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检索条件"任意字段=MIPPR 2011: Pattern Recognition and Computer Vision"

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A Hierarchical Conditional Random Field Model for Labeling and Segmenting Images of Street Scenes

A Hierarchical Conditional Random Field Model for Labeling a...

引用

IEEE Conference on computer vision and pattern recognition

作者： Qixing Huang Mei Han Bo Wu Sergey Ioffe Stanford University Google Inc.

ISBN: (纸本)9781457703942

Simultaneously segmenting and labeling images is a fundamental problem in computer vision. In this paper, we introduce a hierarchical CRF model to deal with the problem of labeling images of street scenes by several distinctive object classes. In addition to learning a CRF model from all the labeled images, we group images into clusters of similar images and learn a CRF model from each cluster separately. When labeling a new image, we pick the closest cluster and use the associated CRF model to label this image. Experimental results show that this hierarchical image labeling method is comparable to, and in many cases superior to, previous methods on benchmark data sets. In addition to segmentation and labeling results, we also showed how to apply the image labeling result to rerank Google similar images.

关键词： images labelled image Corticotropin-Releasing Hormone field simulation Hierarchical computer vision Imagery (Psychotherapy) Image

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Radiometric Calibration by Transform Invariant Low-rank Structure

Radiometric Calibration by Transform Invariant Low-rank Stru...

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IEEE Conference on computer vision and pattern recognition

作者： Joon-Young Lee Boxin Shi Yasuyuki Matsushita In-So Kweon Katsushi Ikeuchi Robotics and Computer Vision Lab KAIST The University of Tokyo Microsoft Research Asia

ISBN: (纸本)9781457703942

We present a robust radiometric calibration method that capitalizes on the transform invariant low-rank structure of sensor irradiances recorded from a static scene with different exposure times. We formulate the radiometric calibration problem as a rank minimization problem. Unlike previous approaches, our method naturally avoids over-fitting problem;therefore, it is robust against biased distribution of the input data, which is common in practice. When the exposure times are completely unknown, the proposed method can robustly estimate the response function up to an exponential ambiguity. The method is evaluated using both simulation and real-world datasets and shows a superior performance than previous approaches.

关键词： exposure time incoming data Radiation Response function Invariant Transform CALIBRATION METHODS

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A Probabilistic Representation for Efficient Large Scale Visual recognition Tasks

A Probabilistic Representation for Efficient Large Scale Vis...

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IEEE Conference on computer vision and pattern recognition

作者： Subhabrata Bhattacharya Rahul Sukthankar Rong Jin Mubarak Shah Computer Vision Lab University of Central Florida Intel Labs and Carnegie Mellon University Michigan State University

ISBN: (纸本)9781457703942

In this paper, we present an efficient alternative to the traditional vocabulary based on bag-of-visual words (BoW) used for visual classification tasks. Our representation is both conceptually and computationally superior to the bag-of-visual words: (1) We iteratively generate a Maximum Likelihood estimate of an image given a set of characteristic features in contrast to the BoW methods where an image is represented as a histogram of visual words, (2) We randomly sample a set of characteristic features instead of employing computation-intensive clustering algorithms used during the vocabulary generation step of BoW methods. Our performance compares favorably to the state-of-the-art on experiments over three challenging human action and a scene categorization dataset, demonstrating the universal applicability of our method.

关键词： BOW Imagery (Psychotherapy) images Visualization Clustering algorithms Religious Missions histograms Vocabulary maximum likelihood estimation

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Artificial Intelligence Applications and Innovations 2011

