2018 Digital Image Computing: Techniques and Applications (DICTA)最新文献

Accurate Shift Estimation under One-Parameter Geometric Distortion using the Brosc Filter 基于Brosc滤波器的单参数几何失真下的精确移位估计

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615835

P. Fletcher, Matthew R. Arnison, Eric W. Chong

{"title":"Accurate Shift Estimation under One-Parameter Geometric Distortion using the Brosc Filter","authors":"P. Fletcher, Matthew R. Arnison, Eric W. Chong","doi":"10.1109/DICTA.2018.8615835","DOIUrl":"https://doi.org/10.1109/DICTA.2018.8615835","url":null,"abstract":"Shift estimation is the task of estimating an unknown translation factor which best relates two relatively distorted representations of the same image data. Where distortion is large and also includes rotation and scaling, estimates of the global distortion can be obtained with good accuracy using RST-matching methods, but such algorithms are slow and complicated. Where geometric distortion is small, correlation-based methods can achieve millipixel accuracy. These methods begin to fail, however, when even quite small geometric distortions are present, such as rotation by 1° or 2°, or a scaling by as little as 5%. A new spatially-variant filter, the brosc filter (\"better rotation or scaling\"), can be used to preserve the accuracy of correlation-based shift estimation where the expected distortion can be modelled as a single parameter, for example, as a pure rotation, a pure scaling, or a pure scaling along a known axis. By applying the brosc filter before shift estimation, shift accuracy under geometric distortion is improved, and a variant of the brosc filter using complex arithmetic provides in addition an estimate of the single parameter representing the unknown distortion.","PeriodicalId":130057,"journal":{"name":"2018 Digital Image Computing: Techniques and Applications (DICTA)","volume":"33 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":"123180746","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

Descriptor-Driven Keypoint Detection 描述符驱动的关键点检测

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615841

A. Sluzek

引用次数: 0

Deep Learning Models for Facial Expression Recognition 面部表情识别的深度学习模型

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615843

Atul Sajjanhar, Zhaoqi Wu, Q. Wen

引用次数: 25

Early Experience of Depth Estimation on Intricate Objects using Generative Adversarial Networks 基于生成对抗网络的复杂目标深度估计的早期经验

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615783

Wai Y. K. San, Teng Zhang, Shaokang Chen, A. Wiliem, Dario Stefanelli, B. Lovell

引用次数: 0

A General Approach to Segmentation in CT Grayscale Images using Variable Neighborhood Search 基于变邻域搜索的CT灰度图像分割方法

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615823

T. Siriapisith, Worapan Kusakunniran, P. Haddawy

{"title":"A General Approach to Segmentation in CT Grayscale Images using Variable Neighborhood Search","authors":"T. Siriapisith, Worapan Kusakunniran, P. Haddawy","doi":"10.1109/DICTA.2018.8615823","DOIUrl":"https://doi.org/10.1109/DICTA.2018.8615823","url":null,"abstract":"Medical image segmentation is essential for several tasks including pre-treatment planning and tumor monitoring. Computed tomography (CT) is the most useful imaging modality for abdominal organs and tumors, with benefits of high imaging resolution and few motion artifacts. Unfortunately, CT images contain only limited information of intensity and gradient, which makes accurate segmentation a challenge. In this paper, we propose a 2D segmentation method that applies the concept of variable neighborhood search (VNS) by iteratively alternating search through intensity and gradient spaces. By alternating between the two search spaces, the technique can escape local minima that occur when segmenting in a single search space. The main techniques used in the proposed framework are graph-cut with probability density function (GCPDF) and graph-cut based active contour (GCBAC). The presented method is quantitatively evaluated on a public clinical dataset, which includes various sizes of liver tumor, kidney and spleen. The segmentation performance is evaluated using dice similarity coefficient (DSC), Jaccard similarity coefficient (JSC), and volume difference (VD). The presented method achieves the outstanding segmentation performance with a DSC of 84.48±5.84%, 76.93±8.24%, 91.70±2.68% and 89.27±5.21%, for large liver tumor, small liver tumor, kidney and spleen, respectively.","PeriodicalId":130057,"journal":{"name":"2018 Digital Image Computing: Techniques and Applications (DICTA)","volume":"299302 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":"116576789","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

Binarization of Color Character Strings in Scene Images using Deep Neural Network 基于深度神经网络的场景图像颜色字符串二值化

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615837

Wenjiao Bian, T. Wakahara, Tao Wu, He Tang, Jirui Lin

引用次数: 1

Colour Analysis of Strawberries on a Real Time Production Line 草莓实时生产线上的颜色分析

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615779

Gilbert Eaton, Andrew Busch, Rudi Bartels, Yongsheng Gao

引用次数: 1

RGB-D Fall Detection via Deep Residual Convolutional LSTM Networks 基于深度残差卷积LSTM网络的RGB-D跌落检测

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615759

A. Abobakr, M. Hossny, Hala Abdelkader, S. Nahavandi

{"title":"RGB-D Fall Detection via Deep Residual Convolutional LSTM Networks","authors":"A. Abobakr, M. Hossny, Hala Abdelkader, S. Nahavandi","doi":"10.1109/DICTA.2018.8615759","DOIUrl":"https://doi.org/10.1109/DICTA.2018.8615759","url":null,"abstract":"The development of smart healthcare environments has witnessed impressive advancements exploiting the recent technological capabilities. Since falls are considered a major health concern especially among older adults, low-cost fall detection systems have become an indispensable component in these environments. This paper proposes an integrable, privacy preserving and efficient fall detection system from depth images acquired using a Kinect RGB-D sensor. The proposed system uses an end-to-end deep learning architecture composed of convolutional and recurrent neural networks to detect fall events. The deep convolutional network (ConvNet) analyses the human body and extracts visual features from input sequence frames. Fall events are detected via modeling complex temporal dependencies between subsequent frame features using Long-Shot-Term-Memory (LSTM) recurrent neural networks. Both models are combined and jointly trained in an end-to-end ConvLSTM architecture. This allows the model to learn visual representations and complex temporal dynamics of fall motions simultaneously. The proposed method has been validated on the public URFD fall detection dataset and compared with different approaches, including accelerometer based methods. We achieved a near unity sensitivity and specificity rates in detecting fall events.","PeriodicalId":130057,"journal":{"name":"2018 Digital Image Computing: Techniques and Applications (DICTA)","volume":"43 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":"121980119","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: 25

Fine-Grained Categorization by Deep Part-Collaboration Convolution Net 基于深度局部协作卷积网络的细粒度分类

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615855

Qiyu Liao, H. Holewa, Min Xu, Dadong Wang

引用次数: 3

Hand Detection using Deformable Part Models on an Egocentric Perspective 基于自中心视角的可变形零件模型手部检测

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615781

Sergio R. Cruz, Antoni B. Chan

引用次数: 7