2019 International Conference on Image and Vision Computing New Zealand (IVCNZ)最新文献

Body Part Labelling with Minkowski Networks 用Minkowski网络标记身体部位

2019 International Conference on Image and Vision Computing New Zealand (IVCNZ) Pub Date : 2019-12-01 DOI: 10.1109/IVCNZ48456.2019.8961026

Joseph Cahill-Lane, S. Mills, Stuart Duncan

引用次数: 0

Comparison of Three-Dimensional Scanning Devices 三维扫描设备的比较

2019 International Conference on Image and Vision Computing New Zealand (IVCNZ) Pub Date : 2019-12-01 DOI: 10.1109/IVCNZ48456.2019.8961017

Niklas Deckers, R. Reulke

引用次数: 0

Plant Leaf Recognition using Geometric Features and Pearson Correlations 基于几何特征和Pearson相关性的植物叶片识别

2019 International Conference on Image and Vision Computing New Zealand (IVCNZ) Pub Date : 2019-12-01 DOI: 10.1109/IVCNZ48456.2019.8961036

Md. Ajij, D. S. Roy, Sanjoy Pratihar

引用次数: 2

A deep learning approach to handwritten text recognition in the presence of struck-out text 在剔除文本的情况下手写文本识别的深度学习方法

2019 International Conference on Image and Vision Computing New Zealand (IVCNZ) Pub Date : 2019-12-01 DOI: 10.1109/IVCNZ48456.2019.8961024

Hiqmat Nisa, J. Thom, V. Ciesielski, Ruwan Tennakoon

引用次数: 12

Are These Birds Similar: Learning Branched Networks for Fine-grained Representations 这些鸟类相似吗？学习细粒度表征的分支网络

2019 International Conference on Image and Vision Computing New Zealand (IVCNZ) Pub Date : 2019-12-01 DOI: 10.1109/IVCNZ48456.2019.8960960

Shah Nawaz, Alessandro Calefati, Moreno Caraffini, Nicola Landro, I. Gallo

引用次数: 9

Evaluating Spatial Configuration Constrained CNNs for Localizing Facial and Body Pose Landmarks 评估空间配置约束cnn定位面部和身体姿势地标

2019 International Conference on Image and Vision Computing New Zealand (IVCNZ) Pub Date : 2019-12-01 DOI: 10.1109/IVCNZ48456.2019.8961000

Christian Payer, D. Štern, M. Urschler

{"title":"Evaluating Spatial Configuration Constrained CNNs for Localizing Facial and Body Pose Landmarks","authors":"Christian Payer, D. Štern, M. Urschler","doi":"10.1109/IVCNZ48456.2019.8961000","DOIUrl":"https://doi.org/10.1109/IVCNZ48456.2019.8961000","url":null,"abstract":"Landmark localization is a widely used task required in medical image analysis and computer vision applications. Formulated in a heatmap regression framework, we have recently proposed a CNN architecture that learns on its own to split the localization task into two simpler sub-problems, dedicating one component to locally accurate but ambiguous predictions, while the other component improves robustness by incorporating the spatial configuration of landmarks to remove ambiguities. We learn this simplification in our SpatialConfiguration-Net (SCN) by multiplying the heatmap predictions of its two components and by training the network in and end-to-end manner, thus achieving regularization similar to e.g. a hand-crafted Markov Random Field model. While we have previously shown localization results solely on data from 2D and 3D medical imaging modalities, in this work our aim is to study the generalization capabilities of our SpatialConfiguration-Net to computer vision problems. Therefore, we evaluate our performance both in terms of accuracy and robustness on a facial alignment task, where we improve upon the state-of-the-art methods, as well as on a human body pose estimation task, where we demonstrate results in line with the recent state-of-the-art.","PeriodicalId":217359,"journal":{"name":"2019 International Conference on Image and Vision Computing New Zealand (IVCNZ)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128203119","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

Identifying Simple Shapes to Classify the Big Picture 识别简单的形状来分类大图

2019 International Conference on Image and Vision Computing New Zealand (IVCNZ) Pub Date : 2019-12-01 DOI: 10.1109/IVCNZ48456.2019.8960989