引用

丛书名： IFIP Advances in Information and Communication Technology

2011年

作者： Lazaros S. Iliadis Ilias Maglogiannis Harris Papadopoulos

ISBN: (数字)9783642239601

ISBN: (纸本)9783642239595

The two-volume set IFIP AICT 363 and 364 constitutes the refereed proceedings of the 12th International Conference on Engineering Applications of Neural Networks, EANN 2011, and the 7th IFIP WG 12.5 International Conference, AIAI 2011, held jointly in Corfu, Greece, in September 2011. The 52 revised full papers and 28 revised short papers presented together with 31 workshop papers were carefully reviewed and selected from 150 submissions. The second volume includes the papers that were accepted for presentation at the AIAI 2011 conference. They are organized in topical sections on computer vision and robotics, classification/pattern recognition, financial and management applications of AI, fuzzy systems, learning and novel algorithms, recurrent and radial basis function ANN, machine learning, generic algorithms, data mining, reinforcement learning, Web applications of ANN, medical applications of ANN and ethics of AI, and environmental and earth applications of AI. The volume also contains the accepted papers from the First Workshop on Computational Intelligence in Software Engineering (CISE 2011) and the Workshop on Artificial Intelligence Applications in Biomedicine (AIAB 2011).

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Assessment of time-of-flight cameras for short camera-object distances

Assessment of time-of-flight cameras for short camera-object...

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

作者： Stunner, Michael Becker, Guido Hornegger, Joachim Pattern Recognition Lab. Department of Computer Science Friedrich-Alexander-University Erlangen-Niirnberg Germany Germany Lion Systems S A. Luxembourg

ISBN: (纸本)9783905673852

In this paper we have compared Time-of-Flight cameras of different vendors at object-camera distances of 500 mm, 1500 mm and 2500 mm. The aim was to find the highest possible precision at the distance of 500 mm, to estimate the change of the accuracy depending on scene-reflectivity and working distance and to investigate the possibility to use the cameras as per-pixel sub-centimeter accurate measuring devices. To this end, we have evaluated the variation of the distance measurement noise over several distances as well as the minimum noise we could achieve with each camera. As the amplitude-dependent distance error may become significantly large, we also tried to quantify it in order to estimate if it can be reduced to fulfill given accuracy requirements. We compared a Camcube3 from PMD Technologies, a Swissranger 4000 from MESA Imaging and a C70E from Fotonic. All cameras showed different behaviors in terms of temporal noise, variation of noise and amplitude dependency. The Camcube showed the strongest amplitude dependent effects. The minimum standard deviations at 500 mm distance were at 4.8 mm for the Camcube, 1.6 mm for the Swissranger and 0.9 mm for the C70E. © The Eurographics Association 2011.

关键词： Cameras

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Nonparametric Density Estimation on A Graph: Learning Framework, Fast Approximation and Application in Image Segmentation

Nonparametric Density Estimation on A Graph: Learning Framew...

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IEEE Conference on computer vision and pattern recognition

作者： Zhiding Yu Oscar C. Au Ketan Tang Chunjing Xu Dept. of Electronic and Computer Engineering Hong Kong University of Science and Technology Shenzhen Inst. of Advanced Technology Chinese Academy of Sciences

ISBN: (纸本)9781457703942

We present a novel framework for tree-structure embedded density estimation and its fast approximation for mode seeking. The proposed method could find diverse applications in computer vision and feature space analysis. Given any undirected, connected and weighted graph, the density function is defined as a joint representation of the feature space and the distance domain on the graph's spanning tree. Since the distance domain of a tree is a constrained one, mode seeking can not be directly achieved by traditional mean shift in both domain. we address this problem by introducing node shifting with force competition and its fast approximation. Our work is closely related to the previous literature of nonparametric methods. One shall see, however, that the new formulation of this problem can lead to many advantages and new characteristics in its application, as will be illustrated later in this paper.

关键词： Eigenvector image segmentation images computer vision approximation Fasting Line graph Density functions Domain Frameworks

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Adaptive and Natural Computing Algorithms - 10th International Conference, ICANNGA 2011, Proceedings

Adaptive and Natural Computing Algorithms - 10th Internation...

