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检索条件"任意字段=26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013"

共 656 条记录，以下是481-490 订阅

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Volumetric Reconstruction from Multi-Energy Single-View Radiography

Volumetric Reconstruction from Multi-Energy Single-View Radi...

引用

26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.10

作者： Sang N. Le Mei Kay Lee Shamima Banu Anthony C. Fang Department of Computer Science School of Computing National University of Singapore Singapore Physical Education and Sports Sciences National Institute of Education Nanyang Technological University Singapore

We address the volumetric reconstruction problem that takes as input a series of orthographic multi-energy X-ray images, producing as output a reconstructed model space consisting of uniform-size mass density voxels. Our approach solves the non-linear constrained optimization formulation problem by constructing a compliant estimate of volumetric distribution, subject to projective and domain constraints, and minimizes variational irregularities. To resolve the inherent ambiguities of single-view formulation, an optional shape model may be introduced to aid the reconstruction process. We demonstrate our method's practical usage as a new in-vivo method for estimating three-dimensional body segmental compositions, and compare its results with those of existing methods.

关键词： Radiography Image reconstruction Image segmentation Magnetic resonance imaging Bones X-ray imaging Costs Minerals Layout Biomedical measurements

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Evaluation of Color Descriptors for Object and Scene recognition

Evaluation of Color Descriptors for Object and Scene Recogni...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.8

作者： Koen E. A. van de Sande theo Gevers Cees G. M. Snoek University of Amsterdam Amsterdam Netherlands

Image category recognition is important to access visual information on the level of objects and scene types. So far, intensity-based descriptors have been widely used. To increase illumination invariance and discriminative power, color descriptors have been proposed only recently. As many descriptors exist, a structured overview of color invariant descriptors in the context of image category recognition is required. therefore, this paper studies the invariance properties and the distinctiveness of color descriptors in a structured way. the invariance properties of color descriptors are shown analytically using a taxonomy based on invariance properties with respect to photometric transformations. the distinctiveness of color descriptors is assessed experimentally using two benchmarks from the image domain and the video domain. From the theoretical and experimental results, it can be derived that invariance to light intensity changes and light color changes affects category recognition. the results reveal further that, for light intensity changes, the usefulness of invariance is category-specific.

关键词： Layout Lighting Image recognition Taxonomy Reflectivity Image color analysis Image retrieval Robustness Photometry Vegetation mapping

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Boosting Ordinal Features for Accurate and Fast Iris recognition

Boosting Ordinal Features for Accurate and Fast Iris Recogni...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.8

作者： Zhaofeng He Zhenan Sun Tieniu Tan Xianchao Qiu Cheng Zhong Wenbo Dong Center for Biometrics and Security Research National Laboratory of Pattern RecognitionNational Laboratory of Pattern Recognition Chinese Academy and Sciences Beijing China

In this paper, we present a novel iris recognition method based on learned ordinal features. Firstly, taking full advantages of the properties of iris textures, a new iris representation method based on regional ordinal measure encoding is presented, which provides an over-complete iris feature set for learning. Secondly, a novel Similarity Oriented Boosting (SOBoost) algorithm is proposed to train an efficient and stable classifier with a small set of features. Compared with Adaboost, SOBoost is advantageous in that it operates on similarity oriented training samples, and therefore provides a better way for boosting strong classifiers. Finally, the well-known cascade architecture is adopted to reorganize the learned SOBoost classifier into a 'cascade', by which the searching ability of iris recognition towards large-scale deployments is greatly enhanced. Extensive experiments on two challenging iris image databases demonstrate that the proposed method achieves state-of-the-art iris recognition accuracy and speed. In addition, SOBoost outperforms Adaboost (Gentle-Adaboost, JS-Adaboost, etc.) in terms of both accuracy and generalization capability across different iris databases.

