2019 Digital Image Computing: Techniques and Applications (DICTA)最新文献_第8页

Temporal 3D Fully Connected Network for Water-Hazard Detection 用于水害检测的时间三维全连接网络

2019 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2019-12-01 DOI: 10.1109/DICTA47822.2019.8945849

Juntao Li, Chuong H. Nguyen, Shaodi You

引用次数: 2

To What Extent Does Downsampling, Compression, and Data Scarcity Impact Renal Image Analysis? 降采样、压缩和数据稀缺性在多大程度上影响肾脏图像分析?

2019 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2019-09-22 DOI: 10.1109/DICTA47822.2019.8945813

Can Peng, Kun Zhao, A. Wiliem, Teng Zhang, Peter Hobson, A. Jennings, B. Lovell

{"title":"To What Extent Does Downsampling, Compression, and Data Scarcity Impact Renal Image Analysis?","authors":"Can Peng, Kun Zhao, A. Wiliem, Teng Zhang, Peter Hobson, A. Jennings, B. Lovell","doi":"10.1109/DICTA47822.2019.8945813","DOIUrl":"https://doi.org/10.1109/DICTA47822.2019.8945813","url":null,"abstract":"The condition of the Glomeruli, or filter sacks, in renal Direct Immunofluorescence (DIF) specimens is a critical indicator for diagnosing kidney diseases. A digital pathology system which digitizes a glass histology slide into a Whole Slide Image (WSI) and then automatically detects and zooms in on the glomeruli with a higher magnification objective will be extremely helpful for pathologists. In this paper, using glomerulus detection as the study case, we provide analysis and observations on several important issues to help with the development of Computer Aided Diagnostic (CAD) systems to process WSIs. Large image resolution, large file size, and data scarcity are always challenging to deal with. To this end, we first examine image downsampling rates in terms of their effect on detection accuracy. Second, we examine the impact of image compression. Third, we examine the relationship between the size of the training set and detection accuracy. To understand the above issues, experiments are performed on the state-of-the-art detectors: Faster R-CNN, R-FCN, Mask R-CNN and SSD. Critical findings are observed: (1) The best balance between detection accuracy, detection speed and file size is achieved at 8 times downsampling captured with a 40 χ objective; (2) compression which reduces the file size dramatically, does not necessarily have an adverse effect on overall accuracy; (3) reducing the amount of training data to some extents causes a drop in precision but has a negligible impact on the recall; (4) in most cases, Faster R-CNN achieves the best accuracy in the glomerulus detection task. We show that the image file size of 40 χ WSI images can be reduced by a factor of over 6000 with negligible loss of glomerulus detection accuracy.","PeriodicalId":6696,"journal":{"name":"2019 Digital Image Computing: Techniques and Applications (DICTA)","volume":"36 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2019-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80540993","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: 4

Class Activation Map Generation by Multiple Level Class Grouping and Orthogonal Constraint 基于多级类分组和正交约束的类激活图生成

2019 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2019-09-21 DOI: 10.1109/DICTA47822.2019.8946068

Kaixu Huang, Fanman Meng, Hongliang Li, Shuai Chen, Qingbo Wu, K. Ngan

{"title":"Class Activation Map Generation by Multiple Level Class Grouping and Orthogonal Constraint","authors":"Kaixu Huang, Fanman Meng, Hongliang Li, Shuai Chen, Qingbo Wu, K. Ngan","doi":"10.1109/DICTA47822.2019.8946068","DOIUrl":"https://doi.org/10.1109/DICTA47822.2019.8946068","url":null,"abstract":"Class activation map (CAM) highlights regions of classes based on classification network, which is widely used in weakly supervised tasks. However, it faces the problem that the class activation regions are usually small and local. Although several efforts paid to the second step (the CAM generation step) have partially enhanced the generation, we believe such problem is also caused by the first step (training step), because single classification model trained on the entire classes contains finite discriminate information that limits the object region extraction. To this end, this paper solves CAM generation by using multiple classification models. To form multiple classification networks that carry different discriminative information, we try to capture the semantic relationships between classes to form different semantic levels of classification models. Specifically, hierarchical clustering based on class relationships is used to form hierarchical clustering results, where the clustering levels are treated as semantic levels to form the classification models. Moreover, a new orthogonal module and a two-branch based CAM generation method are proposed to generate class regions that are orthogonal and complementary. We use the PASCAL VOC 2012 dataset to verify the proposed method. Experimental results show that our approach improves the CAM generation.","PeriodicalId":6696,"journal":{"name":"2019 Digital Image Computing: Techniques and Applications (DICTA)","volume":"42 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2019-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79851500","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: 4

Deep Latent Space Learning for Cross-Modal Mapping of Audio and Visual Signals 音频和视觉信号跨模态映射的深度潜在空间学习

2019 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2019-09-18 DOI: 10.1109/DICTA47822.2019.8945863

