2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ)最新文献

Improving the Efficient Neural Architecture Search via Rewarding Modifications 基于奖励修正的高效神经结构搜索

2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ) Pub Date : 2020-11-25 DOI: 10.1109/IVCNZ51579.2020.9290732

I. Gallo, Gabriele Magistrali, Nicola Landro, Riccardo La Grassa

{"title":"Improving the Efficient Neural Architecture Search via Rewarding Modifications","authors":"I. Gallo, Gabriele Magistrali, Nicola Landro, Riccardo La Grassa","doi":"10.1109/IVCNZ51579.2020.9290732","DOIUrl":"https://doi.org/10.1109/IVCNZ51579.2020.9290732","url":null,"abstract":"Nowadays, a challenge for the scientific community concerning deep learning is to design architectural models to obtain the best performance on specific data sets. Building effective models is not a trivial task and it can be very time-consuming if done manually. Neural Architecture Search (NAS) has achieved remarkable results in deep learning applications in the past few years. It involves training a recurrent neural network (RNN) controller using Reinforcement Learning (RL) to automatically generate architectures. Efficient Neural Architecture Search (ENAS) was created to address the prohibitively expensive computational complexity of NAS using weight sharing. In this paper we propose Improved-ENAS (I-ENAS), a further improvement of ENAS that augments the reinforcement learning training method by modifying the reward of each tested architecture according to the results obtained in previously tested architectures. We have conducted many experiments on different public domain datasets and demonstrated that I-ENAS, in the worst-case reaches the performance of ENAS, but in many other cases it overcomes ENAS in terms of convergence time needed to achieve better accuracies.","PeriodicalId":164317,"journal":{"name":"2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115363647","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

Experimental Validation of Bias in Checkerboard Corner Detection 棋盘角点检测中偏差的实验验证

2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ) Pub Date : 2020-11-25 DOI: 10.1109/IVCNZ51579.2020.9290652

M. J. Edwards, M. Hayes, R. Green

引用次数: 2

Predicting physician gaze in clinical settings using optical flow and positioning 预测医生的目光在临床设置使用光流和定位

2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ) Pub Date : 2020-11-25 DOI: 10.1109/IVCNZ51579.2020.9290716

A. Govindaswamy, E. Montague, D. Raicu, J. Furst

{"title":"Predicting physician gaze in clinical settings using optical flow and positioning","authors":"A. Govindaswamy, E. Montague, D. Raicu, J. Furst","doi":"10.1109/IVCNZ51579.2020.9290716","DOIUrl":"https://doi.org/10.1109/IVCNZ51579.2020.9290716","url":null,"abstract":"Electronic health record systems used in clinical settings to facilitate informed decision making, affects the dynamics between the physician and the patient during clinical interactions. The interaction between the patient and the physician can impact patient satisfaction, and overall health outcomes. Gaze during patient-doctor interactions was found to impact patient-physician relationship and is an important measure of attention towards humans and technology. This study aims to automatically label physician gaze for video interactions which is typically measured using extensive human coding. In this study, physicians’ gaze is predicted at any time during the recorded video interaction using optical flow and body positioning coordinates as image features. Findings show that physician gaze could be predicted with an accuracy of over 83%. Our approach highlights the potential for the model to be an annotation tool which reduces the extensive human labor of annotating the videos for physician’s gaze. These interactions can further be connected to patient ratings to better understand patient outcomes.","PeriodicalId":164317,"journal":{"name":"2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124995853","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

Wavelet Based Thresholding for Fourier Ptychography Microscopy 基于小波阈值的傅里叶显微摄影

2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ) Pub Date : 2020-11-25 DOI: 10.1109/IVCNZ51579.2020.9290707

Nazabat Hussain, Mojde Hasanzade, D. Breiby, M. Akram

{"title":"Wavelet Based Thresholding for Fourier Ptychography Microscopy","authors":"Nazabat Hussain, Mojde Hasanzade, D. Breiby, M. Akram","doi":"10.1109/IVCNZ51579.2020.9290707","DOIUrl":"https://doi.org/10.1109/IVCNZ51579.2020.9290707","url":null,"abstract":"Computational microscopy algorithms can be used to improve resolution by synthesizing a bigger numerical aperture. Fourier Ptychographic (FP) microscopy utilizes multiple exposures, each illuminated with a unique incidence angle coherent source. The recorded images are often corrupted with background noises and preprocessing improves the quality of the FP recovered image. The preprocessing involves data denoising, thresholding and intensity balancing. We propose a wavelet-based thresholding scheme for noise removal. Any image can be decomposed into its coarse approximation, horizontal details, vertical details, and diagonal details using suitable wavelets. The details are extracted to find a suitable threshold, which is used to perform thresholding. In the proposed algorithm, two wavelet families, Daubechies and Biorthogonal with compact support of db4, db30, bior2.2 and bior6.8, have been used in conjunction with ptychographic phase retrieval. The obtained results show that the wavelet-based thresholding significantly improves the quality of the reconstructed FP microscopy image.","PeriodicalId":164317,"journal":{"name":"2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115203192","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

Leveraging Linguistically-aware Object Relations and NASNet for Image Captioning 利用语言感知对象关系和NASNet进行图像字幕

2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ) Pub Date : 2020-11-25 DOI: 10.1109/IVCNZ51579.2020.9290719

