2019 Digital Image Computing: Techniques and Applications (DICTA)最新文献_第4页

Deep Learning for Autonomous Driving 自动驾驶的深度学习

2019 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2019-12-01 DOI: 10.1109/DICTA47822.2019.8945818

Nicholas Burleigh, Jordan King, T. Bräunl

引用次数: 3

Improving Follicular Lymphoma Identification using the Class of Interest for Transfer Learning 利用兴趣班进行迁移学习提高滤泡性淋巴瘤的识别

2019 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2019-12-01 DOI: 10.1109/DICTA47822.2019.8946075

U. Somaratne, Kok Wai Wong, J. Parry, Ferdous Sohel, Xuequn Wang, Hamid Laga

{"title":"Improving Follicular Lymphoma Identification using the Class of Interest for Transfer Learning","authors":"U. Somaratne, Kok Wai Wong, J. Parry, Ferdous Sohel, Xuequn Wang, Hamid Laga","doi":"10.1109/DICTA47822.2019.8946075","DOIUrl":"https://doi.org/10.1109/DICTA47822.2019.8946075","url":null,"abstract":"Follicular Lymphoma (FL) is a type of lymphoma that grows silently and is usually diagnosed in its later stages. To increase the patients' survival rates, FL requires a fast diagnosis. While, traditionally, the diagnosis is performed by visual inspection of Whole Slide Images (WSI), recent advances in deep learning techniques provide an opportunity to automate this process. The main challenge, however, is that WSI images often exhibit large variations across different operating environments, hereinafter referred to as sites. As such, deep learning models usually require retraining using labeled data from each new site. This is, however, not feasible since the labelling process requires pathologists to visually inspect and label each sample. In this paper, we propose a deep learning model that uses transfer learning with fine-tuning to improve the identification of Follicular Lymphoma on images from new sites that are different from those used during training. Our results show that the proposed approach improves the prediction accuracy with 12% to 52% compared to the initial prediction of the model for images from a new site in the target environment.","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":"90485127","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

Perspective-Consistent Multifocus Multiview 3D Reconstruction of Small Objects 视角一致的多焦点多视角小物体三维重建

2019 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2019-12-01 DOI: 10.1109/DICTA47822.2019.8946006

Hengjia Li, Chuong V. Nguyen

{"title":"Perspective-Consistent Multifocus Multiview 3D Reconstruction of Small Objects","authors":"Hengjia Li, Chuong V. Nguyen","doi":"10.1109/DICTA47822.2019.8946006","DOIUrl":"https://doi.org/10.1109/DICTA47822.2019.8946006","url":null,"abstract":"Image-based 3D reconstruction or 3D photogrammetry of small-scale objects including insects and biological specimens is challenging due to the use of high magnification lens with inherent limited depth of field, and the object's fine structures and complex surface properties. Due to these challenges, traditional 3D reconstruction techniques cannot be applied without suitable image pre-processings. One such preprocessing technique is multifocus stacking that combines a set of partially focused images captured from the same viewing angle to create a single in-focus image. Traditional multifocus image capture uses a camera on a macro rail. Furthermore, the scale and shift are not properly considered by multifocus stacking techniques. As a consequence, the resulting in-focus images contain artifacts that violate perspective image formation. A 3D reconstruction using such images will fail to produce an accurate 3D model of the object. This paper shows how this problem can be solved effectively by a new multifocus stacking procedure which includes a new Fixed-Lens Multifocus Capture and camera calibration for image scale and shift. Initial experimental results are presented to confirm our expectation and show that the camera poses of fixed-lens images are at least 3-times less noisy than those of conventional moving lens images.","PeriodicalId":6696,"journal":{"name":"2019 Digital Image Computing: Techniques and Applications (DICTA)","volume":"5 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":"89370970","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

Adult or Child: Recognizing through Touch Gestures on Smartphones 成人或儿童:通过智能手机上的触摸手势识别

2019 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2019-12-01 DOI: 10.1109/DICTA47822.2019.8945816

