2020 Digital Image Computing: Techniques and Applications (DICTA)最新文献_第4页

An improved method for pylon extraction and vegetation encroachment analysis in high voltage transmission lines using LiDAR data 基于激光雷达数据的高压输电线路塔架提取与植被侵占分析改进方法

2020 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2020-11-29 DOI: 10.1109/DICTA51227.2020.9363391

Nosheen Munir, M. Awrangjeb, Bela Stantic

{"title":"An improved method for pylon extraction and vegetation encroachment analysis in high voltage transmission lines using LiDAR data","authors":"Nosheen Munir, M. Awrangjeb, Bela Stantic","doi":"10.1109/DICTA51227.2020.9363391","DOIUrl":"https://doi.org/10.1109/DICTA51227.2020.9363391","url":null,"abstract":"The maintenance of high-voltage power lines rights-of-way due to vegetation intrusions is important for electric power distribution companies for safe and secure delivery of electricity. However, the monitoring becomes more challenging if power line corridor (PLC) exists in complex environment such as mountainous terrains or forests. To overcome these challenges, this paper aims to provide an automated method for extraction of individual pylons and monitoring of vegetation near the PLC in hilly terrain. The proposed method starts off by dividing the large dataset into small manageable datasets. A voxel grid is formed on each dataset to separate power lines from pylons and vegetation. The power line points are converted into a binary image to get the individual spans. These span points are used to find nearby vegetation and pylon points and individual pylons and vegetation are further separated using a statistical analysis. Finally, the height and location of extracted vegetation with reference to power lines are estimated and separated into danger and clearance zones. The experiment on two large Australian datasets shows that the proposed method provides high completeness and correctness of 96.5% and 99% for pylons, respectively. Moreover, the growing vegetation beneath and around the PLC that can harm the power lines is identified.","PeriodicalId":348164,"journal":{"name":"2020 Digital Image Computing: Techniques and Applications (DICTA)","volume":"71 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":"124847170","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

Evaluation of U-Net CNN Approaches for Human Neck MRI Segmentation U-Net CNN方法对人体颈部MRI分割的评价

2020 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2020-11-29 DOI: 10.1109/DICTA51227.2020.9363385

A. Suman, Yash Khemchandani, Md. Asikuzzaman, A. Webb, D. Perriman, M. Tahtali, M. Pickering

{"title":"Evaluation of U-Net CNN Approaches for Human Neck MRI Segmentation","authors":"A. Suman, Yash Khemchandani, Md. Asikuzzaman, A. Webb, D. Perriman, M. Tahtali, M. Pickering","doi":"10.1109/DICTA51227.2020.9363385","DOIUrl":"https://doi.org/10.1109/DICTA51227.2020.9363385","url":null,"abstract":"The segmentation of neck muscles is useful for the diagnoses and planning of medical interventions for neck pain-related conditions such as whiplash and cervical dystonia. Neck muscles are tightly grouped, have similar appearance to each other and display large anatomical variability between subjects. They also exhibit low contrast with background organs in magnetic resonance (MR) images. These characteristics make the segmentation of neck muscles a challenging task. Due to the significant success of the U-Net architecture for deep learning-based segmentation, numerous versions of this approach have emerged for the task of medical image segmentation. This paper presents an evaluation of 10 U-Net CNN approaches, 6 direct (U-Net, CRF-Unet, A-Unet, MFP-Unet, R2Unet and U-Net++) and 4 modified (R2A-Unet, R2A-Unet++, PMS-Unet and MS-Unet). The modifications are inspired by recent multi-scale and multi-stream techniques for deep learning algorithms. T1 weighted axial MR images of the neck, at the distal end of the C3 vertebrae, from 45 subjects with real-time data augmentation were used in our evaluation of neck muscle segmentation approaches. The analysis of our numerical results indicates that the R2Unet architecture achieves the best accuracy.","PeriodicalId":348164,"journal":{"name":"2020 Digital Image Computing: Techniques and Applications (DICTA)","volume":"1 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":"128756122","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

SL3D - Single Look 3D Object Detection based on RGB-D Images SL3D -基于RGB-D图像的单视3D目标检测

2020 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2020-11-29 DOI: 10.1109/DICTA51227.2020.9363404

