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arXiv 2017年

作者： Zhou, Zhiguo Zhou, Zhi-Jie Hao, Hongxia Li, Shulong Chen, Xi Zhang, You Folkert, Michael Wang, Jing Department of Radiation Oncology UT Southwestern Medical Center DallasTX United States High-Tech Institute of Xi'an Xi'an China Department of Information and Control Engineering Xi'an University of Technology Xi'an China School of Computer Science and Technology Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xi'an China School of Biomedical Engineering Southern Medical Unversity Guangzhou China Institute of Image Processing and Pattern Recognition Xi'an Jiaotong Unversity Xi'an China

Radiomics aims to extract and analyze large numbers of quantitative features from medical images and is highly promising in staging, diagnosing, and predicting outcomes of cancer treatments. Nevertheless, several challenges need to be addressed to construct an optimal radiomics predictive model. First, the predictive performance of the model may be reduced when features extracted from an individual imaging modality (e.g. PET, CT, MRI) are blindly combined into a single predictive model. Second, because many different types of classifiers are available to construct a predictive model, selecting an "optimal" classifier for a particular application is still challenging. In this work, we developed multi-modality and multi-classifier radiomics predictive models that address the aforementioned issues in currently available models. Specifically, a new reliable classifier fusion strategy was proposed to optimally combine output from different modalities and classifiers. In this strategy, modality-specific classifiers were first trained, and an analytic evidential reasoning (ER) rule was developed to fuse the output score from each modality to construct an optimal predictive model. One public datasets and two clinical case studies were performed to validate model performance. The experimental results indicated that the proposed ER rule based radiomics models outperformed the traditional models that rely on a single classifier or simply use combined features from different modalities. Copyright © 2017, The Authors. All rights reserved.

关键词： Diagnosis

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Face Alignment Across Large Poses: A 3D Solution

Face Alignment Across Large Poses: A 3D Solution

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IEEE Conference on computer Vision and pattern recognition

作者： Xiangyu Zhu Zhen Lei Xiaoming Liu Hailin Shi Stan Z. Li Center for Biometrics and Security Research & National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Department of Computer Science and Engineering Michigan State University

ISBN: (纸本)9781467388528

Face alignment, which fits a face model to an image and extracts the semantic meanings of facial pixels, has been an important topic in CV community. However, most algorithms are designed for faces in small to medium poses (below 45°), lacking the ability to align faces in large poses up to 90°. The challenges are three-fold: Firstly, the commonly used landmark-based face model assumes that all the landmarks are visible and is therefore not suitable for profile views. Secondly, the face appearance varies more dramatically across large poses, ranging from frontal view to profile view. Thirdly, labelling landmarks in large poses is extremely challenging since the invisible landmarks have to be guessed. In this paper, we propose a solution to the three problems in an new alignment framework, called 3D Dense Face Alignment (3DDFA), in which a dense 3D face model is fitted to the image via convolutional neutral network (CNN). We also propose a method to synthesize large-scale training samples in profile views to solve the third problem of data labelling. Experiments on the challenging AFLW database show that our approach achieves significant improvements over state-of-the-art methods.

关键词： face modeling Face Posing Face landmarks Imagery (Psychotherapy) images Alignment

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A Survey of Blue-Noise Sampling and Its Applications

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Journal of computer science & Technology 2015年第3期30卷 439-452页

作者：严冬明郭建伟王斌张晓鹏 Peter Wonk Visual Computing Center King Abdullah University of Science and Technology Thuwal 23955-6900 Saudi Arabia National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing 100190 China School of Software Tsinghua University Beijing 100084 China Department of Computer Science and Engineering Arizona State University Tempe AZ 85287 U.S.A.

In this paper, we survey recent approaches to blue-noise sampling and discuss their beneficial applications. We discuss the sampling algorithms that use points as sampling primitives and classify the sampling algorithms based on various aspects, e.g., the sampling domain and the type of algorithm. We demonstrate several well-known applications that can be improved by recent blue-noise sampling techniques, as well as some new applications such as dynamic sampling and blue-noise remeshing.

