2019 Digital Image Computing: Techniques and Applications (DICTA)最新文献_第2页

Multi-Pooling Attention Learning for Melanoma Recognition 黑色素瘤识别的多池注意学习

2019 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2019-12-01 DOI: 10.1109/DICTA47822.2019.8945868

Ruolin Liang, Qiuxia Wu, Xiaowei Yang

{"title":"Multi-Pooling Attention Learning for Melanoma Recognition","authors":"Ruolin Liang, Qiuxia Wu, Xiaowei Yang","doi":"10.1109/DICTA47822.2019.8945868","DOIUrl":"https://doi.org/10.1109/DICTA47822.2019.8945868","url":null,"abstract":"Melanoma is a type of skin cancer with high mortality rate. Early diagnosis of malignant melanoma usually relies on the judgement of dermatologists. Recently, more and more research paid attention to classify skin lesion automatically by dermoscopy images, which is able to assist clinicians to diagnose quickly and accurately. However, there still remain some challenges on extraction of discriminative feature due to the inter-class similarity and intra-class variation. In this paper, we propose a novel multi-pooling attention learning for skin lesions classification, which consists of multiple types of pooling operations, attention mechanism and feature fusion. The multi-pooling layer with flexible pooling patterns is designed to capture more representative features. The attention mechanism has the ability to help our model focus on the lesion regions. Meanwhile, in order to deal with the issues of inter-class similarity and intra-class variation of skin lesions, we adopt a joint loss function to optimize the final results. Our proposed approach has been validated on ISIC2017 dataset that provided by IEEE International Symposium on Biomedical Imaging (ISBI) 2017 challenge for Skin Lesion Analysis Towards Melanoma Detection. The results show that our proposed network achieves superior performance over six top-ranking approaches in ISBI 2017 challenge leaderboard and five other methods.","PeriodicalId":6696,"journal":{"name":"2019 Digital Image Computing: Techniques and Applications (DICTA)","volume":"120 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":"87789222","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

Robust Image Watermarking Framework Powered by Convolutional Encoder-Decoder Network 基于卷积编解码器网络的鲁棒图像水印框架

2019 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2019-12-01 DOI: 10.1109/DICTA47822.2019.8945866

Thien Huynh-The, Cam-Hao Hua, Nguyen Anh Tu, Dong-Seong Kim

{"title":"Robust Image Watermarking Framework Powered by Convolutional Encoder-Decoder Network","authors":"Thien Huynh-The, Cam-Hao Hua, Nguyen Anh Tu, Dong-Seong Kim","doi":"10.1109/DICTA47822.2019.8945866","DOIUrl":"https://doi.org/10.1109/DICTA47822.2019.8945866","url":null,"abstract":"The spread of illegal replication of digital image has seriously affected the protection of intellectual property rights. Nowadays, although watermarking is the most popular technology for copyright protection and ownership authentication, most of the current works cannot encounter numerous intentional attacks of watermark destruction. In this research, we introduce a novel framework of blind image watermarking that is able to learn attacking patterns effectively based on a deep convolutional encoder-decoder network. For details, a binary watermark image is hidden into selective wavelet blocks by the mean of an optimal encoding rule, wherein the quality image degradation is minimized over the Mean Square Error metric for a significant image imperceptibility enhancement. Then, the embedding maps, defined as the wavelet coefficient difference values, of various attacks simulated as digital image transformations of the watermarked image are revealed for training the deep learning-based watermark extraction model. Accordingly, the watermark information hidden in an attacked image can be precisely recovered from its embedding map by the trained model. From experimental results, the proposed watermark framework achieves a good performance trade-off between image imperceptibility and watermark robustness through an adjustable embedding strength. In addition, our approach is strongly defeats several state-of-the-art methods in terms of watermark robustness against many critical attacks.","PeriodicalId":6696,"journal":{"name":"2019 Digital Image Computing: Techniques and Applications (DICTA)","volume":"179 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":"73286673","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

Blind Motion Deblurring for Satellite Image using Convolutional Neural Network 基于卷积神经网络的卫星图像盲运动去模糊

2019 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2019-12-01 DOI: 10.1109/DICTA47822.2019.8945977