Naeha Sharif, M. Jalwana, Bennamoun, Wei Liu, Syed Afaq Ali Shah

引用次数: 3

CoCoNet: A Collaborative Convolutional Network applied to fine-grained bird species classification 椰网:一个应用于细粒度鸟类分类的协同卷积网络

2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ) Pub Date : 2020-11-25 DOI: 10.1109/IVCNZ51579.2020.9290677

Tapabrata (Rohan) Chakraborty, B. McCane, S. Mills, U. Pal

引用次数: 1

Evolutionary Algorithm Based Residual Block Search for Compression Artifact Removal 基于进化算法的残差块搜索压缩伪影去除

2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ) Pub Date : 2020-11-25 DOI: 10.1109/IVCNZ51579.2020.9290620

Rishil Shah

{"title":"Evolutionary Algorithm Based Residual Block Search for Compression Artifact Removal","authors":"Rishil Shah","doi":"10.1109/IVCNZ51579.2020.9290620","DOIUrl":"https://doi.org/10.1109/IVCNZ51579.2020.9290620","url":null,"abstract":"Lossy image compression is ubiquitously used for storage and transmission at lower rates. Among the existing lossy image compression methods, the JPEG standard is the most widely used technique in the multimedia world. Over the years, numerous methods have been proposed to suppress the compression artifacts introduced in JPEG-compressed images. However, all current learning-based methods include deep convolutional neural networks (CNNs) that are manually-designed by researchers. The network design process requires extensive computational resources and expertise. Focusing on this issue, we investigate evolutionary search for finding the optimal residual block based architecture for artifact removal. We first define a residual network structure and its corresponding genotype representation used in the search. Then, we provide details of the evolutionary algorithm and the multi-objective function used to find the optimal residual block architecture. Finally, we present experimental results to indicate the effectiveness of our approach and compare performance with existing artifact removal networks. The proposed approach is scalable and portable to numerous low-level vision tasks.","PeriodicalId":164317,"journal":{"name":"2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123208083","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

Deep Learning Methods for Virus Identification from Digital Images 基于数字图像的病毒识别深度学习方法

2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ) Pub Date : 2020-11-25 DOI: 10.1109/IVCNZ51579.2020.9290670

Luxin Zhang, W. Yan

{"title":"Deep Learning Methods for Virus Identification from Digital Images","authors":"Luxin Zhang, W. Yan","doi":"10.1109/IVCNZ51579.2020.9290670","DOIUrl":"https://doi.org/10.1109/IVCNZ51579.2020.9290670","url":null,"abstract":"The use of deep learning methods for virus identification from digital images is a timely research topic. Given an electron microscopy image, virus recognition utilizing deep learning approaches is critical at present, because virus identification by human experts is relatively slow and time-consuming. In this project, our objective is to develop deep learning methods for automatic virus identification from digital images, there are four viral species taken into consideration, namely, SARS, MERS, HIV, and COVID-19. In this work, we firstly examine virus morphological characteristics and propose a novel loss function which aims at virus identification from the given electron micrographs. We take into account of attention mechanism for virus locating and classification from digital images. In order to generate the most reliable estimate of bounding boxes and classification for a virus as visual object, we train and test five deep learning models: R-CNN, Fast R-CNN, Faster R-CNN, YOLO, and SSD, based on our dataset of virus electron microscopy. Additionally, we explicate the evaluation approaches. The conclusion reveals SSD and Faster R-CNN outperform in the virus identification.","PeriodicalId":164317,"journal":{"name":"2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126346106","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

Pothole Detection and Dimension Estimation System using Deep Learning (YOLO) and Image Processing 基于深度学习(YOLO)和图像处理的凹坑检测与尺寸估计系统

2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ) Pub Date : 2020-11-25 DOI: 10.1109/IVCNZ51579.2020.9290547

P. Chitale, Kaustubh Y. Kekre, Hrishikesh Shenai, R. Karani, Jay Gala

{"title":"Pothole Detection and Dimension Estimation System using Deep Learning (YOLO) and Image Processing","authors":"P. Chitale, Kaustubh Y. Kekre, Hrishikesh Shenai, R. Karani, Jay Gala","doi":"10.1109/IVCNZ51579.2020.9290547","DOIUrl":"https://doi.org/10.1109/IVCNZ51579.2020.9290547","url":null,"abstract":"The world is advancing towards an autonomous environment at a great pace and it has become a need of an hour, especially during the current pandemic situation. The pandemic has hindered the functioning of many sectors, one of them being Road development and maintenance. Creating a safe working environment for workers is a major concern of road maintenance during such difficult times. This can be achieved to some extent with the help of an autonomous system that will aim at reducing human dependency. In this paper, one of such systems, a pothole detection and dimension estimation, is proposed. The proposed system uses a Deep Learning based algorithm YOLO (You Only Look Once) for pothole detection. Further, an image processing based triangular similarity measure is used for pothole dimension estimation. The proposed system provides reasonably accurate results of both pothole detection and dimension estimation. The proposed system also helps in reducing the time required for road maintenance. The system uses a custom made dataset consisting of images of water-logged and dry potholes of various shapes and sizes.","PeriodicalId":164317,"journal":{"name":"2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129372719","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: 18

History and Evolution of Single Pass Connected Component Analysis 单道连通分量分析的历史与发展

2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ) Pub Date : 2020-11-25 DOI: 10.1109/IVCNZ51579.2020.9290585

D. Bailey

引用次数: 2