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Efficient planar features matching for robot localization using GPU

Efficient planar features matching for robot localization us...

引用

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

作者： Baptiste Charmette Eric Royer Frédéric Chausse Clermont Université LASMEA Université Blaise Pascal France

Matching image features between an image and a map of landmarks is usually a time consuming process in mobile robot localization or Simultaneous Localisation And Mapping algorithms. The main problem is being able to match features in spite of viewpoint changes. Methods based on interest point descriptors such as SIFT have been implemented on GPUs to reach real time performance. In this paper, we present another way to match features with the use of a local 3D model of the features and a motion model of the robot. This matching algorithm dedicated to robot localization would be much too slow if executed on a CPU. Thanks to a GPU implementation, we show that it is possible to achieve real-time performance while offering more robustness than descriptor based methods.

关键词： Robot localization Simultaneous localization and mapping Robustness Robot vision systems Graphics Hardware Central Processing Unit Mobile robots Context modeling Predictive models

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A Smart Sensor with Hyper spectral/Range Fovea and Panoramic Peripheral View

A Smart Sensor with Hyper spectral/Range Fovea and Panoramic...

引用

ieee computer society conference on computer vision and pattern recognition Workshops

作者： Tao Wang Zhigang Zhu Harvey Rhody Department of Computer Science Department of Computer Science The CUNY Graduate Center Center for Imaging Science Rochester Institute of Technology

ISBN: (纸本)9781424439942

We propose an adaptive and effective multimodal peripheral-fovea sensor design for real-time targets tracking. This design is inspired by the biological vision systems for achieving real-time target detection and recognition with a hyperspectral/range fovea and panoramic peripheral view. A realistic scene simulation approach is used to evaluate our sensor design and the related data exploitation algorithms before a real sensor is made. The goal is to reduce development time and system cost while achieving optimal results through an iterative process that incorporates simulation, sensing, processing and evaluation. Important issues such as multimodal sensory component integration, region of interest extraction, target tracking, hyperspectral image analysis and target signature identification are discussed.

关键词： Hyperspectral imaging Panoramic vision bio-inspired sensing Target tracking

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Modeling of anatomical information in clustering of white matter fiber trajectories using Dirichlet distribution

Modeling of anatomical information in clustering of white ma...

引用

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

作者： Mahnaz Maddah Lilla Zollei W. Eric L. Grimson William M. Wells Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA USA Surgical Planning Laboratory Brigham and Women''s Hospital Boston MA USA Martinos Center for Biomedical Imaging Massachusetts General Hospital Boston MA USA

In this work, we describe a white matter trajectory clustering algorithm that allows for incorporating and appropriately weighting anatomical information. The influence of the anatomical prior reflects confidence in its accuracy and relevance. It can either be defined by the user or it can be inferred automatically. After a detailed description of our novel clustering framework, we demonstrate its properties through a set of preliminary experiments.

关键词： Trajectory Clustering algorithms Diffusion tensor imaging Laboratories Hospitals computer science Artificial intelligence Appropriate technology Surgery Technology planning

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Learning bilinear models for two-factor problems in vision

Learning bilinear models for two-factor problems in vision

引用

conference on computer vision and pattern recognition (CVPR)

作者： W.T. Freeman J.B. Tenenbaum MERL-A Mitsubishi Electric Research Laboratories Cambridge MA USA Department of Brain and Cognitive Sciences MIT Cambridge MA USA

In many vision problems, we want to infer two (or more) hidden factors which interact to produce our observations. We may want to disentangle illuminant and object colors in color constancy; rendering conditions from surface shape in shape-from-shading; face identity and head pose in face recognition; or font and letter class in character recognition. We refer to these two factors generically as "style" and "content". Bilinear models offer a powerful framework for extracting the two-factor structure of a set of observations, and are familiar in computational vision from several well-known lines of research. This paper shows how bilinear models can be used to learn the style-content structure of a pattern analysis or synthesis problem, which can then be generalized to solve related tasks using different styles and/or content. We focus on three tasks: extrapolating the style of data to unseen content classes, classifying data with known content under a novel style, and translating data from novel content classes and style to a known style or content. We show examples from color constancy, face pose estimation, shape-from-shading, typography and speech.

关键词： Shape Face recognition Extrapolation Face detection Head Character recognition computer vision pattern analysis Speech recognition Image recognition

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Experiments on visual loop closing using vocabulary trees

Experiments on visual loop closing using vocabulary trees

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

作者： Ankita Kumar Jean-Philippe Tardif Roy Anati Kostas Daniilidis Grasp Laboratory University of Pennsylvania Philadelphia PA USA

In this paper we study the problem of visual loop closing for long trajectories in an urban environment. We use GPS positioning only to narrow down the search area and use pre-built vocabulary trees to find the best matching image in this search area. Geometric consistency is then used to prune out the bad matches. We compare several vocabulary trees on a sequence of 6.5 kilometers. We experiment with hierarchical k-means based trees as well as extremely randomized trees and compare results obtained using five different trees. We obtain the best results using extremely randomized trees. After enforcing geometric consistency the matched images look promising for structure from motion applications.