Shah Nawaz, Muhammad Kamran Janjua, I. Gallo, A. Mahmood, Alessandro Calefati

引用次数: 19

Corporate IT-Support Help-Desk Process Hybrid-Automation Solution with Machine Learning Approach 采用机器学习方法的企业it支持服务台流程混合自动化解决方案

2019 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2019-09-18 DOI: 10.1109/DICTA47822.2019.8946083

Kuruparan Shanmugalingam, Nisal Chandrasekara, Calvin Hindle, Gihan Fernando, Chanaka Gunawardhana

{"title":"Corporate IT-Support Help-Desk Process Hybrid-Automation Solution with Machine Learning Approach","authors":"Kuruparan Shanmugalingam, Nisal Chandrasekara, Calvin Hindle, Gihan Fernando, Chanaka Gunawardhana","doi":"10.1109/DICTA47822.2019.8946083","DOIUrl":"https://doi.org/10.1109/DICTA47822.2019.8946083","url":null,"abstract":"Comprehensive IT support teams in large scale organizations require more man power for handling engagement and requests of employees from different channels on a 24×7 basis. Automated email technical queries help desk is proposed to have instant real-time quick solutions and email categorisation. Email topic modelling with various machine learning, deep-learning approaches are compared with different features for a scalable, generalised solution along with sure-shot static rules. Email's title, body, attachment, OCR text, and some feature engineered custom features are given as input elements. XGBoost cascaded hierarchical models, Bi-LSTM model with word embeddings perform well showing 77.3 overall accuracy For the real world corporate email data set. By introducing the thresholding techniques, the overall automation system architecture provides 85.6 percentage of accuracy for real world corporate emails. Combination of quick fixes, static rules, ML categorization as a low cost inference solution reduces 81 percentage of the human effort in the process of automation and real time implementation.","PeriodicalId":6696,"journal":{"name":"2019 Digital Image Computing: Techniques and Applications (DICTA)","volume":"36 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2019-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81105482","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: 3

Data-Efficient Classification of Birdcall through Convolutional Neural Networks Transfer Learning 基于卷积神经网络迁移学习的鸟叫声数据高效分类

2019 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2019-09-17 DOI: 10.1109/DICTA47822.2019.8946016

Dina B. Efremova, M. Sankupellay, D. Konovalov

引用次数: 13

Picture What You Read 想象你所读的内容

2019 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2019-09-09 DOI: 10.1109/DICTA47822.2019.8946033

I. Gallo, Shah Nawaz, Alessandro Calefati, Riccardo La Grassa, Nicola Landro

引用次数: 0

A Classification Methodology Based on Subspace Graphs Learning 基于子空间图学习的分类方法

2019 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2019-09-09 DOI: 10.1109/DICTA47822.2019.8946011

Riccardo La Grassa, I. Gallo, Alessandro Calefati, D. Ognibene

引用次数: 2

Automatic Weight Estimation of Harvested Fish from Images 从图像中自动估计收获鱼的重量

2019 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2019-09-06 DOI: 10.1109/DICTA47822.2019.8945971

D. Konovalov, Alzayat Saleh, Dina B. Efremova, J. Domingos, D. Jerry

引用次数: 35

Generalised Zero-Shot Learning with Domain Classification in a Joint Semantic and Visual Space 语义和视觉联合空间中具有领域分类的广义零学习

2019 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2019-08-14 DOI: 10.1109/DICTA47822.2019.8945949

Rafael Felix, Ben Harwood, M. Sasdelli, G. Carneiro

{"title":"Generalised Zero-Shot Learning with Domain Classification in a Joint Semantic and Visual Space","authors":"Rafael Felix, Ben Harwood, M. Sasdelli, G. Carneiro","doi":"10.1109/DICTA47822.2019.8945949","DOIUrl":"https://doi.org/10.1109/DICTA47822.2019.8945949","url":null,"abstract":"Generalised zero-shot learning (GZSL) is a classification problem where the learning stage relies on a set of seen visual classes and the inference stage aims to identify both the seen visual classes and a new set of unseen visual classes. Critically, both the learning and inference stages can leverage a semantic representation that is available for the seen and unseen classes. Most state-of-the-art GZSL approaches rely on a mapping between latent visual and semantic spaces without considering if a particular sample belongs to the set of seen or unseen classes. In this paper, we propose a novel GZSL method that learns a joint latent representation that combines both visual and semantic information. This mitigates the need for learning a mapping between the two spaces. Our method also introduces a domain classification that estimates whether a sample belongs to a seen or an unseen class. Our classifier then combines a class discriminator with this domain classifier with the goal of reducing the natural bias that GZSL approaches have toward the seen classes. Experiments show that our method achieves state-of-the-art results in terms of harmonic mean, the area under the seen and unseen curve and unseen classification accuracy on public GZSL benchmark data sets. Our code will be available upon acceptance of this paper.","PeriodicalId":6696,"journal":{"name":"2019 Digital Image Computing: Techniques and Applications (DICTA)","volume":"27 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2019-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74563602","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: 9