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IEEE Conference on computer vision and pattern recognition

作者： Guangliang Chen Mauro Maggioni Mathematics Duke University

ISBN: (纸本)9781457703942

Modeling data by multiple low-dimensional planes is an important problem in many applications such as computer vision and pattern recognition. In the most general setting where only coordinates of the data are given, the problem asks to determine the optimal model parameters (i.e., number of planes and their dimensions), estimate the model planes, and cluster the data accordingly. Though many algorithms have been proposed, most of them need to assume prior knowledge of the model parameters and thus address only the last two components of the problem. In this paper we propose an efficient algorithm based on multiscale SVD analysis and spectral methods to tackle the problem in full generality. We also demonstrate its state-of-the-art performance on both synthetic and real data.

关键词： plane model parameters Spectrum Analysis spectral methods efficient algorithm Prior Learning images computer vision TWO-COMPONENT pattern recognition observational data Platanus hybrida Planing SYNTHETICS dimensions

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RITK: The range imaging toolkit - A framework for 3-D range image stream processing

RITK: The range imaging toolkit - A framework for 3-D range ...

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16th International Workshop on vision, Modeling and Visualization, VMV 2011

作者： Wasza, J. Bauer, S. Haase, S. Schmid, M. Reichert, S. Hornegger, J. Pattern Recognition Lab. Dept. of Computer Science Germany Hardware/Software Co-Design Dept. of Computer Science Germany Friedrich-Alexander-Universität Erlangen-Nürnberg Germany Softgate GmbH Erlangen Germany

ISBN: (纸本)9783905673852

The recent introduction of low-cost devices for real-time acquisition of dense 3-D range imaging (RI) streams has attracted a great deal of attention. However, to date, there exists no open source framework that is explicitly dedicated to real-time processing of RI streams. In this paper, we present the Range Imaging Toolkit (RITK). The goal is to provide a powerful yet intuitive software platform that facilitates the development of range image stream applications. RITK puts emphasis on real-time processing of range image streams and proposes the use of a dedicated pipeline mechanism. Furthermore, we introduce a powerful and convenient interface for range image processing on the graphics processing unit (GPU). Being designed thoroughly and in a generic manner, the toolkit is able to cope with the broad diversity of data streams provided by available RI devices and can easily be extended by custom range imaging sensors or processing modules. RITK is an open source project and will be made publicly available at http://***/ritk. © The Eurographics Association 2011.

关键词： Graphics processing unit

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Image classification by non-negative sparse coding, low-rank and sparse decomposition

Image classification by non-negative sparse coding, low-rank...

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作者： Zhang, Chunjie Liu, Jing Tian, Qi Xu, Changsheng Lu, Hanqing Ma, Songde National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences P.O.Box 2728 Beijing China University of Texas at San Antonio One UTSA Circle San Antonio TX 78249 United States

ISBN: (纸本)9781457703942

We propose an image classification framework by leveraging the non-negative sparse coding, low-rank and sparse matrix decomposition techniques (LR-ScSPM). First, we propose a new non-negative sparse coding along with max pooling and spatial pyramid matching method (Sc+ SPM) to extract local features' information in order to represent images, where non-negative sparse coding is used to encode local features. Max pooling along with spatial pyramid matching (SPM) is then utilized to get the feature vectors to represent images. Second, motivated by the observation that images of the same class often contain correlated (or common) items and specific (or noisy) items, we propose to leverage the low-rank and sparse matrix recovery technique to decompose the feature vectors of images per class into a low-rank matrix and a sparse error matrix. To incorporate the common and specific attributes into the image representation, we still adopt the idea of sparse coding to recode the Sc + SPM representation of each image. In particular, we collect the columns of the both matrixes as the bases and use the coding parameters as the updated image representation by learning them through the locality-constrained linear coding (LLC). Finally, linear SVM classifier is leveraged for the final classification. Experimental results show that the proposed method achieves or outperforms the state-of-the-art results on several benchmarks. © 2011 IEEE.

关键词： Image representation

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Automatic motion-guided video stylization and personalization 11

Automatic motion-guided video stylization and personalizatio...