Osama Rasheed, A. Rextin, Mehwish Nasim

{"title":"Adult or Child: Recognizing through Touch Gestures on Smartphones","authors":"Osama Rasheed, A. Rextin, Mehwish Nasim","doi":"10.1109/DICTA47822.2019.8945816","DOIUrl":"https://doi.org/10.1109/DICTA47822.2019.8945816","url":null,"abstract":"With the increasing popularity of smartphones and its audience including children as young as 2 year old, smartphones can be a hazard for young children in terms of health concerns, time wastage, viewing of inappropriate material and conversely children who are too young can be a threat to the smartphone as well e.g, causing battery drainage, making unwanted calls/text messages, doing physical damage etc. In order to protect the smartphone and children from each other, we require user identification on our devices so the device could perform certain functions for instance restricting adult content once a user is identified as a child. This paper is a user study that aims at detecting the touch patterns of adults and children. To this end we collected data from 60 people, 30 adults and 30 children while they were asked to perform the 6 basic tasks that are performed on touch devices to find the differences in the touch gestures of children from adults. We first perform an exploratory data analysis. We then model the problem as a supervised binary classification problem and use the data as input for different machine learning algorithms to find whether we can classify a user previously unknown to the machine as an adult or a child. Our work shows there are differences in touch gestures among children and adults which are sufficient for user group identification.","PeriodicalId":6696,"journal":{"name":"2019 Digital Image Computing: Techniques and Applications (DICTA)","volume":"250 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":"78358532","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

Automatic Generation of Lymphoma Post-Treatment PETs using Conditional-GANs 使用条件gan自动生成淋巴瘤治疗后pet

2019 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2019-12-01 DOI: 10.1109/DICTA47822.2019.8945835

G. Silva, Inês Domingues, Hugo Duarte, João A. M. Santos

{"title":"Automatic Generation of Lymphoma Post-Treatment PETs using Conditional-GANs","authors":"G. Silva, Inês Domingues, Hugo Duarte, João A. M. Santos","doi":"10.1109/DICTA47822.2019.8945835","DOIUrl":"https://doi.org/10.1109/DICTA47822.2019.8945835","url":null,"abstract":"Positron emission tomography (PET) imaging is a nuclear medicine functional imaging technique and as such it is expensive to perform and subjects the human body to radiation. Therefore, it would be ideal to find a technique that could allow for these images to be generated automatically. This generation can be done using deep learning techniques, more specifically with generative adversarial networks. As far as we are aware there have been no attempts at PET-to-PET generation to date. The objective of this article is to develop a generative adversarial network capable of generating after-treatment PET images from pre-treatment PET images. In order to develop this model, PET scans, originally in 3D, were converted to 2D images. Two methods were used, hand picking each slice and maximum intensity projection. After extracting the slices, several image co-registration techniques were applied in order to find which one would produce the best results according to two metrics, peak signal-to-noise ratio and structural similarity index. They achieved results of 18.8 and 0.856, respectively, using data from 90 patients with Hodgkin's Lymphoma.","PeriodicalId":6696,"journal":{"name":"2019 Digital Image Computing: Techniques and Applications (DICTA)","volume":"4 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74253307","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

Improved Detection for WAMI using Background Contextual Information 利用背景上下文信息改进WAMI检测

2019 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2019-12-01 DOI: 10.1109/DICTA47822.2019.8945924

Elena M. Vella, Anee Azim, H. Gaetjens, Boris Repasky, Timothy Payne

{"title":"Improved Detection for WAMI using Background Contextual Information","authors":"Elena M. Vella, Anee Azim, H. Gaetjens, Boris Repasky, Timothy Payne","doi":"10.1109/DICTA47822.2019.8945924","DOIUrl":"https://doi.org/10.1109/DICTA47822.2019.8945924","url":null,"abstract":"Current vehicle detection and tracking in imagery characterised by large ground coverage, low resolution and low frame rate data, such as Wide Area Motion Imagery (WAMI), does not reliably sustain vehicle tracks through start-stop movement profiles. This limits the continuity of tracks and its usefulness in higher level analysis such as pattern of behaviour or activity analysis. We develop and implement a two-step registration method to create well-registered images which are used to generate a novel low-noise representation of the static background context which is fed into our Context Convolutional Neural Network (C-CNN) detector. This network is unique as the C-CCN learns changing features in the scene and thus produces reliable, sustained vehicle detection independent of motion. A quantitative evaluation against WAMI imagery is presented for a Region of Interest (ROI) of the WPAFB 2009 annotated dataset [1]. We apply a Kalman filter tracker with WAMI-specific adaptions to the single frame C-CNN detections, and evaluate the results with respect to the tracking ground truth. We show improved detection and sustained tracking in WAMI using static background contextual information and reliably detect all vehicles that move, including vehicles that become stationary for short periods of time as they move through stop-start manoeuvres.","PeriodicalId":6696,"journal":{"name":"2019 Digital Image Computing: Techniques and Applications (DICTA)","volume":"145 1","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80461478","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

Single View 3D Point Cloud Reconstruction using Novel View Synthesis and Self-Supervised Depth Estimation 基于新颖视角合成和自监督深度估计的单视角三维点云重建

2019 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2019-12-01 DOI: 10.1109/DICTA47822.2019.8945841

