2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA)最新文献_第9页

Pollution Detection on Rail Surface for Adhesion Evaluation Using Multispectral Images 基于多光谱图像的轨道表面污染检测及其附着力评价

2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2017-11-01 DOI: 10.1109/DICTA.2017.8227477

C. Nicodeme, B. Stanciulescu

引用次数: 1

Irish Sign Language Recognition Using Principal Component Analysis and Convolutional Neural Networks 使用主成分分析和卷积神经网络的爱尔兰手语识别

2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2017-11-01 DOI: 10.1109/DICTA.2017.8227451

Marlon Oliveira, Houssem Chatbri, S. Little, Ylva Ferstl, N. O’Connor, Alistair Sutherland

引用次数: 15

Tomographic Reconstruction Using Global Statistical Priors 基于全局统计先验的层析重建

2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2017-11-01 DOI: 10.1109/DICTA.2017.8227496

Preeti Gopal, Ritwick Chaudhry, S. Chandran, I. Svalbe, Ajit V. Rajwade

引用次数: 2

Learning Deep Spatial Lung Features by 3D Convolutional Neural Network for Early Cancer Detection 基于三维卷积神经网络的肺深部空间特征学习用于早期癌症检测

2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2017-11-01 DOI: 10.1109/DICTA.2017.8227454

Taolin Jin, Hui Cui, Shan Zeng, Xiuying Wang

引用次数: 26

Image Retrieval with Attribute-Associated Auxiliary References 基于属性关联辅助引用的图像检索

2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2017-11-01 DOI: 10.1109/DICTA.2017.8227444

Lin Nie, Keze Wang, Wenxiong Kang, Yuefang Gao

引用次数: 1

Automatic Recognition of Human Emotions Induced by Visual Contents of Digital Images Based on Color Histogram 基于颜色直方图的数字图像视觉内容情感自动识别

2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2017-11-01 DOI: 10.1109/DICTA.2017.8227410

Seyed Abdolreza Mohseni, H. Wu, J. Thom

{"title":"Automatic Recognition of Human Emotions Induced by Visual Contents of Digital Images Based on Color Histogram","authors":"Seyed Abdolreza Mohseni, H. Wu, J. Thom","doi":"10.1109/DICTA.2017.8227410","DOIUrl":"https://doi.org/10.1109/DICTA.2017.8227410","url":null,"abstract":"Using color histograms in automatic emotion recognition systems faces different issues. One of the important challenges is to determine the appropriate number of bins in the color histogram to achieve the highest recognition performance possible with minimal computations. This research focuses on emotion recognition induced by visual contents of images, or REVC for short, using ARTphoto dataset. Twenty-two different classifiers are used with color histograms in both RGB (red, green, blue) and HSV (hue, saturation, value) color spaces across different numbers of bins, and overall performance of each bin size is compared with that of other bin sizes. The research findings show that the performance of REVC system does not improve in terms of overall sensitivity rate, when the number of bins in color histogram is increased. Moreover, this paper identifies the advantage of using HSV color space over RGB in using color histogram for REVC systems. Furthermore, findings recognize the optimum number of bins in both RGB and HSV color spaces, and ANOVA (analysis of variance) is used to analyze experimental data, which identifies the optimum color histogram bin size used for HSV color space is significantly better than that used for RGB color space in REVC systems.","PeriodicalId":194175,"journal":{"name":"2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","volume":"304 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121281537","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

Bilinear CNN Models for Food Recognition 用于食物识别的双线性CNN模型

2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2017-11-01 DOI: 10.1109/DICTA.2017.8227411

Hesen Chen, Jingyu Wang, Q. Qi, Yujian Li, Haifeng Sun

引用次数: 12

Constrained Stochastic Gradient Descent: The Good Practice 约束随机梯度下降:良好实践

2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2017-11-01 DOI: 10.1109/DICTA.2017.8227420

S. Roy, Mehrtash Harandi

引用次数: 6

Multi-Object Model-Free Tracking with Joint Appearance and Motion Inference 基于关节外观和运动推理的多目标无模型跟踪

2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2017-11-01 DOI: 10.1109/DICTA.2017.8227468

Chongyu Liu, Rui Yao, S. H. Rezatofighi, I. Reid, Javen Qinfeng Shi

{"title":"Multi-Object Model-Free Tracking with Joint Appearance and Motion Inference","authors":"Chongyu Liu, Rui Yao, S. H. Rezatofighi, I. Reid, Javen Qinfeng Shi","doi":"10.1109/DICTA.2017.8227468","DOIUrl":"https://doi.org/10.1109/DICTA.2017.8227468","url":null,"abstract":"Multi-object model-free tracking is challenging because the tracker is not aware of the objects' type (not allowed to use object detectors), and needs to distinguish one object from background as well as other similar objects. Most existing methods keep updating their appearance model individually for each target, and their performance is hampered by sudden appearance change and/or occlusion. We propose to use both appearance model and motion model to overcome this issue. We introduce an indicator variable to predict sudden appearance change and occlusion. When they happen, our model stops updating the appearance model to avoid parameter update based on background or incorrect object, and rely more on motion model to track. Moreover, we consider the correlation among all targets, and seek the joint optimal locations for all target simultaneously. We formulate the problem of finding the most likely locations jointly as a graphical model inference problem, and learn the joint parameters for both appearance model and motion model in an online fashion in the framework of LaRank. Experiment results show that our method outperforms the state-of-the-art.","PeriodicalId":194175,"journal":{"name":"2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129309853","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

A Method to Create Stable Lighting and Remove Specular Reflections for Vision Systems 一种为视觉系统创造稳定照明和消除镜面反射的方法

2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2017-11-01 DOI: 10.1109/DICTA.2017.8227392

Gilbert Eaton, Andrew Busch, Rudi Bartels, Yongsheng Gao

引用次数: 1