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19th ACM International Conference on Multimedia ACM Multimedia 2011, MM'11

作者： Cao, Chen Chen, Shifeng Zhang, Wei Tang, Xiaoou Shenzhen Key Laboratory for Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Department of Information Engineering Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9781450306164

Video stylization transfers a source video into an artistic version while maintaining temporal coherence between adjacent frames. In this paper, we formulate the unsupervised example-based video stylization with Markov random field model. In our algorithm, we implement an improved optical flow algorithm to maintain temporal coherence while improve the accuracy of estimation along motion boundaries. We also extend our algorithm to the application of video personalization, in which human faces keep clear and distinguishable. A series of techniques are fused in video personalization, including face detection and alignment, motion flow, skin detection, and illumination blending. Given a source video and a style template image, our algorithm produces the stylized and/or personalized video(s) automatically. Experimental results demonstrate that our algorithm performs excellently in both video stylization and personalization. Copyright 2011 ACM.

关键词： Face recognition

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C⁴: Exploring Multiple Solutions in Graphical Models by Cluster Sampling

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IEEE TRANSACTIONS ON pattern ANALYSIS AND MACHINE INTELLIGENCE 2011年第9期33卷 1713-1727页

作者： Porway, Jake Zhu, Song-Chun New York Times Co R&D Div New York NY USA Univ Calif Los Angeles Dept Stat Los Angeles CA 90024 USA Univ Calif Los Angeles Dept Comp Sci Los Angeles CA 90024 USA

This paper presents a novel Markov Chain Monte Carlo (MCMC) inference algorithm called C-4-Clustering with Cooperative and Competitive Constraints-for computing multiple solutions from posterior probabilities defined on graphical models, including Markov random fields (MRF), conditional random fields (CRF), and hierarchical models. The graphs may have both positive and negative edges for cooperative and competitive constraints. C-4 is a probabilistic clustering algorithm in the spirit of Swendsen-Wang [34]. By turning the positive edges on/off probabilistically, C-4 partitions the graph into a number of connected components (ccps) and each ccp is a coupled subsolution with nodes connected by positive edges. Then, by turning the negative edges on/off probabilistically, C-4 obtains composite ccps (called cccps) with competing ccps connected by negative edges. At each step, C-4 flips the labels of all nodes in a cccp so that nodes in each ccp keep the same label while different ccps are assigned different labels to observe both positive and negative constraints. Thus, the algorithm can jump between multiple competing solutions (or modes of the posterior probability) in a single or a few steps. It computes multiple distinct solutions to preserve the intrinsic ambiguities and avoids premature commitments to a single solution that may not be valid given later context. C-4 achieves a mixing rate faster than existing MCMC methods, such as various Gibbs samplers [15], [26] and Swendsen-Wang cuts [2], [34]. It is also more "dynamic" than common optimization methods such as ICM [3], LBP [21], [37], and graph cuts [4], [20]. We demonstrate the C4 algorithm in line drawing interpretation, scene labeling, and object recognition.

关键词： Markov random fields computer vision graph labeling probabilistic algorithms constraint satisfaction Monte Carlo

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Baby Talk: Understanding and Generating Simple Image Descriptions

Baby Talk: Understanding and Generating Simple Image Descrip...

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IEEE Conference on computer vision and pattern recognition

作者： Girish Kulkarni Visruth Premraj Sagnik Dhar Siming Li Yejin Choi Alexander C. Berg Tamara L. Berg Stony Brook University

ISBN: (纸本)9781457703942

We posit that visually descriptive language offers computer vision researchers both information about the world, and information about how people describe the world. The potential benefit from this source is made more significant due to the enormous amount of language data easily available today. We present a system to automatically generate natural language descriptions from images that exploits both statistics gleaned from parsing large quantities of text data and recognition algorithms from computer vision. The system is very effective at producing relevant sentences for images. It also generates descriptions that are notably more true to the specific image content than previous work.

