasets, which include events such as walking, running, global merging, local merging, global splitting and local splitting. We compared our scheme with state of the art methods and showed the superiority of our method in accurately detecting the crowd behavioral changes.","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":"124800550","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

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615804

Di Wu, N. Sharma, M. Blumenstein

{"title":"Similar Gesture Recognition using Hierarchical Classification Approach in RGB Videos","authors":"Di Wu, N. Sharma, M. Blumenstein","doi":"10.1109/DICTA.2018.8615804","DOIUrl":"https://doi.org/10.1109/DICTA.2018.8615804","url":null,"abstract":"Recognizing human actions from the video streams has become one of the very popular research areas in computer vision and deep learning in the recent years. Action recognition is wildly used in different scenarios in real life, such as surveillance, robotics, healthcare, video indexing and human-computer interaction. The challenges and complexity involved in developing a video-based human action recognition system are manifold. In particular, recognizing actions with similar gestures and describing complex actions is a very challenging problem. To address these issues, we study the problem of classifying human actions using Convolutional Neural Networks (CNN) and develop a hierarchical 3DCNN architecture for similar gesture recognition. The proposed model firstly combines similar gesture pairs into one class, and classify them along with all other class, as a stage-1 classification. In stage-2, similar gesture pairs are classified individually, which reduces the problem to binary classification. We apply and evaluate the developed models to recognize the similar human actions on the HMDB51 dataset. The result shows that the proposed model can achieve high performance in comparison to the state-of-the-art methods.","PeriodicalId":130057,"journal":{"name":"2018 Digital Image Computing: Techniques and Applications (DICTA)","volume":"204 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":"131556502","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

Heuristic Evaluations of Cultural Heritage Websites 文化遗产网站的启发式评价

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615847

Duyen Lam, Atul Sajjanhar

引用次数: 3

Human Brain Tissue Segmentation in fMRI using Deep Long-Term Recurrent Convolutional Network 基于深度长期递归卷积网络的fMRI人脑组织分割

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615850

Sui Paul Ang, S. L. Phung, M. Schira, A. Bouzerdoum, S. T. Duong

引用次数: 8

Table Detection in Document Images using Foreground and Background Features 使用前景和背景特征的文档图像中的表检测

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615795

Saman Arif, F. Shafait

引用次数: 32

DICTA 2018 Keynotes

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/dicta.2018.8615756

引用次数: 0

DGDI: A Dataset for Detecting Glomeruli on Renal Direct Immunofluorescence DGDI:肾直接免疫荧光检测肾小球的数据集

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615769

Kun Zhao, Yuliang Tang, Teng Zhang, J. Carvajal, Daniel F. Smith, A. Wiliem, Peter Hobson, A. Jennings, B. Lovell

{"title":"DGDI: A Dataset for Detecting Glomeruli on Renal Direct Immunofluorescence","authors":"Kun Zhao, Yuliang Tang, Teng Zhang, J. Carvajal, Daniel F. Smith, A. Wiliem, Peter Hobson, A. Jennings, B. Lovell","doi":"10.1109/DICTA.2018.8615769","DOIUrl":"https://doi.org/10.1109/DICTA.2018.8615769","url":null,"abstract":"With the growing popularity of whole slide scanners, there is a high demand to develop computer aided diagnostic techniques for this new digitized pathology data. The ability to extract effective information from digital slides, which serve as fundamental representations of the prognostic data patterns or structures, provides promising opportunities to improve the accuracy of automatic disease diagnosis. The recent advances in computer vision have shown that Convolutional Neural Networks (CNNs) can be used to analyze digitized pathology images providing more consistent and objective information to the pathologists. In this paper, to advance the progress in developing computer aided diagnosis systems for renal direct immunofluorescence test, we introduce a new benchmark dataset for Detecting Glomeruli on renal Direct Immunofluorescence (DGDI). To build the baselines, we investigate various CNN-based detectors on DGDI. Experiments demonstrate that DGDI well represents the challenges of renal direct immunofluorescence image analysis and encourages the progress in developing new approaches for understanding renal disease.","PeriodicalId":130057,"journal":{"name":"2018 Digital Image Computing: Techniques and Applications (DICTA)","volume":"11 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":"121888617","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: 5

Size-Invariant Attention Accuracy Metric for Image Captioning with High-Resolution Residual Attention 具有高分辨率剩余注意的图像标题尺寸不变注意精度度量

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615788

Zongjian Zhang, Qiang Wu, Yang Wang, Fang Chen

{"title":"Size-Invariant Attention Accuracy Metric for Image Captioning with High-Resolution Residual Attention","authors":"Zongjian Zhang, Qiang Wu, Yang Wang, Fang Chen","doi":"10.1109/DICTA.2018.8615788","DOIUrl":"https://doi.org/10.1109/DICTA.2018.8615788","url":null,"abstract":"Spatial visual attention mechanisms have achieved significant performance improvements for image captioning. To quantitatively evaluate the performances of attention mechanisms, the \"attention correctness\" metric has been proposed to calculate the sum of attention weights generated for ground truth regions. However, this metric cannot consistently measure the attention accuracy among the element regions with large size variance. Moreover, its evaluations are inconsistent with captioning performances across different fine-grained attention resolutions. To address these problems, this paper proposes a size-invariant evaluation metric by normalizing the \"attention correctness\" metric with the size percentage of the attended region. To demonstrate the efficiency of our size-invariant metric, this paper further proposes a high-resolution residual attention model that uses RefineNet as the Fully Convolutional Network (FCN) encoder. By using the COCO-Stuff dataset, we can achieve pixel-level evaluations on both object and \"stuff\" regions. We use our metric to evaluate the proposed attention model across four high fine-grained resolutions (i.e., 27×27, 40×40, 60×60, 80×80). The results demonstrate that, compared with the \"attention correctness\" metric, our size-invariant metric is more consistent with the captioning performances and is more efficient for evaluating the attention accuracy.","PeriodicalId":130057,"journal":{"name":"2018 Digital Image Computing: Techniques and Applications (DICTA)","volume":"7 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":"124269994","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