2019 Digital Image Computing: Techniques and Applications (DICTA)最新文献_第6页

Explaining Machine Learning-Based Classifications of In-Vivo Gastral Images 解释基于机器学习的活体胃图像分类

2019 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2019-12-01 DOI: 10.1109/DICTA47822.2019.8945986

A. Malhi, Timotheus Kampik, H. Pannu, Manik Madhikermi, Kary Främling

引用次数: 23

Improved Image Analysis Methodology for Detecting Changes in Evidence Positioning at Crime Scenes 用于检测犯罪现场证据定位变化的改进图像分析方法

2019 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2019-12-01 DOI: 10.1109/DICTA47822.2019.8945934

Mark Petty, S. Teng, Manzur Murshed

{"title":"Improved Image Analysis Methodology for Detecting Changes in Evidence Positioning at Crime Scenes","authors":"Mark Petty, S. Teng, Manzur Murshed","doi":"10.1109/DICTA47822.2019.8945934","DOIUrl":"https://doi.org/10.1109/DICTA47822.2019.8945934","url":null,"abstract":"This paper proposed an improved methodology to assist forensic investigators in detecting positional change of objects due to crime scene contamination. Either intentionally or by accident, crime scene contamination can occur during the investigation and documentation process. This new proposed methodology utilises an ASIFT-based feature detection algorithm that compares pre- and post-contaminated images of the same scene, taken from different viewpoints. The contention is that the ASIFT registration technique is better suited to real world crime scene photography, being more robust to affine distortion that occurs when capturing images from different viewpoints. The proposed methodology was tested with both the SIFT and ASIFT registration techniques to show that (1) it could identify missing, planted and displaced objects using both SIFT and ASIFT and (2) ASIFT is superior to SIFT in terms of error in displacement estimation, especially for larger viewpoint discrepancies between the pre- and post-contamination images. This supports the contention that our proposed methodology in combination with ASIFT is better suited to handle real world crime scene photography.","PeriodicalId":6696,"journal":{"name":"2019 Digital Image Computing: Techniques and Applications (DICTA)","volume":"252 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91007865","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

Change Detection over the State of Queensland using High Resolution Planet Satellite Mosaics 使用高分辨率行星卫星马赛克探测昆士兰州的变化

2019 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2019-12-01 DOI: 10.1109/DICTA47822.2019.8945942

C. McLaughlin, Holly Hutson, Lance De Vine, A. Woodley, S. Geva, Timothy Chappell, W. Kelly, W. Boles, Dimitri Perrin

引用次数: 2

Facial Gender Classification — Analysis using Convolutional Neural Networks 面部性别分类-使用卷积神经网络的分析

2019 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2019-12-01 DOI: 10.1109/DICTA47822.2019.8946109

Brian Lee, S. Z. Gilani, G. Hassan, A. Mian

引用次数: 6

Part-Based Feature Aggregation Method for Dynamic Scene Recognition 基于局部特征聚合的动态场景识别方法

2019 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2019-12-01 DOI: 10.1109/DICTA47822.2019.8946036

Xiaoming Peng, A. Bouzerdoum

引用次数: 3

Semantic Segmentation under Severe Imaging Conditions 严重成像条件下的语义分割

2019 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2019-12-01 DOI: 10.1109/DICTA47822.2019.8945923

Hoda Imam, Bassem A. Abdullah, H. A. E. Munim

引用次数: 2

Modelling and Flame Segmentation for Real-Time Monitoring of Rotary Kilns 回转窑实时监测建模与火焰分割

2019 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2019-12-01 DOI: 10.1109/DICTA47822.2019.8945833

J. Ridley, Duc-Son Pham, M. Lazarescu

引用次数: 1

From Chest X-Rays to Radiology Reports: A Multimodal Machine Learning Approach 从胸部x光到放射学报告:一种多模式机器学习方法

2019 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2019-12-01 DOI: 10.1109/DICTA47822.2019.8945819

