2018 Digital Image Computing: Techniques and Applications (DICTA)最新文献_第9页

Ensemble Classification Technique for Water Detection in Satellite Images 卫星图像中水体检测的集成分类技术

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615870

R. Jony, A. Woodley, A. Raj, Dimitri Perrin

引用次数: 6

Robust CNN-based Gait Verification and Identification using Skeleton Gait Energy Image 基于骨骼步态能量图像的鲁棒cnn步态验证与识别

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615802

Lingxiang Yao, Worapan Kusakunniran, Qiang Wu, Jian Zhang, Zhenmin Tang

引用次数: 27

Face Recognition with Multi-channel Local Mesh High-order Pattern Descriptor and Convolutional Neural Network 基于多通道局部网格高阶模式描述子和卷积神经网络的人脸识别

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615831

M. Asif, Yongsheng Gao, J. Zhou

{"title":"Face Recognition with Multi-channel Local Mesh High-order Pattern Descriptor and Convolutional Neural Network","authors":"M. Asif, Yongsheng Gao, J. Zhou","doi":"10.1109/DICTA.2018.8615831","DOIUrl":"https://doi.org/10.1109/DICTA.2018.8615831","url":null,"abstract":"In this paper, we propose a novel Local Mesh High-order Pattern Descriptor (LMHPD) for face recognition. This description is constructed in a high-order derivative space and is integrated with a Convolutional Neural Network (CNN) architecture. Based on the information collected at a local neighborhood of reference pixel with diverse radiuses and mesh angles, a vectorized feature representation of the reference pixel is generated to provide micro-patterns. They are then converted to multi-channels to use in conjunction with the CNN. The CNN adopted in the proposed architecture is generic and very compact with a small number of convolutional layers. However, LMHPD is derived in such a way that it can work with most of the available CNN architectures. For keeping the computational cost and time complexity at the minimum, we propose a lighter approach of high-order texture descriptor with CNN architecture that can effectively extract discriminative face features. Extensive experiments on Extended Yale B and CMU-PIE datasets show that our method consistently outperforms several alternative descriptors for face recognition under various circumstances.","PeriodicalId":130057,"journal":{"name":"2018 Digital Image Computing: Techniques and Applications (DICTA)","volume":"11 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":"121328189","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

Complexity and Entropy of Knee Kinematics in a Joint Reposition Test: Effect of Strapping and Kinesiology Taping 膝关节复位试验中膝关节运动学的复杂性和熵:绑带和运动学胶带的影响

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615777

L. Donnan, M. Paul, L. Crowley, Kilian Felesimo, H. Jelinek

{"title":"Complexity and Entropy of Knee Kinematics in a Joint Reposition Test: Effect of Strapping and Kinesiology Taping","authors":"L. Donnan, M. Paul, L. Crowley, Kilian Felesimo, H. Jelinek","doi":"10.1109/DICTA.2018.8615777","DOIUrl":"https://doi.org/10.1109/DICTA.2018.8615777","url":null,"abstract":"Proprioception plays an important role in neuromuscular control and stability. Taping the knee or ankle with strapping tape (ST) is a common means to increase stability and limit unwanted joint motion. Kinesiology tape (KT) is an alternative tape has been proposed to enhance proprioceptive information from the skin muscles and joints. Comparisons of muscle responses associated with different taping methods has not been investigated using a joint reposition test (JRT). The current study investigated lower limb muscle activity during a blind folded JRT in a group of college students with no known injuries. Thirty nine healthy college students between 18–35 years of age were recruited using convenience sampling. Electromyographical (EMG) data was recorded from lower limb muscles and 3D video recordings tracked knee joint angle accuracy for the JRT. Participants were blindfolded and guided to 40 degrees of knee flexion by the experimenter, and were asked to repeat this joint position five times unaided. Higuchi fractal dimension and sample entropy were used to determine the nonlinear dynamic properties of the muscle responses. Statistical analysis was with repeated measures ANOVA and Tukey post hoc test. Significance was set at p<0.05. The results indicated that ST led to higher complexity and randomness of muscle activity, compared to KT. These results correlated with the absolute error associated with the JRT, where KT was significantly lower with a lower standard deviation. Higher complexity and randomness may indicate compromised muscle activity due to loss of proprioceptive information and decreased sensorimotor effectiveness.","PeriodicalId":130057,"journal":{"name":"2018 Digital Image Computing: Techniques and Applications (DICTA)","volume":"14 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":"115566684","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

Dynamic Saliency Model Inspired by Middle Temporal Visual Area: A Spatio-Temporal Perspective 中时视觉区激发的动态显著性模型:一个时空视角

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615806

Hassan Mahmood, S. Islam, S. O. Gilani, Y. Ayaz

引用次数: 0

Deep Learning on Brain Cortical Thickness Data for Disease Classification 脑皮质厚度数据的深度学习用于疾病分类

