2020 Digital Image Computing: Techniques and Applications (DICTA)最新文献_第2页

Miss Yoga: A Yoga Assistant Mobile Application Based on Keypoint Detection Miss Yoga:一款基于关键点检测的瑜伽助手手机应用

2020 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2020-11-29 DOI: 10.1109/DICTA51227.2020.9363384

Renhao Huang, Jiqing Wang, Haowei Lou, Haodong Lu, Bofei Wang

引用次数: 4

An Improved Discriminator for GAN-Based Trajectory Prediction Models 基于gan的弹道预测模型的改进判别器

2020 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2020-11-29 DOI: 10.1109/DICTA51227.2020.9363414

Renhao Huang, Yang Song, M. Pagnucco

引用次数: 2

3D Reconstruction of Edge Line by ICP-based Matching with Geometric Constraints 基于icp的几何约束匹配边缘线三维重建

2020 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2020-11-29 DOI: 10.1109/DICTA51227.2020.9363373

Kojiro Takeyama

引用次数: 0

Branch Profiles for Shape Analysis 用于形状分析的分支型材

2020 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2020-11-29 DOI: 10.1109/DICTA51227.2020.9363421

Zayed M. Asiri, B. Martin, M. Bottema

引用次数: 0

Tuna Nutriment Tracking using Trajectory Mapping in Application to Aquaculture Fish Tank 基于轨迹映射的金枪鱼营养跟踪在水产养殖鱼缸中的应用

2020 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2020-11-29 DOI: 10.1109/DICTA51227.2020.9363387

Hilmil Pradana, K. Horio

{"title":"Tuna Nutriment Tracking using Trajectory Mapping in Application to Aquaculture Fish Tank","authors":"Hilmil Pradana, K. Horio","doi":"10.1109/DICTA51227.2020.9363387","DOIUrl":"https://doi.org/10.1109/DICTA51227.2020.9363387","url":null,"abstract":"The cost of fish feeding is usually around 40 percent of total production cost. Estimating a state of fishes in a tank and adjusting an amount of nutriments play an important role to manage cost of fish feeding system. Our approach is based on tracking nutriments on videos collected from an active aquaculture fish farm. Tracking approach is applied to acknowledge movement of nutriment to understand more about the fish behavior. Recently, there has been increasing number of researchers focused on developing tracking algorithms to generate more accurate and faster determination of object. Unfortunately, recent studies have shown that efficient and robust tracking of multiple objects with complex relations remain unsolved. Hence, focusing to develop tracking algorithm in aquaculture is more challenging because tracked object has a lot of aquatic variant creatures. By following aforementioned problem, we develop tuna nutriment tracking based on the classical minimum cost problem which consistently performs well in real environment datasets. In evaluation, the proposed method achieved 21.32 pixels and 3.08 pixels for average error distance and standard deviation, respectively. Quantitative evaluation based on the data generated by human annotators shows that the proposed method is valuable for aquaculture fish farm and can be widely applied to real environment datasets.","PeriodicalId":348164,"journal":{"name":"2020 Digital Image Computing: Techniques and Applications (DICTA)","volume":"726 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133282886","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

Fusing Visual Features and Metadata to Detect Flooding in Flickr Images 融合视觉特征和元数据来检测Flickr图像中的洪水

2020 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2020-11-29 DOI: 10.1109/DICTA51227.2020.9363418

R. Jony, A. Woodley, Dimitri Perrin

引用次数: 1

A 3D-2D Registration Method for Stereo Scan Overlay on Structure from Motion Model 运动模型结构立体扫描叠加的三维-二维配准方法

2020 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2020-11-29 DOI: 10.1109/DICTA51227.2020.9363423

Deepak Rajamohan, M. Garratt, M. Pickering

引用次数: 0

Multitask Learning for Video-based Surgical Skill Assessment 基于视频的多任务学习外科技能评估

2020 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2020-11-29 DOI: 10.1109/DICTA51227.2020.9363408