关键词： Boosting Iris recognition Image databases Spatial databases Machine learning algorithms Large-scale systems Helium Sun Biometrics National security

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Human-Assisted Motion Annotation

Human-Assisted Motion Annotation

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.12

作者： Ce Liu William T. Freeman Edward H. Adelson Yair Weiss CSAIL-MIT Israel Hebrew University of Jerusalem Israel

Obtaining ground-truth motion for arbitrary, real-world video sequences is a challenging but important task for both algorithm evaluation and model design. Existing ground-truth databases are either synthetic, such as the Yosemite sequence, or limited to indoor, experimental setups, such as the database developed in [5]. We propose a human-in-loop methodology to create a ground-truth motion database for the videos taken with ordinary cameras in both indoor and outdoor scenes, using the fact that human beings are experts at segmenting objects and inspecting the match between two frames. We designed an interactive computer vision system to allow a user to efficiently annotate motion. Our methodology is cross-validated by showing that human annotated motion is repeatable, consistent across annotators, and close to the ground truth obtained by [5]. Using our system, we collected and annotated 10 indoor and outdoor real-world videos to form a ground-truth motion database. the source code, annotation tool and database is online for public evaluation and benchmarking.

关键词： Image motion analysis Databases Video sequences Optical sensors Humans computer vision Motion analysis Optical noise Motion estimation Labeling

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Flat Refractive Geometry

Flat Refractive Geometry

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.12

作者： Tali Treibitz Yoav Y. Schechner Hanumant Singh Department of Electrical Engineering Technion-Israel Institute of Technology Haifa Israel Woods Hole Oceanographic Institution Woods Hole MA USA

While the study of geometry has mainly concentrated on single-viewpoint (SVP) cameras, there is growing attention to more general non-SVP systems. Here we study an important class of systems that inherently have a non-SVP: a perspective camera imaging through an interface into a medium. Such systems are ubiquitous: they are common when looking into water-based environments. the paper analyzes the common flat-interface class of systems. It characterizes the locus of the viewpoints (caustic) of this class, and proves that the SVP model is invalid in it. this may explain geometrical errors encountered in prior studies. Our physics-based model is parameterized by the distance of the lens from the medium interface, beside the focal length. the physical parameters are calibrated by a simple approach that can be based on a single-frame. this directly determines the system geometry. the calibration is then used to compensate for modeled system distortion. Based on this model, geometrical measurements of objects are significantly more accurate, than if based on an SVP model. this is demonstrated in real-world experiments.

关键词： Geometry Cameras Marine animals computer vision Solid modeling Microscopy Robot vision systems Humans Stereo vision Marine technology

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Unsupervised Feature Selection via Distributed Coding for Multi-view Object recognition

Unsupervised Feature Selection via Distributed Coding for Mu...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.7

作者： C. Mario Christoudias Raquel Urtasun Trevor Darrell UC Berkeley EECS & ICSI MIT CSAIL USA

Object recognition accuracy can be improved when information from multiple views is integrated, but information in each view can often be highly redundant. We consider the problem of distributed object recognition or indexing from multiple cameras, where the computational power available at each camera sensor is limited and communication between cameras is prohibitively expensive. In this scenario, it is desirable to avoid sending redundant visual features from multiple views. Traditional supervised feature selection approaches are inapplicable as the class label is unknown at each camera. In this paper we propose an unsupervised multi-view feature selection algorithm based on a distributed coding approach. With our method, a Gaussian Process model of the joint view statistics is used at the receiver to obtain a joint encoding of the views without directly sharing information across encoders. We demonstrate our approach on recognition and indexing tasks with multi-view image databases and show that our method compares favorably to an independent encoding of the features from each camera.

关键词： Object recognition Cameras Indexing Distributed computing Gaussian processes Statistical distributions Encoding Image recognition Image databases Image coding

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Rotation Symmetry Group Detection Via Frequency Analysis of Frieze-Expansions

Rotation Symmetry Group Detection Via Frequency Analysis of ...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.12

作者： Seungkyu Lee Robert T. Collins Yanxi Liu Department of Computer Science and Engineering Pennsylvania State University USA Dept. of Electrical Engineering The Pennsylvania State University USA

We present a novel and effective algorithm for rotation symmetry group detection from real-world images. We propose a frieze-expansion method that transforms rotation symmetry group detection into a simple translation symmetry detection problem. We define and construct a dense symmetry strength map from a given image, and search for potential rotational symmetry centers automatically. Frequency analysis, using Discrete Fourier Transform (DFT), is applied to the frieze-expansion patterns to uncover the types and the cardinality of multiple rotation symmetry groups in an image, concentric or otherwise. Furthermore, our detection algorithm can discriminate discrete versus continuous and cyclic versus dihedral symmetry groups, and identify the corresponding supporting regions in the image. Experimental results on over 80 synthetic and natural images demonstrate superior performance of our rotation detection algorithm in accuracy and in speed over the state of the art rotation detection algorithms.

