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检索条件"任意字段=IEEE-Computer-Society Conference on Computer Vision and Pattern Recognition Workshops"

共 8962 条记录，以下是4491-4500 订阅

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Standardization of intensity-values acquired by Time-of-Flight-cameras

Standardization of intensity-values acquired by Time-of-Flig...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Michael Sturmer Jochen Penne Joachim Hornegger Chair of Pattern Recognition University of Erlangen Nuremberg Germany

The intensity-images captured by time-of-flight (ToF)-cameras are biased in several ways. The values differ significantly, depending on the integration time set within the camera and on the distance of the scene. Whereas the integration time leads to an almost linear scaling of the whole image, the attenuation due to the distance is nonlinear, resulting in higher intensities for objects closer to the camera. The background regions that are farther away contain comparably low values, leading to a bad contrast within the image. Another effect is that some kind of specularity may be observed due to uncommon reflecting conditions at some points within the scene. These three effects lead to intensity images which exhibit significantly different values depending on the integration time of the camera and the distance to the scene, thus making parameterization of processing steps like edge-detection, segmentation, registration and threshold computation a tedious task. Additionally, outliers with exceptionally high values lead to insufficient visualization results and problems in processing. In this work we propose scaling techniques which generate images whose intensities are independent of the integration time of the camera and the measured distance. Furthermore, a simple approach for reducing specularity effects is introduced.

关键词： Standardization Cameras Layout Image segmentation Time measurement Software algorithms pattern recognition Attenuation Visualization Image generation

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Tracking objects in 6D for reconstructing static scenes

Tracking objects in 6D for reconstructing static scenes

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Agnes Swadzba Niklas Beuter Joachim Schmidt Gerhard Sagerer Applied Computer Science Faculty of Technology Bielefeld University Bielefeld Germany

This paper focuses on two aspects of a human robot interaction scenario: Detection and tracking of moving objects, e.g., persons is necessary for localizing possible interaction partners and reconstruction of the surroundings can be used for navigation purposes and room categorization. Although these processes can be addressed independent from each other, we show that using the available data in exchange enables a more exact reconstruction of the static scene. A 6D data representation consisting of 3D Time-of-Flight (ToF) Sensor data and computed 3D velocities allows segmenting the scene into clusters with consistent velocities. A weak object model is applied to localize and track objects within a particle filter framework. As a consequence, points emerging from moving objects can be neglected during reconstruction. Experiments demonstrate enhanced reconstruction results in comparison to pure bottom-up methods, especially for very short image sequences.

关键词： Layout Image reconstruction Mobile robots Stereo vision Clouds Human robot interaction Cameras Navigation Image sequences Sensor systems

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Visual cortex on the GPU: Biologically inspired classifier and feature descriptor for rapid recognition

Visual cortex on the GPU: Biologically inspired classifier a...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Kris Woodbeck Gerhard Roth Huiqiong Chen School of Information Technology and Engineering University of Ottawa Ottawa ONT Canada Faculty of Computer Science Dalhousie University Halifax NS Canada

We present a biologically motivated classifier and feature descriptors that are designed for execution on single instruction multi data hardware and are applied to high speed multiclass object recognition. Our feature extractor uses a cellular tuning approach to select the optimal Gabor filters to process a given input, followed by the computation of scale and rotation-invariant features that are sparsified with a lateral inhibition mechanism. Neighboring features are pooled into feature hierarchies whose resonant properties are used to select the most representative hierarchies for each object class. The feature hierarchies are used to train a novel form of adaptive resonance theory classifier for multiclass object recognition. Our model has unprecedented biologically plausibility at all stages and uses the programmable graphics processing unit (GPU) for high speed feature extraction and object classification. We demonstrate the speedup achieved with the use of the GPU and test our model on the Caltech 101 and 15 Scene datasets, where our system achieves state-of-the-art performance.

