2020 Digital Image Computing: Techniques and Applications (DICTA)最新文献

Density-Based Vehicle Counting with Unsupervised Scale Selection 基于密度的无监督尺度选择车辆计数

2020 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2020-11-29 DOI: 10.1109/DICTA51227.2020.9363401

P. Dobes, Jakub Špaňhel, Vojtech Bartl, Roman Juránek, A. Herout

引用次数: 1

Dual-Stage Domain Adaptive Mitosis Detection for Histopathology Images 组织病理学图像的双阶段域自适应有丝分裂检测

2020 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2020-11-29 DOI: 10.1109/DICTA51227.2020.9363411

Veena Dodballapur, Yang Song, Heng Huang, Mei Chen, Wojciech Chrzanowski, Weidong (Tom) Cai

引用次数: 1

Using Environmental Context to Synthesis Missing Pixels 使用环境上下文合成缺失像素

2020 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2020-11-29 DOI: 10.1109/DICTA51227.2020.9363419

Thaer F. Ali, A. Woodley

{"title":"Using Environmental Context to Synthesis Missing Pixels","authors":"Thaer F. Ali, A. Woodley","doi":"10.1109/DICTA51227.2020.9363419","DOIUrl":"https://doi.org/10.1109/DICTA51227.2020.9363419","url":null,"abstract":"Satellites have proven to be a technology that can help in a variety of environmental and human development contexts. However, at times some pixels in the satellite images are not captured. These uncaptured pixels are called missing pixels. Having these missing pixels means that important data for research and satellite imagery-based applications is lost. Therefore, people have developed pixel synthesis methods. This paper presents a new pixel synthesis method called the Iterative Self-Organizing Data Analysis Techniques Algorithm - Integration of Geostatistical and Temporal Missing Pixels' Properties (ISODATA-IGTMPP). The method is built upon the Integration of Geostatistical and Temporal Missing Pixels' Properties (IG TMPP) method and adds a seminal clustering technique called the Iterative Self-Organizing Data Analysis Techniques Algorithm (ISODATA). The clustering technique allows a new way of predicting the missing pixel from their environmental class with benefit of the spatial and temporal properties. Here, the ISODATA-IGTMPP method was tested on the Spatial-Temporal Change in the Environment Context (STCEC) dataset and was compared with results of four missing pixel predicting methods. The method shows the best performing results and preforms very well across different environment types.","PeriodicalId":348164,"journal":{"name":"2020 Digital Image Computing: Techniques and Applications (DICTA)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116324707","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

Pseudo Supervised Solar Panel Mapping based on Deep Convolutional Networks with Label Correction Strategy in Aerial Images 航拍图像中基于深度卷积网络标签校正策略的伪监督太阳能电池板映射

2020 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2020-11-29 DOI: 10.1109/DICTA51227.2020.9363379

Jue Zhang, X. Jia, Jiankun Hu

{"title":"Pseudo Supervised Solar Panel Mapping based on Deep Convolutional Networks with Label Correction Strategy in Aerial Images","authors":"Jue Zhang, X. Jia, Jiankun Hu","doi":"10.1109/DICTA51227.2020.9363379","DOIUrl":"https://doi.org/10.1109/DICTA51227.2020.9363379","url":null,"abstract":"Solar panel mapping has gained a rising interest in renewable energy field with the aid of remote sensing imagery. Significant previous work is based on fully supervised learning with classical classifiers or convolutional neural networks (CNNs), which often require manual annotations of pixel-wise ground-truth to provide accurate supervision. Weakly supervised methods can accept image-wise annotations which can help reduce the cost for pixel-level labelling. Inevitable performance gap, however, exists between weakly and fully supervised methods in mapping accuracy. To address this problem, we propose a pseudo supervised deep convolutional network with label correction strategy (PS-CNNLC) for solar panels mapping. It combines the benefits of both weak and strong supervision to provide accurate solar panel extraction. First, a convolutional neural network is trained with positive and negative samples with image-level labels. It is then used to automatically identify more positive samples from randomly selected unlabeled images. The feature maps of the positive samples are further processed by gradient-weighted class activation mapping to generate initial mapping results, which are taken as initial pseudo labels as they are generally coarse and incomplete. A progressive label correction strategy is designed to refine the initial pseudo labels and train an end-to-end target mapping network iteratively, thereby improving the model reliability. Comprehensive evaluations and ablation study conducted validate the superiority of the proposed PS-CNNLC.","PeriodicalId":348164,"journal":{"name":"2020 Digital Image Computing: Techniques and Applications (DICTA)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126541107","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

Vis-CRF: A Simplified Filtering Model for Vision 视觉- crf:一种简化的视觉过滤模型

2020 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2020-11-29 DOI: 10.1109/DICTA51227.2020.9363403

Nasim Nematzadeh, D. Powers, T. Lewis

引用次数: 0

M2-Net: A Multi-scale Multi-level Feature Enhanced Network for Object Detection in Optical Remote Sensing Images M2-Net:一种用于光学遥感图像目标检测的多尺度多层次特征增强网络

2020 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2020-11-29 DOI: 10.1109/DICTA51227.2020.9363420