关键词： Frequency Detection algorithms Reflection computer vision Image edge detection Image analysis Discrete Fourier transforms Algorithm design and analysis computer science Discrete transforms

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Pair-Activity Classification by Bi-Trajectories Analysis

Pair-Activity Classification by Bi-Trajectories Analysis

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.12

作者： Yue Zhou Shuicheng Yan thomas S. Huang Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign USA Department of Electrical and Computer Engineering National University of Singapore Singapore

In this paper, we address the pair-activity classification problem, which explores the relationship between two active objects based on their motion information. Our contributions are three-fold. First, we design a set of features, e.g., causality ratio and feedback ratio based on the Granger Causality Test (GCT), for describing the pair-activities encoded as trajectory pairs. these features along with conventional velocity and position features are essentially of multi-modalities, and may be greatly different in scale and importance. To make full use of them, we then present a novel feature normalization procedure to learn the coefficients for weighting these features by maximizing the discriminating power measured by weighted correlation. Finally, we collected a pair-activity database of five categories, each of which consists of about 170 instances. the extensive experiments on this database validate the effectiveness of the designed features for pair-activity representation, and also demonstrate that the proposed feature normalization procedure greatly boosts the pair-activity classification accuracy.

关键词： Information analysis Feedback Testing Spatial databases computer vision Trajectory Motion analysis Power measurement Weight measurement Event detection

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Kernel-based learning of cast shadows from a physical model of light sources and surfaces for low-level segmentation

Kernel-based learning of cast shadows from a physical model ...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.3

作者： Nicolas Martel-Brisson Andre Zaccarin Computer Vision and Systems Laboratory Department of Electrical and Computer Engineering Laval University QUE Canada

In background subtraction, cast shadows induce silhouette distortions and object fusions hindering performance of high level algorithms in scene monitoring. We introduce a nonparametric framework to model surface behavior when shadows are cast on them. Based on physical properties of light sources and surfaces, we identify a direction in RGB space on which background surface values under cast shadows are found. We then model the posterior distribution of lighting attenuation under cast shadows and foreground objects, which allows differentiation of foreground and cast shadow values with similar chromaticity. the algorithms are completely unsupervised and take advantage of scene activity to learn model parameters. Spatial gradient information is also used to reinforce the learning process. Contributions are two-fold. Firstly, with a better model describing cast shadows on surfaces, we achieve a higher success rate in segmenting moving cast shadows in complex scenes. Secondly, obtaining such models is a step toward a full scene parametrization where light source properties, surface reflectance models and scene 3D geometry are estimated for low-level segmentation.

关键词： Light sources Layout Solid modeling Geometry Lighting Reflectivity Object recognition Video sequences Attenuation computer vision

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Scene Classification with Low-dimensional Semantic Spaces and Weak Supervision

Scene Classification with Low-dimensional Semantic Spaces an...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.1

作者： Nikhil Rasiwasia Nuno Vasconcelos Department of Electrical and Computer Engineering University of California San Diego USA

A novel approach to scene categorization is proposed. Similar to previous works of [11, 15, 3, 12], we introduce an intermediate space, based on a low dimensional semantic "theme" image representation. However, instead of learning the themes in an unsupervised manner, they are learned with weak supervision, from casual image annotations. Each theme induces a probability density on the space of low-level features, and images are represented as vectors of posterior theme probabilities. this enables an image to be associated with multiple themes, even when there are no multiple associations in the training labels. An implementation is presented and compared to various existing algorithms, on benchmark datasets. It is shown that the proposed low dimensional representation correlates well with human scene understanding, and is able to learn theme co-occurrences without explicit training. It is also shown to outperform unsupervised latent-space methods, with much smaller training complexity, and to achieve performance close to the state of the art methods, which rely on much higher-dimensional image representations. Finally a study of the effect of dimensionality on the classification performance is presented, indicating that the dimensionality of theme space grows sub-linearly with the number of scene categories.

关键词： Layout Image representation Humans computer vision Robustness Support vector machines Object detection Cities and towns Frequency Large-scale systems

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