2018 Digital Image Computing: Techniques and Applications (DICTA)最新文献_第5页

Mapping of Rice Varieties with Sentinel-2 Data via Deep CNN Learning in Spectral and Time Domains 基于谱域和时域深度CNN学习的Sentinel-2数据水稻品种映射

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615872

Yiqing Guo, X. Jia, D. Paull

{"title":"Mapping of Rice Varieties with Sentinel-2 Data via Deep CNN Learning in Spectral and Time Domains","authors":"Yiqing Guo, X. Jia, D. Paull","doi":"10.1109/DICTA.2018.8615872","DOIUrl":"https://doi.org/10.1109/DICTA.2018.8615872","url":null,"abstract":"Generating rice variety distribution maps with remote sensing image time series provides meaningful information for intelligent management of rice farms and precise budgeting of irrigation water. However, as different rice varieties share highly similar spectral/temporal patterns, distinguishing one variety from another is highly challenging. In this study, a deep convolutional neural network (deep CNN) is constructed in both spectral and time domains. The purpose is to learn the fine features of each rice variety in terms of its spectral reflectance characteristics and growing phenology, which is a new attempt aiming for agriculture intelligence. An experiment was conducted at a major rice planting area in southwest New South Wales, Australia, during the 2016–17 rice growing season. Based on a ground reference map of rice variety distribution, more than one million labelled samples were collected. Five rice varieties currently grown in the study area are investigated and they are Reiziq, Sherpa, Topaz, YRM 70, and Langi. A time series of multitemporal remote sensing images recorded by the Multispectral Instrument (MSI) on-board the Sentinel-2A satellite was used as inputs. These images covered the entire rice growing season from November 2016 to May 2017. Experimental results showed that a good overall accuracy of 92.87% was achieved with the proposed approach, outperforming a standard support vector machine classifier that produced an accuracy of 57.49%. The Sherpa variety showed the highest producer's accuracy (98.46%), while the highest user's accuracy was observed for the Reiziq variety (97.93%). The results obtained with the proposed deep CNN learning provide the prospect of applying remote sensing image time series for rice variety mapping in an operational context in future.","PeriodicalId":130057,"journal":{"name":"2018 Digital Image Computing: Techniques and Applications (DICTA)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127398143","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: 5

Animal Call Recognition with Acoustic Indices: Little Spotted Kiwi as a Case Study 动物叫声识别声学指数:小斑点猕猴桃为例研究

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615857

Hongxiao Gan, M. Towsey, Yuefeng Li, Jinglan Zhang, P. Roe

{"title":"Animal Call Recognition with Acoustic Indices: Little Spotted Kiwi as a Case Study","authors":"Hongxiao Gan, M. Towsey, Yuefeng Li, Jinglan Zhang, P. Roe","doi":"10.1109/DICTA.2018.8615857","DOIUrl":"https://doi.org/10.1109/DICTA.2018.8615857","url":null,"abstract":"Long-duration recordings of the natural environment are very useful in monitoring of animal diversity. After accumulating weeks or even months of recordings, ecologists need an efficient tool to recognize species in those recordings. Automated species recognizers are developed to interpret field-collected recordings and quickly identify species. However, the repetitive work of designing and selecting features for different species is becoming a serious problem for ecologists. This situation creates a demand for generic recognizers that perform well on multiple animal calls. Meanwhile, acoustic indices are proposed to summarize the structure and distribution of acoustic energy in natural environment recordings. They are designed to assess the acoustic activity of animal habitats and do not have discrimination against any species. That characteristic makes them natural generic features for recognizers. In this study, we explore the potential of acoustic indices being generic features and build a kiwi call recognizer with them as a case study. We proposed a kiwi call recognizer built with a Multilayer Perceptron (MLP) classifier and acoustic index features. Experimental results on 13 hours of kiwi call recordings show that our recognizer performs well, in terms of precision, recall and F1 measure. This study shows that acoustic indices have the potential of being generic features that can discriminate multiple animal calls.","PeriodicalId":130057,"journal":{"name":"2018 Digital Image Computing: Techniques and Applications (DICTA)","volume":"559 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127676912","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

Using Edge Position Difference and Pixel Correlation for Aligning Stereo-Camera Generated 3D Scans 利用边缘位置差和像素相关对齐立体相机生成的三维扫描

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615836

Deepak Rajamohan, M. Pickering, M. Garratt

引用次数: 1

Bay Lobsters Moulting Stage Analysis Based on High-Order Texture Descriptor 基于高阶纹理描述符的海湾龙虾换壳阶段分析