X. Ye, Fengchao Xiong, Jianfeng Lu, Haifeng Zhao, Jun Zhou

{"title":"M2-Net: A Multi-scale Multi-level Feature Enhanced Network for Object Detection in Optical Remote Sensing Images","authors":"X. Ye, Fengchao Xiong, Jianfeng Lu, Haifeng Zhao, Jun Zhou","doi":"10.1109/DICTA51227.2020.9363420","DOIUrl":"https://doi.org/10.1109/DICTA51227.2020.9363420","url":null,"abstract":"Object detection in remote sensing images is a challenging task due to diversified orientation, complex background, dense distribution and scale variation of objects. In this paper, we tackle this problem by proposing a novel multi-scale multi-level feature enhanced network ($M$2-Net) that integrates a Feature Map Enhancement (FME) module and a Feature Fusion Block (FFB) into Rotational RetinaNet. The FME module aims to enhance the weak features by factorizing the convolutional operation into two similar branches instead of one single branch, which helps to broaden receptive field with less parameters. This module is embedded into different layers in the backbone network to capture multi-scale semantics and location information for detection. The FFB module is used to shorten the information propagation path between low-level high-resolution features in shallow layers and high-level semantic features in deep layers, facilitating more effective feature fusion and object detection especially those with small sizes. Experimental results on three benchmark datasets show that our method not only outperforms many one-stage detectors but also achieves competitive accuracy with lower time cost than two-stage detectors.","PeriodicalId":348164,"journal":{"name":"2020 Digital Image Computing: Techniques and Applications (DICTA)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114461377","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 Assessment of Open Street Maps Database Quality using Aerial Imagery 基于航空影像的开放街道地图数据库质量自动评估

2020 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2020-11-29 DOI: 10.1109/DICTA51227.2020.9363412

Boris Repasky, Timothy Payne, A. Dick

引用次数: 0

HCI for Elderly, Measuring Visual Complexity of Webpages Based on Machine Learning 面向老年人的人机交互，基于机器学习的网页视觉复杂性测量

2020 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2020-11-29 DOI: 10.1109/DICTA51227.2020.9363381

Zahra Sadeghi, E. Homayounvala, M. Borhani

{"title":"HCI for Elderly, Measuring Visual Complexity of Webpages Based on Machine Learning","authors":"Zahra Sadeghi, E. Homayounvala, M. Borhani","doi":"10.1109/DICTA51227.2020.9363381","DOIUrl":"https://doi.org/10.1109/DICTA51227.2020.9363381","url":null,"abstract":"The increasing number of elderly persons, aged 65 and over, highlights the problem of improving their experience with computers and the web considering their preferences and needs. Elderlies' skills like cognitive, haptic, visual, and motor skills are reduced by age. The visual complexity of web pages has a major influence on the quality of user experience of elderly users according to their reduced abilities. Therefore, it is quite beneficial if the visual complexity of web pages could be measured and reduced in applications and websites which are designed for them. In this way a personalized less complex version of the website could be provided for older users. In this article, a new approach for measuring the visual complexity is proposed by using both Human-Computer Interaction (HCI) and machine learning methods. Six features are considered for complexity measurements. Experimental results demonstrated that the trained proposed machine learning approach increases the accuracy of classification of applications and websites based on their visual complexity up to 82% which is more than its competitors. Besides, a feature selection algorithm indicates that features such as clutter and equilibrium were selected to have the most influence on the classification of webpages based on their visual complexity.","PeriodicalId":348164,"journal":{"name":"2020 Digital Image Computing: Techniques and Applications (DICTA)","volume":"126 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124005656","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

UNET-Based Multi-Task Architecture for Brain Lesion Segmentation 基于unet的脑损伤分割多任务结构

2020 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2020-11-29 DOI: 10.1109/DICTA51227.2020.9363397

Ava Assadi Abolvardi, Len Hamey, K. Ho-Shon

引用次数: 1

Automated Pneumoconiosis Detection on Chest X-Rays Using Cascaded Learning with Real and Synthetic Radiographs 使用级联学习与真实和合成射线片的胸部x射线自动尘肺病检测

2020 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2020-11-29 DOI: 10.1109/DICTA51227.2020.9363416

Dadong Wang, Y. Arzhaeva, Liton Devnath, Maoying Qiao, Saeed K. Amirgholipour, Qiyu Liao, R. McBean, J. Hillhouse, S. Luo, David Meredith, K. Newbigin, Deborah Yates

{"title":"Automated Pneumoconiosis Detection on Chest X-Rays Using Cascaded Learning with Real and Synthetic Radiographs","authors":"Dadong Wang, Y. Arzhaeva, Liton Devnath, Maoying Qiao, Saeed K. Amirgholipour, Qiyu Liao, R. McBean, J. Hillhouse, S. Luo, David Meredith, K. Newbigin, Deborah Yates","doi":"10.1109/DICTA51227.2020.9363416","DOIUrl":"https://doi.org/10.1109/DICTA51227.2020.9363416","url":null,"abstract":"Pneumoconiosis is an incurable respiratory disease caused by long-term inhalation of respirable dust. Due to small pneumoconiosis incidence and restrictions on sharing of patient data, the number of available pneumoconiosis X-rays is insufficient, which introduces significant challenges for training deep learning models. In this paper, we use both real and synthetic pneumoconiosis radiographs to train a cascaded machine learning framework for the automated detection of pneumoconiosis, including a machine learning based pixel classifier for lung field segmentation, and Cycle-Consistent Adversarial Networks (CycleGAN) for generating abundant lung field images for training, and a Convolutional Neural Network (CNN) based image classier. Experiments are conducted to compare the classification results from several state-of-the-art machine learning models and ours. Our proposed model outperforms the others and achieves an overall classification accuracy of 90.24%, a specificity of 88.46% and an excellent sensitivity of 93.33% for detecting pneumoconiosis.","PeriodicalId":348164,"journal":{"name":"2020 Digital Image Computing: Techniques and Applications (DICTA)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123433839","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