G. Erabati, Helder Araújo

{"title":"SL3D - Single Look 3D Object Detection based on RGB-D Images","authors":"G. Erabati, Helder Araújo","doi":"10.1109/DICTA51227.2020.9363404","DOIUrl":"https://doi.org/10.1109/DICTA51227.2020.9363404","url":null,"abstract":"We present SL3D, Single Look 3D object detection approach to detect the 3D objects from the RGB-D image pair. The approach is a proposal free, single-stage 3D object detection method from RGB-D images by leveraging multi-scale feature fusion of RGB and depth feature maps, and multi-layer predictions. The method takes pair of RGB and depth images as an input and outputs predicted 3D bounding boxes. The neural network SL3D, comprises of two modules: multi-scale feature fusion and multi-layer prediction. The multi-scale feature fusion module fuses the multi-scale features from RGB and depth feature maps, which are later used by the multi-layer prediction module for 3D object detection. Each location of prediction layer is attached with a set of predefined 3D prior boxes to account for varying shapes of 3D objects. The output of the network regresses the predicted 3D bounding boxes as an offset to the set of 3D prior boxes and duplicate 3D bounding boxes are removed by applying 3D non-maximum suppression. The network is trained end-to-end on publicly available SUN RGB-D dataset. The SL3D approach with ResNeXt50 achieves 31.77 mAP on SUN RGB-D test dataset with an inference speed of approximately 4 fps, and with MobileNetV2, it achieves approximately 15 fps with a reduction of around 2 mAP. The quantitative results show that the proposed method achieves competitive performance to state-of-the-art methods on SUN RGB-D dataset with near real-time inference speed.","PeriodicalId":348164,"journal":{"name":"2020 Digital Image Computing: Techniques and Applications (DICTA)","volume":"31 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":"125629192","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

CNN to Capsule Network Transformation CNN向胶囊网络转型

2020 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2020-11-29 DOI: 10.1109/DICTA51227.2020.9363395

Takumi Sato, K. Hotta

引用次数: 1

Dual image and mask synthesis with GANs for semantic segmentation in optical coherence tomography 基于gan的双图像和掩模合成用于光学相干断层成像的语义分割

2020 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2020-11-29 DOI: 10.1109/DICTA51227.2020.9363402

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

{"title":"Dual image and mask synthesis with GANs for semantic segmentation in optical coherence tomography","authors":"J. Kugelman, D. Alonso-Caneiro, Scott A. Read, Stephen J. Vincent, F. Chen, M. Collins","doi":"10.1109/DICTA51227.2020.9363402","DOIUrl":"https://doi.org/10.1109/DICTA51227.2020.9363402","url":null,"abstract":"In recent years, deep learning-based OCT segmentation methods have addressed many of the limitations of traditional segmentation approaches and are capable of performing rapid, consistent and accurate segmentation of the chorio-retinal layers. However, robust deep learning methods require a sufficiently large and diverse dataset for training, which is not always feasible in many biomedical applications. Generative adversarial networks (GANs) have demonstrated the capability of producing realistic and diverse high-resolution images for a range of modalities and datasets, including for data augmentation, a powerful application of GAN methods. In this study we propose the use of a StyleGAN inspired approach to generate chorio-retinal optical coherence tomography (OCT) images with a high degree of realism and diversity. We utilize the method to synthesize image and segmentation mask pairs that can be used to train a deep learning semantic segmentation method for subsequent boundary delineation of three chorioretinal layer boundaries. By pursuing a dual output solution rather than a mask-to-image translation solution, we remove an unnecessary constraint on the generated images and enable the synthesis of new unseen area mask labels. The results are encouraging with near comparable performance observed when training using purely synthetic data, compared to the real data. Moreover, training using a combination of real and synthetic data results in zero measurable performance loss, further demonstrating the reliability of this technique and feasibility for data augmentation in future work.","PeriodicalId":348164,"journal":{"name":"2020 Digital Image Computing: Techniques and Applications (DICTA)","volume":"52 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":"127432971","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

Base-Package Recommendation Framework Based on Consumer Behaviours in IPTV Platform 基于IPTV平台消费者行为的基包推荐框架

2020 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2020-11-29 DOI: 10.1109/DICTA51227.2020.9363400

Kuruparan Shanmugalingam, Ruwinda Ranganayake, Chanaka Gunawardhana, Rajitha Navarathna

引用次数: 0

Recurrent Motion Neural Network for Low Resolution Drone Detection 低分辨率无人机检测的递归运动神经网络

2020 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2020-11-29 DOI: 10.1109/DICTA51227.2020.9363377

Hamish Pratt, B. Evans, T. Rowntree, I. Reid, S. Wiederman

引用次数: 0

Visualizing and Understanding Inherent Image Features in CNN-based Glaucoma Detection 基于cnn的青光眼检测中固有图像特征的可视化和理解

2020 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2020-11-29 DOI: 10.1109/DICTA51227.2020.9363369

Dhaval Vaghjiani, Sajib Saha, Yann Connan, Shaun Frost, Y. Kanagasingam

引用次数: 5

Attentive Inception Module based Convolutional Neural Network for Image Enhancement 基于细心盗梦模块的卷积神经网络图像增强

2020 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2020-11-29 DOI: 10.1109/DICTA51227.2020.9363375

Purbaditya Bhattacharya, U. Zölzer

引用次数: 1

Network-based structure flow estimation 基于网络的结构流估计

2020 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2020-11-29 DOI: 10.1109/DICTA51227.2020.9363398

Shu Liu, Nick Barnes, R. Mahony, Haolei Ye

引用次数: 0