2018 Digital Image Computing: Techniques and Applications (DICTA)最新文献_第7页

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

Combining Deep and Handcrafted Image Features for Vehicle Classification in Drone Imagery 结合深度和手工图像特征在无人机图像中的车辆分类

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615853

Xuesong Le, Yufei Wang, Jun Jo

引用次数: 3

Classifier-Free Extraction of Power Line Wires from Point Cloud Data 从点云数据中提取无分类器的电力线

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615869

M. Awrangjeb, Yongsheng Gao, Guojun Lu

引用次数: 3

Spectral Super-resolution for RGB Images using Class-based BP Neural Networks 基于类的BP神经网络的光谱超分辨率RGB图像

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615862

Xiaolin Han, Jing Yu, Jing-Hao Xue, Weidong Sun

{"title":"Spectral Super-resolution for RGB Images using Class-based BP Neural Networks","authors":"Xiaolin Han, Jing Yu, Jing-Hao Xue, Weidong Sun","doi":"10.1109/DICTA.2018.8615862","DOIUrl":"https://doi.org/10.1109/DICTA.2018.8615862","url":null,"abstract":"Hyperspectral images are of high spectral resolution and have been widely used in many applications, but the imaging process to achieve high spectral resolution is at the expense of spatial resolution. This paper aims to construct a high-spatial-resolution hyperspectral (HHS) image from a high-spatial-resolution RGB image, by proposing a novel class-based spectral super-resolution method. With the help of a set of RGB and HHS image-pairs, our proposed method learns nonlinear spectral mappings between RGB and HHS image-pairs using class-based back propagation neural networks (BPNNs). In the training stage, unsupervised clustering is used to divide an RGB image into several classes according to spectral correlation, and the spectrum-pairs from the classified RGB images and the corresponding HHS images are used to train the BPNNs, to establish the nonlinear spectral mapping for each class. In the spectral super-resolution stage, a supervised classification is used to classify the given RGB image into the classes determined during the training stage, and the final HHS image is reconstructed from the classified given RGB image using the trained BPNNs. Comparisons on three standard datasets, ICVL, CAVE and NUS, demonstrate that, our proposed method achieves a better spectral super-resolution quality than related state-of-the-art methods.","PeriodicalId":130057,"journal":{"name":"2018 Digital Image Computing: Techniques and Applications (DICTA)","volume":"146 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":"128435845","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

Systematic Analysis of Direct Sparse Odometry 直接稀疏里程计的系统分析

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615807

F. Particke, A. Kalisz, Christian Hofmann, M. Hiller, Henrik Bey, J. Thielecke

{"title":"Systematic Analysis of Direct Sparse Odometry","authors":"F. Particke, A. Kalisz, Christian Hofmann, M. Hiller, Henrik Bey, J. Thielecke","doi":"10.1109/DICTA.2018.8615807","DOIUrl":"https://doi.org/10.1109/DICTA.2018.8615807","url":null,"abstract":"In the field of robotics and autonomous driving, the camera as a sensor gets more and more important, as the camera is cheap and robust against environmental influences. One challenging task is the localization of the robot on an unknown map. This leads to the so-called Simultaneous Localization and Mapping (SLAM) problem. For the Visual SLAM problem, a plethora of algorithms was proposed in the last years, but the algorithms were rarely evaluated regarding the robustness of the approaches. This contribution motivates the systematic analysis of Visual SLAMs in simulation by using heterogeneous environments in Blender. For this purpose, three different environments are used for evaluation ranging from very low detailed to high detailed worlds. In this contribution, the Direct Sparse Odometry (DSO) is evaluated as an exemplary Visual SLAM. It is shown that the DSO is very sensitive to rotations of the camera. In addition, it is presented that if the scene does not provide sufficient clues about the depth, an estimation of the trajectory is not possible. The results are complemented by real-world experiments.","PeriodicalId":130057,"journal":{"name":"2018 Digital Image Computing: Techniques and Applications (DICTA)","volume":"110 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":"134390411","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

Combination of Supervised Learning and Unsupervised Learning Based on Object Association for Land Cover Classification 基于目标关联的有监督学习与无监督学习相结合的土地覆盖分类

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615871

Na Li, Arnaud Martin, R. Estival

引用次数: 6

Bi-Modal Content Based Image Retrieval using Multi-class Cycle-GAN 基于多类循环gan的双模态图像检索

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615838

Girraj Pahariya

引用次数: 1