关键词： blue-noise sampling Poisson-disk sampling Lloyd relaxation rendering remeshing

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SHREC'15 track: 3D object retrieval with multimodal views 8

SHREC'15 track: 3D object retrieval with multimodal views

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8th Eurographics Workshop on 3D Object Retrieval, 3DOR 2015

作者： Gao, Yue Liu, Anan Nie, Weizhi Su, Yuting Dai, Qionghai Chen, Fuhai Chen, Yingying Cheng, Yanhua Dong, Shuilong Duan, Xingyue Fu, Jianlong Gao, Zan Guo, Haiyun Guo, Xin Huang, Kaiqi Ji, Rongrong Jiang, Yingfeng Li, Haisheng Lu, Hanqing Song, Jianming Sun, Jing Tan, Tieniu Wang, Jinqiao Yin, Huanpu Zhang, Chaoli Zhang, Guotai Zhang, Yan Zhao, Chaoyang Zhao, Xin Zhu, Guibo School of Medicine University of North Carolina Chapel Hill United States School of Electronic Information Engineering Tianjin University China Department of Automation Tsinghua University China School of Information Science and Engineering Xiamen University China Center for Research on Intelligent Perception and Computing Institute of Automation Chinese Academy of Sciences China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences China College of Computing and Digital Media DePaul University United States School of Computing Tianjin University of Technology China School of Computer Science and Information Engineering Beijing Technology and Business University China

ISBN: (纸本)9783905674781

This paper reports the results of the SHREC'15 track: 3D Object Retrieval with Multimodal Views, which goal is to evaluate the performance of retrieval algorithms when multimodal views are employed for 3D object representation. In this task, a collection of 505 objects is generated and both the color images and the depth images are provided for each object. 311 objects are selected as the queries and average retrieval performance is measured. The track attracted six participants and the submission of 26 runs, to two tasks. The evaluation results show a promising scenario about multimodal view-based 3D retrieval methods, and reveal interesting insights in dealing with multimodal data. © The Eurographics Association 2015.

关键词： Information retrieval

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Undo the codebook bias by linear transformation for visual applications 13

Undo the codebook bias by linear transformation for visual a...

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21st ACM International Conference on Multimedia, MM 2013

作者： Zhang, Chunjie Zhang, Yifan Wang, Shuhui Pang, Junbiao Liang, Chao Huang, Qingming Tian, Qi School of Computer and Control Engineering University of Chinese Academy of Sciences 100049 Beijing China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China Key Lab of Intell. Info. Process Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China College of Computer Science and Technology Beijing University of Technology 100124 Beijing China School of Computer National Engineering Research Center for Multimedia Software Wuhan University 430072 Wuhan China Department of Computer Sciences University of Texas at San Antonio San Antonio TX 78249 United States

ISBN: (纸本)9781450324045

The bag of visual words model (BoW) and its variants have demonstrate their effectiveness for visual applications and have been widely used by researchers. The BoW model first extracts local features and generates the corresponding codebook, the elements of a codebook are viewed as visual words. The local features within each image are then encoded to get the final histogram representation. However, the codebook is dataset dependent and has to be generated for each image dataset. This costs a lot of computational time and weakens the generalization power of the BoW model. To solve these problems, in this paper, we propose to undo the dataset bias by codebook linear transformation. To represent every points within the local feature space using Euclidean distance, the number of bases should be no less than the space dimensions. Hence, each codebook can be viewed as a linear transformation of these bases. In this way, we can transform the pre-learned codebooks for a new dataset. However, not all of the visual words are equally important for the new dataset, it would be more effective if we can make some selection using sparsity constraints and choose the most discriminative visual words for transformation. We propose an alternative optimization algorithm to jointly search for the optimal linear transformation matrixes and the encoding parameters. Image classification experimental results on several image datasets show the effectiveness of the proposed method. Copyright © 2013 ACM.

关键词： Linear transformations

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Contour-traversal based algorithm for insertion of virtual advertisements in videos

Contour-traversal based algorithm for insertion of virtual a...

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2012 International Conference on Communication, Information and Computing Technology, ICCICT 2012

作者： Hegde, Akshay Kumar, Deepa Anil Srinivasa, Gowri Department of Computer Science and Engineering PES Institute of Technology Bangalore South Campus India Department of Information Science and Engineering PES Institute of Technology Bangalore South Campus India Center for Pattern Recognition PES Institute of Technology Bangalore South Campus India

ISBN: (纸本)9781457720789

This paper presents a method for the automated insertion of virtual advertisements (logos, messages, etc.) into videos. The method consists of detecting regular polygons from the edge maps of frames in a video. This is followed by the selection of an optimal candidate slot into which the desired advertisement is inserted. Experimental results demonstrate that the contour-tracing based rectangle detection algorithm presented in this paper outperforms its precedents such as the Hough Transform and ant-based polygon detection for the purposes of identifying appropriate slots for introducing content in videos. © 2012 IEEE.