Sonit Singh, Sarvnaz Karimi, K. Ho-Shon, Len Hamey

{"title":"From Chest X-Rays to Radiology Reports: A Multimodal Machine Learning Approach","authors":"Sonit Singh, Sarvnaz Karimi, K. Ho-Shon, Len Hamey","doi":"10.1109/DICTA47822.2019.8945819","DOIUrl":"https://doi.org/10.1109/DICTA47822.2019.8945819","url":null,"abstract":"Interpreting medical images and summarising them in the form of radiology reports is a challenging, tedious, and complex task. A radiologist provides a complete description of a medical image in the form of radiology report by describing normal or abnormal findings and providing a summary for decision making. Research shows that the radiology practice is error-prone due to the limited number of experts, increasing patient volumes, and the subjective nature of human perception. To reduce the number of diagnostic errors and to alleviate the task of radiologists, there is a need for a computer-aided report generation system that can automatically generate a radiology report for a given medical image. We propose an encoder-decoder based framework that can automatically generate radiology reports from medical images. Specifically, we use a Convolutional Neural Network as an encoder coupled with a multi-stage Stacked Long Short-Term Memory as a decoder to generate reports. We perform experiments on the Indiana University Chest X-ray collection, a publicly available dataset, to measure the effectiveness of our model. Experimental results show the effectiveness of our model in automatically generating radiology reports from medical images.","PeriodicalId":6696,"journal":{"name":"2019 Digital Image Computing: Techniques and Applications (DICTA)","volume":"1 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84169788","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: 17

Object Graph Networks for Spatial Language Grounding 空间语言基础的对象图网络

2019 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2019-12-01 DOI: 10.1109/DICTA47822.2019.8946101

Philip Hawkins, F. Maire, S. Denman, Mahsa Baktash

{"title":"Object Graph Networks for Spatial Language Grounding","authors":"Philip Hawkins, F. Maire, S. Denman, Mahsa Baktash","doi":"10.1109/DICTA47822.2019.8946101","DOIUrl":"https://doi.org/10.1109/DICTA47822.2019.8946101","url":null,"abstract":"Consider a domestic robot being asked to pick up \"the cup nearest to the plate\". Natural language is an intuitive way for humans to interact with robots. However, enabling robots to comprehend natural language, and correctly interpret spatial references, is challenging for two reasons. Firstly, phrases must be semantically represented in structures that can be processed computationally; secondly correspondences must be found to map these structures to models that represent objects, relationships and actions in the environment. Recently neural networks have demonstrated a strong potential to address both challenges, most notably in the context of Visual Question Answering (VQA) where they have performed well at answering natural language questions about images. However, the state-of-the-art networks for VQA tasks are not directly applicable to robotic applications. They do not support interfaces suitable for integration with a robotic system and most have a limited capacity to interpret spatial phrases. In this paper we present a neural network architecture trained on synthetic data and evaluated on synthetic and real data. It correctly interprets referring spatial relationships in phrases such as the one above and provides a modular interface that allows a robot to localise an object in the environment from such a phrase.","PeriodicalId":6696,"journal":{"name":"2019 Digital Image Computing: Techniques and Applications (DICTA)","volume":"31 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84214438","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

Social Network Analysis of an Acoustic Environment: The Use of Visualised Data to Characterise Natural Habitats 声环境的社会网络分析:使用可视化数据来描述自然栖息地

2019 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2019-12-01 DOI: 10.1109/DICTA47822.2019.8945963

Junling Wang, M. Sankupellay, D. Konovalov, M. Towsey, P. Roe

{"title":"Social Network Analysis of an Acoustic Environment: The Use of Visualised Data to Characterise Natural Habitats","authors":"Junling Wang, M. Sankupellay, D. Konovalov, M. Towsey, P. Roe","doi":"10.1109/DICTA47822.2019.8945963","DOIUrl":"https://doi.org/10.1109/DICTA47822.2019.8945963","url":null,"abstract":"(1) Background: Ecologists use acoustic recordings for long term environmental monitoring. However, as audio recordings are opaque, obtaining meaningful information from them is a challenging task. Calculating summary indices from recordings is one way to reduce the size of audio data, but the amount of information of summary indices is still too big. (2) Method: In this study we explore the application of social network analysis to visually and quantitatively model acoustic changes. To achieve our aim, we clustered summary indices using two algorithms, and the results were used to generate network maps. (3) Results and Discussion: The network maps allowed us to visually perceive acoustic changes in a day and to visually compare one day to another. To enable quantitative comparison, we also calculated summary values from the social network maps, including Gini coefficient (an economical concept adopted to estimate how unevenly the occurrences are distributed). (4) Conclusion: Social network maps and summary values provide insight into acoustic changes within an environment visually and quantitatively.","PeriodicalId":6696,"journal":{"name":"2019 Digital Image Computing: Techniques and Applications (DICTA)","volume":"1 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83109699","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: 2