Hyun-ho Kim, D. Seo, J. Jung, DongHwan Cha, Donghan Lee

{"title":"Blind Motion Deblurring for Satellite Image using Convolutional Neural Network","authors":"Hyun-ho Kim, D. Seo, J. Jung, DongHwan Cha, Donghan Lee","doi":"10.1109/DICTA47822.2019.8945977","DOIUrl":"https://doi.org/10.1109/DICTA47822.2019.8945977","url":null,"abstract":"KOMPSAT-3A was launched in March 2015, and has been performing normal operations. However, there is an inaccurate line-rate control in KOMPSAT-3A that causes motion blur. This induces an along-track modulation transfer function (MTF) value that is smaller than the across-track MTF value. The motion blur degrades image quality and tends to deteriorate the performance of various computer vision algorithms such as object detection, segmentation, feature detection, and matching. Thus, in order to enhance the visual quality of blurry images, a motion deblurring algorithm is necessary. However, there are few studies about solving motion blur of satellite images. Most studies have been focused on the motion blur of general images taken by smart phones, action cameras, and DSLRs. To solve the satellite image problem, we propose a model for motion deblurring of satellite images and generate training data using the proposed model and KOMPSAT-3A data. In addition, we trained a residual and dense block-based convolutional neural network (CNN). To verify the residual and dense block-based CNN, we utilized simulated blurred images and actual blurry KOMPSAT-3A images. As a result, we confirmed that the proposed method reliably removes more motion blur than do conventional CNN-based motion deblur methods. Additionally, our method made the along-track and across-track MTF values similar. In addition, overall the MTF values increased. The results show that effective learning is possible for use in reducing motion deblurring of satellite images effectively.","PeriodicalId":6696,"journal":{"name":"2019 Digital Image Computing: Techniques and Applications (DICTA)","volume":"os-55 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":"87094194","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

Mine-Like Object Sensing in Sonar Imagery with a Compact Deep Learning Architecture for Scarce Data 基于紧凑型深度学习架构的声呐图像类地雷目标感知

2019 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2019-12-01 DOI: 10.1109/DICTA47822.2019.8945982

S. L. Phung, Thi Nhat Anh Nguyen, Hoang Thanh Le, P. Chapple, Christian Ritz, A. Bouzerdoum, Le Chung Tran

引用次数: 10

Tree Log Identity Matching using Convolutional Correlation Networks 使用卷积相关网络的树日志恒等式匹配

2019 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2019-12-01 DOI: 10.1109/DICTA47822.2019.8945865

Mikko Vihlman, Jakke Kulovesi, A. Visala

{"title":"Tree Log Identity Matching using Convolutional Correlation Networks","authors":"Mikko Vihlman, Jakke Kulovesi, A. Visala","doi":"10.1109/DICTA47822.2019.8945865","DOIUrl":"https://doi.org/10.1109/DICTA47822.2019.8945865","url":null,"abstract":"Log identification is an important task in silviculture and forestry. It involves matching tree logs with each other and telling which of the known individuals a given specimen is. Forest harvesters can image the logs and assess their quality while cutting trees in the forest. Identification allows each log to be traced back to the location it was grown in and efficiently choosing logs of specific quality in the sawmill. In this paper, a deep two-stream convolutional neural network is used to measure the likelihood that a pair of images represents the same part of a log. The similarity between the images is assessed based on the cross-correlation of the convolutional feature maps at one or more levels of the network. The performance of the network is evaluated with two large datasets, containing either spruce or pine logs. The best architecture identifies correctly 99% of the test logs in the spruce dataset and 97% of the test logs in the pine dataset. The results show that the proposed model performs very well in relatively good conditions. The analysis forms a basis for future attempts to utilize deep networks for log identification in challenging real-world forestry applications.","PeriodicalId":6696,"journal":{"name":"2019 Digital Image Computing: Techniques and Applications (DICTA)","volume":"25 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":"77322567","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

Rain Streak Removal from Video Sequence using Spatiotemporal Appearance 利用时空外观从视频序列中去除雨纹

2019 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2019-12-01 DOI: 10.1109/DICTA47822.2019.8946080

M. R. Islam, M. Paul

{"title":"Rain Streak Removal from Video Sequence using Spatiotemporal Appearance","authors":"M. R. Islam, M. Paul","doi":"10.1109/DICTA47822.2019.8946080","DOIUrl":"https://doi.org/10.1109/DICTA47822.2019.8946080","url":null,"abstract":"Capturing images in the challenging atmosphere for example during rain and snow critically degrades the quality of the images. These external phenomena create low contrast, blur and thereby reduce the visibility of the images. Many computer vision applications like visual traffic surveillance, intelligent vehicles, and entertainments are affected by reduced visibility. Rain streaks removal in a video (RSRV) has significant importance in the outdoor vision systems like surveillance video and has recently been studied comprehensively. In the last few years, many approaches have been made towards solving this problem including matrix decomposition, convolutional neural network, streak orientation modelling, Gaussian mixture modelling (GMM), etc. In this paper, we propose a concise model for RSRV based on the spatiotemporal appearance (STA) of rain streak in a video to exploit the rain streak appearance property. Here we apply Gaussian mixture modelling (GMM) to separate the background and foreground. Then, we use STA to separate the rain streak from moving foreground. The experimental results establish that the proposed method outperforms the state-of-the-art methods significantly for both real and synthetic rains.","PeriodicalId":6696,"journal":{"name":"2019 Digital Image Computing: Techniques and Applications (DICTA)","volume":"19 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":"86042092","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