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615775

Medhani Menikdiwela, Chuong V. Nguyen, Marnie E. Shaw

{"title":"Deep Learning on Brain Cortical Thickness Data for Disease Classification","authors":"Medhani Menikdiwela, Chuong V. Nguyen, Marnie E. Shaw","doi":"10.1109/DICTA.2018.8615775","DOIUrl":"https://doi.org/10.1109/DICTA.2018.8615775","url":null,"abstract":"Deep learning has been applied to learn and classify brain disease using volumetric MRI scans with an accuracy approaching or even exceeding that of a human expert. This is typically done by applying convolutional neural networks to slices of a 3D brain image volume. Each slice of the brain volume, however, represents only a small cross-sectional area of the cortical layer. On the other hand, convolutional neural networks are less well developed for 3D volumes. Therefore we sought to apply deep networks to the 2D cortical surface, for the purpose of classifying Alzheimer's disease (AD). AD is known to affect the thickness and geometry of the cortical surface of the brain. Although the cortical surface has a complex geometry, here we present a novel data processing method to feed the information of an entire cortical surface into existing deep networks for more accurate early disease detection. A brain 3D MRI volume is registered and its cortical surface is flattened to a 2D plane. The flattened distributions of the thickness, curvature and surface area are combined into an RBG image which can be readily fed to existing deep networks. In this paper, the ADNI dataset of brain MRI scans are used and flattened cortical images are applied to different deep networks including ResNet and Inception. Two pre-clinical stages of AD are considered; stable mild cognitive impairment (MCIs) and converting mild cognitive impairment (MCIc). Experiments show that using flattened cortical images consistently leads to higher accuracy compared to using brain slices with the same network architecture. Specifically, the highest accuracy of 81% is achieved by Inception with flattened cortical images, as compared to 68% by the same network on brain slices and 75.9% accuracy by the best method in the literature which also used a deep network on brain slices. Our results indicate that flattened cortical images can be used to learn and classify AD with high accuracy.","PeriodicalId":130057,"journal":{"name":"2018 Digital Image Computing: Techniques and Applications (DICTA)","volume":"108 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":"128503283","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: 16

Massively Parallel Implementation of a Fast Resource Efficient White Light Interferometry Algorithm 一种快速资源高效白光干涉测量算法的大规模并行实现

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615828

Tobias Scholz, M. Rosenberger, G. Notni

引用次数: 1

Classification of White Blood Cells using Bispectral Invariant Features of Nuclei Shape 利用细胞核形状的双谱不变性特征对白细胞进行分类

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615762

Khamael Al-Dulaimi, V. Chandran, Jasmine Banks, Inmaculada Tomeo-Reyes, Kien Nguyen

{"title":"Classification of White Blood Cells using Bispectral Invariant Features of Nuclei Shape","authors":"Khamael Al-Dulaimi, V. Chandran, Jasmine Banks, Inmaculada Tomeo-Reyes, Kien Nguyen","doi":"10.1109/DICTA.2018.8615762","DOIUrl":"https://doi.org/10.1109/DICTA.2018.8615762","url":null,"abstract":"Classification of white blood cells from microscope images is a challenging task, especially in the choice of feature representation, considering intra-class variations arising from non-uniform illumination, stage of maturity, scale, rotation and shifting. In this paper, we propose a new feature extraction scheme relying on bispectral invariant features which are robust to these challenges. Bispectral invariant features are extracted from the shape of segmented white blood cell nuclei. Segmentation of white blood cell nuclei is achieved using a level set algorithm via geometric active contours. Binary support vector machines and a classification tree are used for classifying multiple classes of the cells. Performance of the proposed method is evaluated on a combined dataset of 10 classes with 460 white blood cell images collected from 3 datasets and using 5-fold cross validation. It achieves an average classification accuracy of 96.13% and outperforms other popular representations including local binary pattern, histogram of oriented gradients, local directional pattern and speeded up robust features with the same classifier over the same data. The classification accuracy of the proposed method is also compared and benchmarked with the other existing techniques for classification white blood cells into 10 classes over the same datasets and the results show that the proposed method is superior over other approaches.","PeriodicalId":130057,"journal":{"name":"2018 Digital Image Computing: Techniques and Applications (DICTA)","volume":"17 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":"114387637","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: 26

A Scale-Free and Parameter-Free Image Edge Strength Measure 一种无尺度、无参数的图像边缘强度测量方法

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615813

Guy Smith, P. Jackway

引用次数: 0

Active Contours Based on An Anisotropic Diffusion 基于各向异性扩散的活动轮廓

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615767

Shafiullah Soomro, K. Choi

{"title":"Active Contours Based on An Anisotropic Diffusion","authors":"Shafiullah Soomro, K. Choi","doi":"10.1109/DICTA.2018.8615767","DOIUrl":"https://doi.org/10.1109/DICTA.2018.8615767","url":null,"abstract":"Image Segmentation is one of the pivotal procedure in the field of imaging and its objective is to catch required boundaries inside an image. In this paper, we propose a novel active contour method based on anisotropic diffusion. Global regionbased active contour methods rely on global intensity information across the regions. However, these methods fail to produce desired segmentation results when an image has some background variations or noise. In this regard, we adapt Perona and Malik smoothing technique as enhancement step. This technique provides interregional smoothing, sharpens the boundaries and blurs the background of an image. Our main role is the formulation of a new SPF (signed pressure force) function, which uses global intensity information across the regions. Minimizing an energy function using partial differential framework produce results with semantically meaningful boundaries instead of capturing impassive regions. Finally, we use Gaussian kernel to eliminate problem of reinitialization in level set function. We use images taken from different modalities to validate the outcome of the proposed method. In the result section, we have evaluated that, the proposed method achieves good results qualitatively and quantitatively with high accuracy compared to other state-of-the-art models.","PeriodicalId":130057,"journal":{"name":"2018 Digital Image Computing: Techniques and Applications (DICTA)","volume":"1 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":"132544227","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