A. Johnston, G. Carneiro

引用次数: 1

Deep Fusion Net for Coral Classification in Fluorescence and Reflectance Images 荧光和反射图像中珊瑚分类的深度融合网络

2019 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2019-12-01 DOI: 10.1109/DICTA47822.2019.8945925

Uzair Nadeem, Bennamoun, Ferdous Sohel, R. Togneri

引用次数: 3

Constructing Synthetic Chorio-Retinal Patches using Generative Adversarial Networks 利用生成对抗网络构建合成绒毛膜-视网膜补丁

2019 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2019-12-01 DOI: 10.1109/DICTA47822.2019.8946089

J. Kugelman, D. Alonso-Caneiro, Scott A. Read, Stephen J. Vincent, F. Chen, M. Collins

{"title":"Constructing Synthetic Chorio-Retinal Patches using Generative Adversarial Networks","authors":"J. Kugelman, D. Alonso-Caneiro, Scott A. Read, Stephen J. Vincent, F. Chen, M. Collins","doi":"10.1109/DICTA47822.2019.8946089","DOIUrl":"https://doi.org/10.1109/DICTA47822.2019.8946089","url":null,"abstract":"The segmentation of tissue layers in optical coherence tomography (OCT) images of the internal lining of the eye (the retina and choroid) is commonly performed for clinical and research purposes. However, manual segmentation of the numerous scans is time consuming, tedious and error-prone. Fortunately, machine learning-based automated approaches for image segmentation tasks are becoming more common. However, poor performance of these methods can result from a lack of quantity or diversity in the data used to train the models. Recently, generative adversarial networks (GANs) have demonstrated the ability to generate synthetic images, which may be useful for data augmentation purposes. Here, we propose the application of GANs to construct chorio-retinal patches from OCT images which may be used to augment data for a patch-based approach to boundary segmentation. Given the complexity of GAN training, a range of experiments are performed to optimize performance. We show that it is feasible to generate 32×32 versions of such patches that are visually indistinguishable from their real variants. In the best case, the segmentation performance utilizing solely synthetic data to train the model is nearly comparable to real data on all three layer boundaries of interest. The difference in mean absolute error for the inner boundary of the inner limiting membrane (ILM) [0.50 vs. 0.48 pixels], outer boundary of the retinal pigment epithelium (RPE) [0.48 vs. 0.44 pixels] and choroid-scleral interface (CSI) [4.42 vs. 4.00 pixels] shows the performance using synthetic data to be only marginally inferior. These findings highlight the potential use of GANs for data augmentation in future work with chorio-retinal OCT images.","PeriodicalId":6696,"journal":{"name":"2019 Digital Image Computing: Techniques and Applications (DICTA)","volume":"153 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":"91473654","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: 10

Insect-Inspired Small Moving Target Enhancement in Infrared Videos 红外视频中受昆虫启发的小移动目标增强

2019 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2019-12-01 DOI: 10.1109/DICTA47822.2019.8946002

M. Uzair, R. Brinkworth, A. Finn

{"title":"Insect-Inspired Small Moving Target Enhancement in Infrared Videos","authors":"M. Uzair, R. Brinkworth, A. Finn","doi":"10.1109/DICTA47822.2019.8946002","DOIUrl":"https://doi.org/10.1109/DICTA47822.2019.8946002","url":null,"abstract":"Thermal infrared imaging is an effective modality for developing robust methods of small target detection at large distances. However, low target contrast and high background clutter are two main challenges that limit the detection performance. We present bio-inspired spatio-temporal pre-processing of infrared video frames to deal with such challenges. The neurons in the early vision system of small flying insects have remarkable capability for noise filtering, contrast enhancement, signal compression and clutter suppression. These neurons were computationally modeled previously in two stages using a combination of linear and non-linear processing layers. The first stage models the adaptive temporal filtering mechanisms of insect photoreceptor cells. It improves the signal-to-noise-ratio, enhances target background discrimination and expands the possible range of signal variability. The second stage models the spatio-temporal adaptive filtering in the large monopolar cells that remove redundancy and increase target contrast. To show the performance gain achieved by such bio-inspired preprocessing, we perform small target detection experiments on real world high bit-depth infrared video sequences. Results show that the early biological vision based pre-processing significantly improves the performance of four standard infrared small moving target detection techniques. Specifically, the spatio-temporal preprocessing increase the detection rate (at 10−5 false alarm rate) of the best performing method by 100% and by up to 630% for the other methods. Our results are indicative of the strong potential of the bio-processing for allowing systems to detect smaller targets at longer distances in more cluttered environments.","PeriodicalId":6696,"journal":{"name":"2019 Digital Image Computing: Techniques and Applications (DICTA)","volume":"33 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":"87356475","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