Faster R-CNN Based Deep Learning for Seagrass Detection from Underwater Digital Images 基于R-CNN的更快的水下数字图像海草检测深度学习

2019 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2019-12-01 DOI: 10.1109/DICTA47822.2019.8946048

M. Moniruzzaman, S. Islam, P. Lavery, Bennamoun

引用次数: 23

Automated Building Footprint and 3D Building Model Generation from Lidar Point Cloud Data 基于激光雷达点云数据的自动建筑足迹和3D建筑模型生成

2019 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2019-12-01 DOI: 10.1109/DICTA47822.2019.8946008

F. T. Kurdi, M. Awrangjeb, Alan Wee-Chung Liew

{"title":"Automated Building Footprint and 3D Building Model Generation from Lidar Point Cloud Data","authors":"F. T. Kurdi, M. Awrangjeb, Alan Wee-Chung Liew","doi":"10.1109/DICTA47822.2019.8946008","DOIUrl":"https://doi.org/10.1109/DICTA47822.2019.8946008","url":null,"abstract":"Although much effort has been spent in developing a stable algorithm for 3D building modelling from Lidar data, this topic still attracts a lot of attention in the literature. A key task of this problem is the automatic building roof segmentation. Due to the great diversity of building typology, and the noisiness and heterogeneity of point cloud data, the building roof segmentation result needs to be verified/rectified with some geometric constrains before it is used to generate the 3D building models. Otherwise, the generated building model may suffer from undesirable deformations. This paper suggests the generation of 3D building model from Lidar data in two steps. The first step is the automatic 2D building modelling and the second step is the automatic conversion of a 2D building model into 3D model. This approach allows the 2D building model to be refined before starting the 3D building model generation. Furthermore, this approach allows getting the 2D and 3D building models simultaneously. The first step of the proposed algorithm is the generation of the 2D building model. Then after enhancing and fitting the roof planes, the roof plane boundaries are converted into 3D by analysing the relationships between neighbouring planes. This is followed by the adjustment of the 3D roof vertices. Experiment indicated that the proposed algorithm is accurate and robust in generating 3D building models from Lidar data.","PeriodicalId":6696,"journal":{"name":"2019 Digital Image Computing: Techniques and Applications (DICTA)","volume":"102 8 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":"91324146","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

Abundance-Guided Superpixels and Recurrent Neural Network for Hyperspectral Image Classification 基于丰度引导的超像素和递归神经网络的高光谱图像分类

2019 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2019-12-01 DOI: 10.1109/DICTA47822.2019.8946060

F. Alam, J. Zhou, Alan Wee-Chung Liew

引用次数: 1

Scalable Video Classification using Bag of Visual Words on Spark 可扩展的视频分类使用袋的视觉词在Spark上

2019 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2019-12-01 DOI: 10.1109/DICTA47822.2019.8945874

Nguyen Anh Tu, Thien Huynh-The, Young-Koo Lee

{"title":"Scalable Video Classification using Bag of Visual Words on Spark","authors":"Nguyen Anh Tu, Thien Huynh-The, Young-Koo Lee","doi":"10.1109/DICTA47822.2019.8945874","DOIUrl":"https://doi.org/10.1109/DICTA47822.2019.8945874","url":null,"abstract":"Video classification is an important and active field of computer vision research due to its variety of applications (e.g. action recognition), ranging from intelligent surveillance to human-computer interaction. Recently, the rapid growth of camera devices and multimedia sharing platforms has created a tremendous amount of video data. Accordingly, there is the growing demand of the parallel and distributed video processing for dealing with the data-intensive and computing-intensive tasks of the recognition system. These tasks are widely recognized to be time-consuming when running over large-scale video data. In this paper, we propose a distributed framework built on the top of the emerging big data technology Spark which is well-designed for the in-memory parallel computing system. Particularly, based on the state-of-the-art bag of visual words model (BoV) for video classification, we provide a comprehensive study of key steps in BoV (i.e. feature extraction, codebook generation, and feature encoding), and propose their fast and distributed algorithms. In experiments, our results further demonstrate the efficiency of proposed algorithms as well as the scalability of our framework on a public dataset UCF101 by greatly speeding up the process of video classification.","PeriodicalId":6696,"journal":{"name":"2019 Digital Image Computing: Techniques and Applications (DICTA)","volume":"104 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":"80822946","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