关键词： Object recognition Feature extraction Resonance Biological system modeling Hardware Data mining Gabor filters Biology computing Graphics System testing

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Decision-level fusion strategies for correlated biometric classifiers

Decision-level fusion strategies for correlated biometric cl...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Kalyan Veeramachaneni Lisa Osadciw Arun Ross Nisha Srinivas Department of Electrical Engineering and Computer Science Syracuse University USA Department of Computer Science and Electrical Engineering West Virginia University USA

The focus of this paper is on designing decision-level fusion strategies for correlated biometric classifiers. In this regard, two different strategies are investigated. In the first strategy, an optimal fusion rule based on the likelihood ratio test (LRT) and the chair Varshney rule (CVR) is discussed for correlated hypothesis testing where the thresholds of the individual biometric classifiers are first fixed. In the second strategy, a particle swarm optimization (PSO) based procedure is proposed to simultaneously optimize the thresholds and the fusion rule. Results are presented on (a) a synthetic score data conforming to a multivariate normal distribution with different covariance matrices, and (b) the NIST BSSR dataset. We observe that the PSO-based decision fusion strategy performs well on correlated classifiers when compared with the LRT-based method as well as the average sum rule employing z-score normalization. This work highlights the importance of incorporating the correlation structure between classifiers when designing a biometric fusion system.

关键词： Biometrics Testing Particle swarm optimization NIST Fusion power generation Engines computer science Light rail systems Gaussian distribution Covariance matrix

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A probabilistic representation of LiDAR range data for efficient 3D object detection

A probabilistic representation of LiDAR range data for effic...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Theodore C. Yapo Charles V. Stewart Richard J. Radke Department of Electrical Computer and Systems Engineering Limited Department of Computer Science Rensselaer Polytechnic Institute Troy NY USA

We present a novel approach to 3D object detection in scenes scanned by LiDAR sensors, based on a probabilistic representation of free, occupied, and hidden space that extends the concept of occupancy grids from robot mapping algorithms. This scene representation naturally handles LiDAR sampling issues, can be used to fuse multiple LiDAR data sets, and captures the inherent uncertainty of the data due to occlusions and clutter. Using this model, we formulate a hypothesis testing methodology to determine the probability that given 3D objects are present in the scene. By propagating uncertainty in the original sample points, we are able to measure confidence in the detection results in a principled way. We demonstrate the approach in examples of detecting objects that are partially occluded by scene clutter such as camouflage netting.

关键词： Laser radar Object detection Layout Uncertainty Sampling methods Optical sensors computer science Robot sensing systems Orbital robotics Testing

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Scalable classifiers for Internet vision tasks

Scalable classifiers for Internet vision tasks

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Tom Yeh John J. Lee Trevor Darrell MIT EECS and CSAIL Cambridge MA USA University of California Berkeley EECS and ICSI Berkeley CA USA

Object recognition systems designed for Internet applications typically need to adapt to userspsila needs in a flexible fashion and scale up to very large data sets. In this paper, we analyze the complexity of several multiclass SVM-based algorithms and highlight the computational bottleneck they suffer at test time: comparing the input image to every training image. We propose an algorithm that overcomes this bottleneck; it offers not only the efficiency of a simple nearest-neighbor classifier, by voting on class labels based on the k nearest neighbors quickly determined by a vocabulary tree, but also the recognition accuracy comparable to that of a complex SVM classifier, by incorporating SVM parameters into the voting scores incrementally accumulated from individual image features. Empirical results demonstrate that adjusting votes by relevant support vector weights can improve the recognition accuracy of a nearest-neighbor classifier without sacrificing speed. Compared to existing methods, our algorithm achieves a ten-fold speed increase while incurring an acceptable accuracy loss that can be easily offset by showing about two more labels in the result. The speed, scalability, and adaptability of our algorithm makes it suitable for Internet vision applications.

关键词： Internet Voting Classification tree analysis Support vector machines Support vector machine classification Object recognition Image analysis Algorithm design and analysis Testing Nearest neighbor searches

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A study of query by semantic example

A study of query by semantic example

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

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

In recent years, query-by-semantic-example (QBSE) has become a popular approach to do content based image retrieval. QBSE extends the well established query-by-example retrieval paradigm to the semantic domain. While various authors have pointed out the benefits of QBSE, there are still various open questions with respect to this paradigm. These include a lack of precise understanding of how the overall performance depends on various different parameters of the system. In this work, we present a systematic experimental study of the QBSE framework. This can be broadly divided into three categories. First, we examine the space of low-level visual features for its effects on the retrieval performance. Second, we study the space of learned semantic concepts, herein denoted as the ldquosemantic spacerdquo, and show that not all semantic concepts are equally informative for retrieval. Finally, we present a study of the intrinsic structure of the semantic space, by analyzing the contextual relationships between semantic concepts and show that this intrinsic structure is crucial for the performance improvements.