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615832

M. Asif, Yongsheng Gao, Jun Zhou

引用次数: 1

Kernel Support Vector Machines and Convolutional Neural Networks 核支持向量机与卷积神经网络

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615840

Shihao Jiang, R. Hartley, Basura Fernando

{"title":"Kernel Support Vector Machines and Convolutional Neural Networks","authors":"Shihao Jiang, R. Hartley, Basura Fernando","doi":"10.1109/DICTA.2018.8615840","DOIUrl":"https://doi.org/10.1109/DICTA.2018.8615840","url":null,"abstract":"Convolutional Neural Networks (CNN) have achieved great success in various computer vision tasks due to their strong ability in feature extraction. The trend of development of CNN architectures is to increase their depth so as to increase their feature extraction ability. Kernel Support Vector Machines (SVM), on the other hand, are known to give optimal separating surfaces by their ability to automatically select support vectors and perform classification in higher dimensional spaces. We investigate the idea of combining the two such that best of both worlds can be achieved and a more compact model can perform as well as deeper CNNs. In the past, attempts have been made to use CNNs to extract features from images and then classify with a kernel SVM, but this process was performed in two separate steps. In this paper, we propose one single model where a CNN and a kernel SVM are integrated together and can be trained end-to-end. In particular, we propose a fully-differentiable Radial Basis Function (RBF) layer, where it can be seamless adapted to a CNN environment and forms a better classifier compared to the normal linear classifier. Due to end-to-end training, our approach allows the initial layers of the CNN to extract features more adapted to the kernel SVM classifier. Our experiments demonstrate that the hybrid CNN-kSVM model gives superior results to a plain CNN model, and also performs better than the method where feature extraction and classification are performed in separate stages, by a CNN and a kernel SVM respectively.","PeriodicalId":130057,"journal":{"name":"2018 Digital Image Computing: Techniques and Applications (DICTA)","volume":"126 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123033327","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

Virtual View Quality Enhancement using Side View Temporal Modelling Information for Free Viewpoint Video 使用侧面视图时间建模信息增强免费视点视频的虚拟视图质量

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615827

D. M. Rahaman, M. Paul, N. J. Shoumy

{"title":"Virtual View Quality Enhancement using Side View Temporal Modelling Information for Free Viewpoint Video","authors":"D. M. Rahaman, M. Paul, N. J. Shoumy","doi":"10.1109/DICTA.2018.8615827","DOIUrl":"https://doi.org/10.1109/DICTA.2018.8615827","url":null,"abstract":"Virtual viewpoint video needs to be synthesised from adjacent reference viewpoints to provide immersive perceptual 3D viewing experience of a scene. View synthesised techniques suffer poor rendering quality due to holes created by occlusion in the warping process. Currently, spatial and temporal correlation of texture images and depth maps are exploited to improve the quality of the final synthesised view. Due to the low spatial correlation at the edge between foreground and background pixels, spatial correlation e.g. inpainting and inverse mapping (IM) techniques cannot fill holes effectively. Conversely, a temporal correlation among already synthesised frames through learning by Gaussian mixture modelling (GMM) fill missing pixels in occluded areas efficiently. In this process, there are no frames for GMM learning when the user switches view instantly. To address the above issues, in the proposed view synthesis technique, we apply GMM on the adjacent reference viewpoint texture images and depth maps to generate a most common frame in a scene (McFIS). Then, texture McFIS is warped into the target viewpoint by using depth McFIS and both warped McFISes are merged. Then, we utilize the number of GMM models to refine pixel intensities of the synthesised view by using a weighting factor between the pixel intensities of the merged McFIS and the warped images. This technique provides a better pixel correspondence and improves 0.58∼0.70dB PSNR compared to the IM technique.","PeriodicalId":130057,"journal":{"name":"2018 Digital Image Computing: Techniques and Applications (DICTA)","volume":"41 163 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131605778","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

Strategies for Merging Hyperspectral Data of Different Spectral and Spatial Resoultion 不同光谱和空间分辨率高光谱数据合并策略

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615875

R. Illmann, M. Rosenberger, G. Notni

引用次数: 3

A Single Hierarchical Network for Face, Action Unit and Emotion Detection 一种用于人脸、动作单元和情感检测的单一层次网络

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615852

Shreyank Jyoti, Garima Sharma, Abhinav Dhall

引用次数: 3

Detecting Splicing and Copy-Move Attacks in Color Images 检测彩色图像中的拼接和复制移动攻击

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615874

Mohammad Manzurul Islam, G. Karmakar, J. Kamruzzaman, Manzur Murshed, G. Kahandawa, N. Parvin

{"title":"Detecting Splicing and Copy-Move Attacks in Color Images","authors":"Mohammad Manzurul Islam, G. Karmakar, J. Kamruzzaman, Manzur Murshed, G. Kahandawa, N. Parvin","doi":"10.1109/DICTA.2018.8615874","DOIUrl":"https://doi.org/10.1109/DICTA.2018.8615874","url":null,"abstract":"Image sensors are generating limitless digital images every day. Image forgery like splicing and copy-move are very common type of attacks that are easy to execute using sophisticated photo editing tools. As a result, digital forensics has attracted much attention to identify such tampering on digital images. In this paper, a passive (blind) image tampering identification method based on Discrete Cosine Transformation (DCT) and Local Binary Pattern (LBP) has been proposed. First, the chroma components of an image is divided into fixed sized non-overlapping blocks and 2D block DCT is applied to identify the changes due to forgery in local frequency distribution of the image. Then a texture descriptor, LBP is applied on the magnitude component of the 2D-DCT array to enhance the artifacts introduced by the tampering operation. The resulting LBP image is again divided into non-overlapping blocks. Finally, summations of corresponding inter-cell values of all the LBP blocks are computed and arranged as a feature vector. These features are fed into a Support Vector Machine (SVM) with Radial Basis Function (RBF) as kernel to distinguish forged images from authentic ones. The proposed method has been experimented extensively on three publicly available well-known image splicing and copy-move detection benchmark datasets of color images. Results demonstrate the superiority of the proposed method over recently proposed state-of-the-art approaches in terms of well accepted performance metrics such as accuracy, area under ROC curve and others.","PeriodicalId":130057,"journal":{"name":"2018 Digital Image Computing: Techniques and Applications (DICTA)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116824616","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: 7

Long-Term Recurrent Predictive Model for Intent Prediction of Pedestrians via Inverse Reinforcement Learning 基于逆强化学习的行人意图预测长期循环模型

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615854

Khaled Saleh, M. Hossny, S. Nahavandi

引用次数: 20