关键词： Vocabulary Global Positioning System Cameras Road vehicles System testing Robot vision systems Motion estimation Layout Image segmentation Delay

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Fusion-Based Background-Subtraction using Contour Saliency

Fusion-Based Background-Subtraction using Contour Saliency

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

作者： J.W. Davis V. Sharma Department of Computer Science and Engineering Ohio State Uinversity Columbus OH USA

We present a new contour-based background-subtraction technique using thermal and visible imagery for persistent object detection in urban settings. Statistical backgroundsubtraction in the thermal domain is used to identify the initial regions-of-interest. Color and intensity information are used within these areas to obtain the corresponding regionsof- interest in the visible domain. Within each region, input and background gradient information are combined to form a Contour Saliency Map. The binary contour fragments, obtained from corresponding Contour Saliency Maps, are then combined. An A path-constrained search along watershed boundaries is used to complete and close any broken contour segments. Lastly, the contour image is flood- filled to produce silhouettes. Results of our approach are presented and compared against manually segmented data.

关键词： Object detection Cameras Layout Thermal sensors Image segmentation Lighting Temperature sensors computer vision computer science Thermal engineering

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Identifying sensors from fingerprint images

Identifying sensors from fingerprint images

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

作者： Nick Bartlow Nathan Kalka Bojan Cukic Arun Ross West Virginia University Morgantown WV USA

In this paper we study the application of hardware fingerprinting based on PRNU noise analysis of biometric fingerprint devices for sensor identification. For each fingerprint sensor, a noise reference pattern is generated and subsequently correlated with noise residuals extracted from test images. We experiment on three different databases including a total of 20 fingerprint sensors. Our results indicate that fingerprint sensor identification at unit level is attainable with promising prospects. Our analysis indicates that in many cases identification can be performed even when one only has access to a limited number of samples. For two of the three databases one can train on less than 8 images per device and establish sensor identification with little or no misclassification error. On the third database, high levels of identification performance can be achieved when training on similar amounts of images required for other types of sensor identification such as cameras or scanners.

关键词： Image sensors Fingerprint recognition Image matching Biosensors Image databases Noise generators Hardware Biometrics Test pattern generators Testing

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Face age estimation using model selection

Face age estimation using model selection

引用

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

作者： Cuixian Chen Yaw Chang Karl Ricanek Yishi Wang Computer Science UNCW USA

Face age estimation is a difficult problem due to the dynamics of facial aging and its complex interactions owing to genetics and behavior factors. In this work we develop a robust age estimation system tuned by model selection that outperforms all prior systems on the FG-NET face database. We study various model selection methods systematically to determine the best selection methods among Least Angle Regression (LAR), Principle Component Analysis (PCA), and Locality Preserving Projections (LPP) for age estimation. Our performance analysis on PAL and FG-NET databases suggest that age estimation with LAR or LPP outperforms the full feature model. Furthermore, this work develops a novel operator named “graph age preserving” (GAP) to build a neighborhood graph for LPP for age estimation.

关键词： Active appearance model Aging Robustness Spatial databases Principal component analysis Humans Vectors computer science Genetics Face

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Localized Radon transform-based detection of linear features in noisy images

Localized Radon transform-based detection of linear features...

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

作者： Copeland Ravichandran Trivedi Computer Vision and Robotics Research Laboratory Electrical and Computer Engineering Department University of Tennessee Knoxville TN USA

This paper presents a Radon transform-based approach to the detection of linear features in images characterized by high noise levels. This approach is based on the localized Radon transform where the intensity integration is performed over short line segments rather than across the entire image. The algorithm, referred to as the feature space line detector (FSLD) algorithm, is tested on synthetic images of linear features with very high noise levels. The results of this testing demonstrate the algorithm's robustness in the presence of noise, as well as its ability to detect and localize linear features that are significantly shorter than the image dimensions or that display some curvature.< >

关键词： Feature extraction

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Robust occluding contour detection using the Hausdorff distance

Robust occluding contour detection using the Hausdorff dista...

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conference on computer vision and pattern recognition (CVPR)

作者： Xilin Yi O.I. Camps Department of Electrical Engineering The Penosylkania State University University Park PA Pennsylvania State University University Park PA US

In this paper, a correlational approach for distinguishing occluding contours from object markings for 3D object modeling is presented. The proposed method is valid under weak perspective projection, does not require to search for correspondences between frames, can handle scaling between consecutive images. Thus can estimate the full Euclidean surface structure, and does not require camera calibration or camera motion measurement. Extensive experimental results show that the method is robust to the occlusion of feature points and image noise unlike previous affine-based approaches. Qualitative and quantitative results for the relation between the required minimum viewing angle change for the detection and the surface curvature are also presented.

关键词： Robustness Cameras Motion control Information processing Image segmentation Joining processes pattern matching Multidimensional systems pattern recognition Detection algorithms

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