Megan Liang, Gabrielle Palado, Will N. Browne

{"title":"Identifying Simple Shapes to Classify the Big Picture","authors":"Megan Liang, Gabrielle Palado, Will N. Browne","doi":"10.1109/IVCNZ48456.2019.8960989","DOIUrl":"https://doi.org/10.1109/IVCNZ48456.2019.8960989","url":null,"abstract":"In recent years, Deep Artificial Neural Networks (DNNs) have demonstrated their ability in solving visual classification problems. However, an impediment is transparency where it is difficult to interpret why an object is classified in a particular way. Furthermore, it is also difficult to validate whether a learned model truly represents a problem space. Learning Classifier Systems (LCSs) are an Evolutionary Computation technique capable of producing human-readable rules that explain why an instance has been classified, i.e. the system is fully transparent. However, because they can encode complex relationships between features, they are not best suited to domains with a large number of input features, e.g. classification in pixel images. Thus, the aim of this work is to develop a novel DNN-LCS system where the former extracts features from pixels and the latter classifies objects from these features with clear decision boundaries. Results show that the system can explain its classification decisions on curated image data, e.g. plates have elliptical or rectangular shapes. This work represents a promising step towards explainable artificial intelligence in computer vision.","PeriodicalId":217359,"journal":{"name":"2019 International Conference on Image and Vision Computing New Zealand (IVCNZ)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115187003","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

An Image Mapping Approach for Quick Dissimilarity Detection of Binary Images 一种快速检测二值图像不相似度的图像映射方法

2019 International Conference on Image and Vision Computing New Zealand (IVCNZ) Pub Date : 2019-12-01 DOI: 10.1109/IVCNZ48456.2019.8961029

Adnan A. Y. Mustafa

引用次数: 0

Fusion of thermal and visible colour images for robust detection of people in forests 用于森林中人的鲁棒检测的热和可见彩色图像融合

2019 International Conference on Image and Vision Computing New Zealand (IVCNZ) Pub Date : 2019-12-01 DOI: 10.1109/IVCNZ48456.2019.8960964

J. Fourie, K. Pahalawatta, J. Hsiao, C. Bateman, Peter Carey

{"title":"Fusion of thermal and visible colour images for robust detection of people in forests","authors":"J. Fourie, K. Pahalawatta, J. Hsiao, C. Bateman, Peter Carey","doi":"10.1109/IVCNZ48456.2019.8960964","DOIUrl":"https://doi.org/10.1109/IVCNZ48456.2019.8960964","url":null,"abstract":"Safe operation of automated robotic platforms in environments where humans also work require on-board sensors that can accurately and robustly detect humans in the environment so that appropriate action can be taken. This is a challenging problem in unstructured outdoor environments as most sensors are negatively affected by changing environmental conditions like ambient light and moisture. Our aim is to use a combination of thermal and visible colour images to detect humans in forest environments. The system should be able to work through dense foliage and should not be confused by other objects that generate heat like machines or other animals. We developed and tested a system on a data-set of sensor data collected in a similar outdoor environment but with synthetic targets added to highlight the ability of the system to be robust to severe optical occlusion in dense vegetation and to the presence of hot machines that could fool the thermal sensor. Our initial results show promise and also highlight where improvements can be made with further testing in more realistic forest environments.","PeriodicalId":217359,"journal":{"name":"2019 International Conference on Image and Vision Computing New Zealand (IVCNZ)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129426583","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

Automated Segmentation of Breast Arterial Calcifications from Digital Mammography 数字乳房x线摄影中乳腺动脉钙化的自动分割

2019 International Conference on Image and Vision Computing New Zealand (IVCNZ) Pub Date : 2019-12-01 DOI: 10.1109/IVCNZ48456.2019.8960956

Kaier Wang, N. Khan, R. Highnam

{"title":"Automated Segmentation of Breast Arterial Calcifications from Digital Mammography","authors":"Kaier Wang, N. Khan, R. Highnam","doi":"10.1109/IVCNZ48456.2019.8960956","DOIUrl":"https://doi.org/10.1109/IVCNZ48456.2019.8960956","url":null,"abstract":"Breast arterial calcifications (BACs) are formed when calcium is deposited in the walls of arteries in the breast. The accurate segmentation of BACs is a critical step for risk assessment of cardiovascular disease from a mammogram. This paper evaluates the performance of three deep learning architectures, YOLO, Unet and DeepLabv3+, on detecting BACs in digital mammography. In comparison, a simple Hessian-based multiscale filter is developed to enhance BACs pattern, then a self-adaptive thresholding algorithm is applied to obtain the binary mask of BACs. As BACs are relatively small in size, we developed a new metric to better evaluate the small object segmentation. In this study, 135 digital mammographic images containing labelled BACs were obtained, in which 80% for training deep learning networks and 20% for validation. The results show that our Hessian-based filtering method achieves a highest accuracy on validation data, and DeepLabv3+ falls behind with little effectiveness. We conclude simple filtering technique is effective in BACs extraction, and DeepLabv3+ is an expensive alternative in terms of its computational cost and configuration complexity.","PeriodicalId":217359,"journal":{"name":"2019 International Conference on Image and Vision Computing New Zealand (IVCNZ)","volume":"145 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129581914","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