关键词： Image retrieval Content based retrieval Labeling Image analysis Performance analysis Vocabulary Rendering (computer graphics) Image databases Terminology Shape

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3D face matching and registration based on hyperbolic Ricci flow

3D face matching and registration based on hyperbolic Ricci ...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Wei Zeng Xiaotian Yin Yun Zeng Yukun Lai Xianfeng Gu Dimitris Samaras Computer Science Department Stony Brook University NY USA Graduate School of Chinese Academy of Sciences China Institute of Computing Technology Chinese Academy and Sciences China

3D surface matching is fundamental for shape analysis. As a powerful method in geometric analysis, Ricci flow can flexibly design metrics by prescribed target curvature. In this paper we describe a novel approach for matching surfaces with complicated topologies based on hyperbolic Ricci flow. For surfaces with negative Euler characteristics, such as a human face with holes (eye contours), the canonical hyperbolic metric is conformal to the original and can be efficiently computed. Then the surface can be canonically decomposed to hyperbolic hexagons. By matching the corresponding hyperbolic hexagons, the matching between surfaces can be easily established. Compared to existing methods, hyperbolic Ricci flow induces diffeomorphisms between surfaces with complicated topologies with negative Euler characteristics, while avoiding singularities. Furthermore, all the boundaries are intrinsically mapped to hyperbolic lines as alignment constraints. Finally, we demonstrate the applicability of this intrinsic shape representation for 3D face matching and registration.

关键词： Topology Shape Humans Face computer vision Multi-stage noise shaping Conformal mapping computer science Deformable models Convergence

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Estimation of acoustic impedance from multiple ultrasound images with application to spatial compounding

Estimation of acoustic impedance from multiple ultrasound im...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Christian Wachinger Ramtin Shams Nassir Navab Computer Aided Medical Procedures Technische Universitat Munchen Germany Research School of Information Sciences and Engineering Australian National University Australia

Reflection of sound waves, due to acoustic impedance mismatch at the interface of two media, is the principal physical property which allows visualization with ultrasound. In this paper, we investigate reconstruction of the acoustic impedance from ultrasound images for the first time. Similar to spatial compounding, we combine multiple images to improve the estimation. We use phase information to determine regions of high reflection from an ultrasound image. We model the physical imaging process with an emphasis on the reflection of sound waves. The model is used in computing the acoustic impedance (up to a scale) from areas of high reflectivity. The acoustic impedance image can either be directly visualized or be used in simulation of ultrasound images from an arbitrary point of view. The experiments performed on in-vitro and in-vivo data show promising results.

关键词： Acoustic applications Impedance Ultrasonic imaging Acoustic reflection Acoustic waves Image reconstruction Acoustic imaging Reflectivity Data visualization Computational modeling

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Detecting multiple moving objects in crowded environments with coherent motion regions

Detecting multiple moving objects in crowded environments wi...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Anil M. Cheriyadat Budhendra L. Bhaduri Richard J. Radke Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Troy NY USA Oak Ridge National Laboratory Computational Sciences and Engineering Oak Ridge TN USA

We propose an object detection system that uses the locations of tracked low-level feature points as input, and produces a set of independent coherent motion regions as output. As an object moves, tracked feature points on it span a coherent 3D region in the space-time volume defined by the video. In the case of multi-object motion, many possible coherent motion regions can be constructed around the set of all feature point tracks. Our approach is to identify all possible coherent motion regions, and extract the subset that maximizes an overall likelihood function while assigning each point track to at most one motion region. We solve the problem of finding the best set of coherent motion regions with a simple greedy algorithm, and show that our approach produces semantically correct detections and counts of similar objects moving through crowded scenes.

关键词： Object detection Motion detection Tracking Humans Shape Feature extraction Greedy algorithms Layout Spatiotemporal phenomena Assembly

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