关键词： Hough transforms

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Spatial localization and color histograms based insertion of virtual advertisements in videos

Spatial localization and color histograms based insertion of...

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International Conference on Data science & engineering (ICDSE)

作者： Akshay Hegde Deepa Anil Kumar Gowri Srinivasa Department of Computer Science and Engineering Center for Pattern Recognition PES Institute of Technology Bangalore India Department of Information Science and Engineering Center for Pattern Recognition PES Institute of Technology Bangalore India

This paper presents a location-sensitive algorithm for the automated insertion of virtual advertisements in videos. Virtual advertisements are objects (text messages, pictures, an animation or video sequence) that are introduced noninvasively into a video. Typically, this is done manually. Most automated approaches involve insertion of virtual advertisements in hard-coded, specific locations of videos that could be intrusive. In this paper we propose an algorithm based on colour histogram processing that works with an inter-frame difference based classifier. Along with the spatial localization input (based on the type of video), this ensures a virtual advertisement is inserted in a manner that it is noticed and yet appears at a location that does not obfuscate a critical portion of the video. We demonstrate the efficacy of this method through experimental results.

关键词： Videos Histograms Image color analysis Shape Animation Classification algorithms Algorithm design and analysis

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Spatial localization and color histograms based insertion of virtual advertisements in videos

Spatial localization and color histograms based insertion of...

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2012 International Conference on Data science and engineering, ICDSE 2012

作者： Hegde, Akshay Kumar, Deepa Anil Srinivasa, Gowri Dept. of Computer Science and Engineering PES Institute of Technology Bangalore South Campus Bangalore India Dept. of Information Science and Engineering PES Institute of Technology Bangalore South Campus Bangalore India Center for Pattern Recognition PES Institute of Technology Bangalore South Campus Bangalore India

ISBN: (纸本)9781467321488

关键词： Location

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Contour-traversal based algorithm for insertion of virtual advertisements in videos

Contour-traversal based algorithm for insertion of virtual a...

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International Conference on Communication, Information & Computing Technology

作者： Akshay Hegde Deepa Anil Kumar Gowri Srinivasa Department of Computer Science and Engineering Center for Pattern Recognition PES Institute of Technology India Department of Information Science and Engineering PES Institute of Technology India Center for Pattern Recognition Department of Information Science and Engineering PES Institute of Technology India

关键词： Videos Image edge detection Shape Transforms computers Indexes Gray-scale

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Handwritten and Machine Printed Text Separation in Document Images Using the Bag of Visual Words Paradigm

Handwritten and Machine Printed Text Separation in Document ...

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International Workshop on Frontiers in Handwriting recognition

作者： Konstantinos Zagoris Ioannis Pratikakis Apostolos Antonacopoulos Basilis Gatos Nikos Papamarkos Pattern Recognition and Image Analysis (PRImA) Research Laboratory School of Computing Science and Engineering University of Salford Greater Manchester UK Department of Electrical and Computer Engineering Democritus University of Thrace Xanthi Greece Institute of Informatics and Telecommunications National Center for Scientific Research Demokritos Athens Greece

In a number of types of documents, ranging from forms to archive documents and books with annotations, machine printed and handwritten text may be present in the same document image, giving rise to significant issues within a digitisation and recognition pipeline. It is therefore necessary to separate the two types of text before applying different recognition methodologies to each. In this paper, a new approach is proposed which strives towards identifying and separating handwritten from machine printed text using the Bag of Visual Words paradigm (BoVW). Initially, blocks of interest are detected in the document image. For each block, a descriptor is calculated based on the BoVW. The final characterization of the blocks as Handwritten, Machine Printed or Noise is made by a Support Vector Machine classifier. The promising performance of the proposed approach is shown by using a consistent evaluation methodology which couples meaningful measures along with a new dataset.

关键词： Visualization Noise Image segmentation Hidden Markov models Gabor filters Handwriting recognition Text recognition

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