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

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

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

Convolutional 3D Attention Network for Video Based Freezing of Gait Recognition 基于视频冻结的卷积三维注意网络步态识别

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615791

Renfei Sun, Zhiyong Wang, K. E. Martens, S. Lewis

{"title":"Convolutional 3D Attention Network for Video Based Freezing of Gait Recognition","authors":"Renfei Sun, Zhiyong Wang, K. E. Martens, S. Lewis","doi":"10.1109/DICTA.2018.8615791","DOIUrl":"https://doi.org/10.1109/DICTA.2018.8615791","url":null,"abstract":"Freezing of gait (FoG) is defined as a brief, episodic absence or marked reduction of forward progression of the feet despite the intention to walk. It is a typical symptom of Parkinson's disease (PD) and has a significant impact on the life quality of PD patients. Generally trained experts need to review the gait of a patient for clinical diagnosis, which is time consuming and subjective. Nowadays, automatic FoG identification from videos provides a promising solution to address these issues by formulating FoG identification as a human action recognition task. However, most existing human action recognition algorithms are limited in this task as FoG is very subtle and can be easily overlooked when being interfered with by irrelevant motion. In this paper, we propose a novel action recognition algorithm, namely convolutional 3D attention network (C3DAN), to address this issue by learning an informative region for more effective recognition. The network consists of two main parts: Spatial Attention Network (SAN) and 3-dimensional convolutional network (C3D). SAN aims to generate an attention region from coarse to fine, while C3D extracts discriminative features. Our proposed approach is able to localize attention region without manual annotation and to extract discriminative features in an end-to-end way. We evaluate our proposed C3DAN method on a video dataset collected from 45 PD patients in a clinical setting for the quantification of FoG in PD. We obtained sensitivity of 68.2%, specificity of 80.8% and accuracy of 79.3%, which outperformed several state-of-the-art human action recognition methods. To the best of our knowledge, our work is one of the first studies detecting FoG from clinical videos.","PeriodicalId":130057,"journal":{"name":"2018 Digital Image Computing: Techniques and Applications (DICTA)","volume":"39 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":"122837838","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

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

Image Enhancement for Face Recognition in Adverse Environments 不利环境下人脸识别的图像增强

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615793

D. Kamenetsky, Sau Yee Yiu, Martyn Hole

引用次数: 4

Object Classification using Deep Learning on Extremely Low-Resolution Time-of-Flight Data 在极低分辨率飞行时间数据上使用深度学习的目标分类

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615877

Ana Daysi Ruvalcaba-Cardenas, T. Scoleri, Geoffrey Day

{"title":"Object Classification using Deep Learning on Extremely Low-Resolution Time-of-Flight Data","authors":"Ana Daysi Ruvalcaba-Cardenas, T. Scoleri, Geoffrey Day","doi":"10.1109/DICTA.2018.8615877","DOIUrl":"https://doi.org/10.1109/DICTA.2018.8615877","url":null,"abstract":"This paper proposes two novel deep learning models for 2D and 3D classification of objects in extremely low-resolution time-of-flight imagery. The models have been developed to suit contemporary range imaging hardware based on a recently fabricated Single Photon Avalanche Diode (SPAD) camera with 64 χ 64 pixel resolution. Being the first prototype of its kind, only a small data set has been collected so far which makes it challenging for training models. To bypass this hurdle, transfer learning is applied to the widely used VGG-16 convolutional neural network (CNN), with supplementary layers added specifically to handle SPAD data. This classifier and the renowned Faster-RCNN detector offer benchmark models for comparison to a newly created 3D CNN operating on time-of-flight data acquired by the SPAD sensor. Another contribution of this work is the proposed shot noise removal algorithm which is particularly useful to mitigate the camera sensitivity in situations of excessive lighting. Models have been tested in both low-light indoor settings and outdoor daytime conditions, on eight objects exhibiting small physical dimensions, low reflectivity, featureless structures and located at ranges from 25m to 700m. Despite antagonist factors, the proposed 2D model has achieved 95% average precision and recall, with higher accuracy for the 3D model.","PeriodicalId":130057,"journal":{"name":"2018 Digital Image Computing: Techniques and Applications (DICTA)","volume":"75 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":"128587255","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: 7

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