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10th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2011

ISBN: (纸本)9783642202810

The proceedings contain 84 papers. The topics discussed include: new measure of boolean factor analysis quality;mechanisms of adaptive spatial integration in a neural model of cortical motion processing;self-organized short-term memory mechanism in spiking neural network;approximation of functions by multivariable hermite basis: a hybrid method;using pattern recognition to predict driver intent;neural networks committee for improvement of metal's mechanical properties estimates;logarithmic multiplier in hardware implementation of neural networks;a robust learning model for dealing with missing values in many-core architectures;a model of saliency-based selective attention for machine vision inspection application;grapheme-phoneme translator for Brazilian Portuguese;and improvement of inventory control under parametric uncertainty and constraints.

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Efficient Color Transfer Method Based on Colormap Clustering for Night vision Applications

Efficient Color Transfer Method Based on Colormap Clustering...

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National Conference on computer vision, pattern recognition, Image Processing and Graphics (NCVPRIPG)

作者： Ishit Makwana Tanish Zaveri Vivek Gupta Department of Electronics and Communication EngineeringInstitute of Technology Nirma University Ahmedabad Gujarat India

Recent night-vision cameras provide multiband images with complementary information which is useful to enable operations at night and in adverse weather conditions. The grayscale fused image is unnatural in appearance and therefore it is difficult to design a reliable intelligent system based on this results. In this paper, efficient natural color transfer method based on color map clustering is proposed for Night vision applications. The proposed algorithm is a novel and efficient framework to colorize the night vision imagery utilizing color map clustering and cluster recognition based on color similarity. The target color look up table is derived from the set of natural color image database for a specific environment. Proposed method is applied on datasets of different environment and compared with standard color transfer method using objective evaluation parameters to evaluate efficacy of color transfer algorithm. The simulation results show that proposed method enhances the natural color appearance in the resultant image and provides consistent results for various datasets.

关键词： Image color analysis Night vision Measurement Clustering algorithms Color Image fusion Transforms

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12th INNS EANN-SIG International Conference on Engineering Applications of Neural Networks, EANN2011 and 7th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2011

12th INNS EANN-SIG International Conference on Engineering A...

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12th INNS EANN-SIG International Conference, EANN2011 and 7th IFIP WG 12.5 International Conference, AIAI 2011

ISBN: (纸本)9783642239595

The proceedings contain 56 papers. The special focus in this conference is on computer vision, Robotics, pattern recognition, Financial and Management Applications of AI. The topics include: Real time robot policy adaptation based on intelligent algorithms;a model and simulation of early-stage vision as a developmental sensorimotor process;a new discernibility metric and its application on pattern classification and feature evaluation;time variations of association rules in market basket analysis;a software platform for evolutionary computation with pluggable parallelism and quality assurance;disruption management optimization for military logistics;fuzzy and neuro-symbolic approaches to assessment of bank loan applicants;LQR-mapped fuzzy controller applied to attitude stabilization of a power-aided-unicycle;optimal fuzzy controller mapped from LQR under power and torque constraints;a novel scheme for multi-armed Bernoulli bandit problems;a multivalued recurrent neural network for the quadratic assignment problem;employing a radial-basis function artificial neural network to classify western and transition European economies based on the emissions of air pollutants and on their income;elicitation of user preferences via incremental learning in a declarative modelling environment;intelligent software project scheduling and team staffing with genetic algorithms;comparative analysis of content-based and context-based similarity on musical data and a random forests text transliteration system for Greek digraphia.

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Multi-Label Dimensionality Reduction (Chapman & Hall/CRC Machine Learning & pattern recognition)

Multi-Label Dimensionality Reduction (Chapman & Hall/CRC Mac...

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2011年

作者： Jieping Ye Shuiwang Ji Liang Sun

ISBN: (纸本)9781439806159

A comprehensive reference for researchers in machine learning, data mining, and computer vision, this book presents in-depth, systematic discussions on algorithms and applications for dimensionality reduction. It covers emerging models for general dimensionality reduction in multi-label classification. The book also presents a novel framework to unify a variety of models. Based on the discussions of the models and theory, the authors provide thorough analysis and comparison of the algorithms used in these models. They also include applications of these models and algorithms in bioinformatics and biomedical informatics. A supporting website provides updated information.

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