Zhiteng Jian, W. Yue, Qiuxia Wu, Wei Li, Zhiyong Wang, Vincent Lam

{"title":"Multitask Learning for Video-based Surgical Skill Assessment","authors":"Zhiteng Jian, W. Yue, Qiuxia Wu, Wei Li, Zhiyong Wang, Vincent Lam","doi":"10.1109/DICTA51227.2020.9363408","DOIUrl":"https://doi.org/10.1109/DICTA51227.2020.9363408","url":null,"abstract":"Surgical skill assessment (SSA) plays a vital role in medical systems for reducing intraoperative surgical errors and improving clinical outcomes. To ensure objective and efficient SSA, many automatic video-based SSA methods have been developed. In particular, various deep learning methods have been devised recently by utilising CNN or RNN-based networks for various skill assessment tasks (e.g., skill level prediction). While predicting overall skill levels and assessing detailed attribute-based scores are highly correlated, most existing studies deal with these two tasks separately, without fully exploiting different information sources encoded in a dataset. In contrast, we propose a novel end-to-end multitask learning framework to conduct skill level classification and attribute score regression jointly. Specifically, our network incorporates two branches for the two tasks, which share earlier layers for feature extraction and hold different prediction layers for specific targets. The shared feature extractor is optimised under the supervision of both tasks simultaneously, encouraging the model to consider information from different aspects and their relatedness to learn richer and more generalised features. In addition, since not every part of a surgical video contributes to skill assessment equally, we enhance an existing feature extractor I3D with a novel Spatio-Temporal & Channel Attention Module to emphasize important features. Experimental results on the public dataset JIGSAWS show that our proposed network outperforms state-of-the-art models on both skill classification and score regression tasks.","PeriodicalId":348164,"journal":{"name":"2020 Digital Image Computing: Techniques and Applications (DICTA)","volume":"15 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123537177","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

Automated Computational Diagnosis of Peripheral Retinal Pathology in Optical Coherence Tomography (OCT) Scans using Graph Theory 基于图论的光学相干断层扫描(OCT)视网膜病理自动计算诊断

2020 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2020-11-29 DOI: 10.1109/DICTA51227.2020.9363376

T. Lange, Stewart R. Lake, Karen Reynolds, M. Bottema

引用次数: 1

FANet: Feature Aggregation Network for Semantic Segmentation 语义分割的特征聚合网络

2020 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2020-11-29 DOI: 10.1109/DICTA51227.2020.9363370

Tanmay Singha, Duc-Son Pham, A. Krishna

{"title":"FANet: Feature Aggregation Network for Semantic Segmentation","authors":"Tanmay Singha, Duc-Son Pham, A. Krishna","doi":"10.1109/DICTA51227.2020.9363370","DOIUrl":"https://doi.org/10.1109/DICTA51227.2020.9363370","url":null,"abstract":"Due to the rapid development in robotics and autonomous industries, optimization and accuracy have become an important factor in the field of computer vision. It becomes a challenging task for the researchers to design an efficient, optimized model with high accuracy in the field of object detection and semantic segmentation. Some existing off-line scene segmentation methods have shown an outstanding result on different datasets at the cost of a large number of parameters and operations, whereas some well-known real-time semantic segmentation techniques have reduced the number of parameters and operations in demand for resource-constrained applications, but model accuracy is compromised. We propose a novel approach for scene segmentation suitable for resource-constrained embedded devices by keeping a right balance between model architecture and model performance. Exploiting the multi-scale feature fusion technique with accurate localization augmentation, we introduce a fast feature aggregation network, a real-time scene segmentation model capable of handling high-resolution input image (1024 × 2048 px). Relying on an efficient embedded vision backbone network, our feature pyramid network outperforms many existing off-line and real-time pixel-wise deep convolution neural networks (CNNs) and produces 89.7% pixel accuracy and 65.9% mean intersection over union (mIoU) on the Cityscapes benchmark validation dataset whilst having only 1.1M parameters and 5.8B FLOPS.","PeriodicalId":348164,"journal":{"name":"2020 Digital Image Computing: Techniques and Applications (DICTA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134139342","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