关键词： images computer vision language data language description Infant Speaking SURVEY ANALYSIS LANGUAGES Imagery (Psychotherapy) Image

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Visual and Semantic Similarity in ImageNet

Visual and Semantic Similarity in ImageNet

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IEEE Conference on computer vision and pattern recognition

作者： Thomas Deselaers Vittorio Ferrari Computer Vision Laboratory ETH Zurich Google

ISBN: (纸本)9781457703942

Many computer vision approaches take for granted positive answers to questions such as "Are semantic categories visually separable?" and "Is visual similarity correlated to semantic similarity?". In this paper, we study experimentally whether these assumptions hold and show parallels to questions investigated in cognitive science about the human visual system. The insights gained from our analysis enable building a novel distance function between images assessing whether they are from the same basic-level category. This function goes beyond direct visual distance as it also exploits semantic similarity measured through ImageNet. We demonstrate experimentally that it outperforms purely visual distances.

关键词： Visualization Semantics distance function images Cognitive Science computer vision

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Joint Segmentation and Classification of Human Actions in Video

Joint Segmentation and Classification of Human Actions in Vi...

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IEEE Conference on computer vision and pattern recognition

作者： Minh Hoai Zhen-Zhong Lan Fernando De la Torre Carnegie Mellon University

ISBN: (纸本)9781457703942

Automatic video segmentation and action recognition has been a long-standing problem in computer vision. Much work in the literature treats video segmentation and action recognition as two independent problems;while segmentation is often done without a temporal model of the activity, action recognition is usually performed on pre-segmented clips. In this paper we propose a novel method that avoids the limitations of the above approaches by jointly performing video segmentation and action recognition. Unlike standard approaches based on extensions of dynamic Bayesian networks, our method is based on a discriminative temporal extension of the spatial bag-of-words model that has been very popular in object recognition. The classification is performed robustly within a multi-class SVM framework whereas the inference over the segments is done efficiently with dynamic programming. Experimental results on honeybee, Weizmann, and Hollywood datasets illustrate the benefits of our approach compared to state-of-the-art methods.

关键词： Videotapes segmentation Segmentation images computer vision Bayesian networks recognition (Psychology) object recognition Action

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Exploring Cortical Folding pattern Variability Using Local Image Features 1

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13th International Conference on Medical Image Computing and computer-Assisted Intervention (MICCAI)

作者： Rajalingham, Rishi Toews, Matthew Collins, D. Louis Arbel, Tal McGill Univ Ctr Intelligent Machines Montreal PQ H3A 2T5 Canada Brigham & Womens Hosp Harvard Med Sch Boston MA 02115 USA McGill Univ Montreal Neurol Inst Montreal PQ H3A 2T5 Canada

ISBN: (数字)9783642184215

ISBN: (纸本)9783642184208

The variability in cortical morphology across subjects makes it difficult to develop a general atlas of cortical sulci. In this paper, we present a data-driven technique for automatically learning cortical folding patterns from MR brain images. A local image feature-based model is learned using machine learning techniques, to describe brain images as a collection of independent, co-occurring, distinct, localized image features which may not be present in all subjects. The choice of feature type (SIFT, KLT, Harris-affine) is explored with regards to identifying cortical folding patterns while also uncovering their group-related variability across subjects. The model is built on lateral volume renderings from the ICBM dataset, and applied to hemisphere classification in order to identify patterns of lateralization based on each feature type.

关键词： Volume rendering

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A Machine vision Framework for Automated Localization of Microinjection Sites on Low-Contrast Single Adherent Cells

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8th International Conference on Image Analysis and recognition (ICIAR) / 2nd International Conference on Autonomous and Intelligent Systems (AIS)

作者： Esmaeilsabzali, Hadi Sakaki, Kelly Dechev, Nikolai Burke, Robert D. Park, Edward J. Simon Fraser Univ Sch Engn Sci Surrey BC Canada Univ Victoria Dept Mech Engn Victoria BC V8W 2Y2 Canada Univ Victoria Dept Biochem & Microbiol Victoria BC V8W 2Y2 Canada

ISBN: (纸本)9783642215957;9783642215964

To perform high-throughput single-cell studies, automation of the potential experiments is quite necessary. Due to their complex morphology, automatic manipulation and visual analysis of adherent cells which include a wide range of mammalian cell lines is a challenging task. In this paper, the problem of adherent cells localization for the purpose of automated robotic microinjection has been stated and a practical two-stage texture-based solution has been proposed. The method has been tested on NIH/3T3 cells and the results have been reported.

关键词： cell segmentation cytoplasmic and nucleic microinjection adherent cells region-growing algorithm mathematical morphology 3T3 cells

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