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Model-Driven Video-Based Rendering for Vehicles

Model-Driven Video-Based Rendering for Vehicles

引用

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

作者： I.O. Sebe Suya You U. Neumann Computer Graphics and Immersive Technologies Laboratory Los Angeles CA USA

We present a video-based rendering (VBR) method that creates arbitrary views of the scene from images captured by a single un-calibrated camera. The system is model-based and focused on a single object class, specifically vehicles in this paper. The procedure has three main steps. First, the object is rapidly modeled from a single image using a simple interactive tool. The modeling tool creates high quality 3D models by facilitating constrained modifications to a generic model of the object class. Secondly, the dynamic object is tracked in the video sequence and the pose of the object is recorded. The tracking and pose estimation leverage the 3D model to ensure high accuracy. Lastly, the surrounding scene is modeled as a textured piece-wise planar approximation. A free-viewpoint rendering of the video (4D video) is then produced as needed for special effects or virtual reality applications. The versatility and effectiveness of our approach to modeling and rendering is demonstrated on the object class of vehicles.

关键词： Vehicles Rendering (computer graphics) Cameras Laser modes Mice Hardware Buildings computer graphics Laboratories computer vision

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Particle DynamicsWarping Approach for Offline Signature recognition

Particle DynamicsWarping Approach for Offline Signature Reco...

引用

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

作者： G. Agam S. Suresh Department of Computer Science Illinois Institute of Technology Chicago IL USA

Offline signature recognition is an important form of biometric identification that can be used for various purposes. Similar to other biometric measures, signatures have inherent variability and so pose a difficult recognition problem. In this paper we explore a novel approach for reducing the variability associated with matching signatures based on curve warping. Existing techniques, such as the dynamic time warping approach, address this problem by minimizing a cost function through dynamic programming. This is by nature a one dimensional optimization process that is possible when a one dimensional parametrization of the curves is known. In this paper we propose a novel approach for solving the curve correspondence problem that is not limited by the requirement of one dimensional parametrization. The proposed approach utilizes particle dynamics and minimizes a cost function through an iterative solution of a system of first order ordinary differential equations. The proposed approach is therefore capable of handling complex curves for which a simple parametrization is not available. The proposed approach is evaluated by measuring the precision and recall rates of documents based on signature similarity. To facilitate a realistic evaluation, the signature data we use was collected from real world documents spanning a period of several decades.

关键词： Biometrics Dynamic programming Cost function Indexing Training data pattern recognition computer science Iterative methods Differential equations Credit cards

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Combining Regions and Patches for Object Class Localization

Combining Regions and Patches for Object Class Localization

引用

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

作者： C. Pantofaru G. Dorko C. Schmid M. Hebert The Robotics Institute CMU USA TU Darmstadt Germany INRIA Rhone-Alpes France

We introduce a method for object class detection and localization which combines regions generated by image segmentation with local patches. Region-based descriptors can model and match regular textures reliably, but fail on parts of the object which are textureless. They also cannot repeatably identify interest points on their boundaries. By incorporating information from patch-based descriptors near the regions into a new feature, the Region-based Context Feature (RCF), we can address these issues. We apply Region-based Context Features in a semi-supervised learning framework for object detection and localization. This framework produces object-background segmentation masks of deformable objects. Numerical results are presented for pixel-level performance.

关键词： Object detection Shape Labeling Image segmentation Smart pixels Intelligent robots Image generation Semisupervised learning computer vision Layout

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Tracking of the Articulated Upper Body on Multi-View Stereo Image Sequences

Tracking of the Articulated Upper Body on Multi-View Stereo ...

引用

conference on computer vision and pattern recognition (CVPR)

作者： J. Ziegler Kai Nickel R. Stiefelhagen Interactive Systems Laboratories Universität Karlsruhe Germany

We propose a novel method for tracking an articulated model in a 3D-point cloud. The tracking problem is formulated as the registration of two point sets, one of them parameterised by the model’s state vector and the other acquired from a 3D-sensor system. Finding the correct parameter vector is posed as a linear estimation problem, which is solved by means of a scaled unscented Kalman filter. Our method draws on concepts from the widely used iterative closest point registration algorithm (ICP), basing the measurement model on point correspondences established between the synthesised model point cloud and the measured 3D-data. We apply the algorithm to kinematically track a model of the human upper body on a point cloud obtained through stereo image processing from one or more stereo cameras. We determine torso position and orientation as well as joint angles of shoulders and elbows. The algorithm has been successfully tested on thousands of frames of real image data. Challenging sequences of several minutes length where tracked correctly. Complete processing time remains below one second per frame.

关键词： Image sequences Clouds Biological system modeling Vectors Iterative algorithms Iterative methods Iterative closest point algorithm Humans Stereo vision Cameras

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Semi-Supervised Classification Using Linear Neighborhood Propagation

Semi-Supervised Classification Using Linear Neighborhood Pro...

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conference on computer vision and pattern recognition (CVPR)

作者： Fei Wang Changshui Zhang H.C. Shen Jingdong Wang State Key Laboratory of Intelligent Technology and Systems Department of Automation Tsinghua University Beijing China Department of Computer Science and Engineering and Digital Technology Center Hong Kong University of Science and Technology Hong Kong China

In this paper, we address the general problem of learning from both labeled and unlabeled data. Based on the reasonable assumption that the label of each data can be linearly reconstructed from its neighbors’ labels, we develop a novel approach, called Linear Neighborhood Propagation (LNP), to learn the linear construction weights and predict the labels of the unlabeled data from the labeled data. The main procedure is composed of two steps: (1) it computes the local neighborhood geometry according to the image information by solving a constrained least squares problem, (2) the obtained geometry will be used to predict the labels of unlabeled data by solving a linearly constrained quadratic optimization problem. LNP has at least three characteristics. Firstly, it is much practical in that only one free parameter, the size of neighborhood, requires user-tuning. Secondly it can be easily extended to out-of-sample examples. Finally the labels achieved from LNP can be sufficiently smooth with respect to the intrinsic structure collectively revealed by the labeled and unlabeled points. Many promising experimental results, such as object recognition, data ranking and interactive image segmentation, demonstrate the effectiveness and efficiency of the proposed approach.

关键词： Image reconstruction Computational geometry Information geometry Constraint optimization Image segmentation Semisupervised learning Laboratories Intelligent systems Automation computer science

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Multiresolution Approach for Non-Contact Measurements of Arterial Pulse using Thermal Imaging

Multiresolution Approach for Non-Contact Measurements of Art...

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

作者： S.Y. Chekmenev A.A. Farag E.A. Essock Computer Vision and Image Processing Laboratory University of Louisville Louisville KY USA Department of Psychological and Brain University of Louisville Louisville KY USA

This paper presents a novel, multiresolution (MR) approach for non-contact passive measurements of the arterial pulse along the arterial tree using thermal imaging of the most accessible regions of human face and neck. The method automatically detects Region/s Of Measurement (ROM) of the arterial pulse using the periodic nature of arterial thermal patterns. This ROM constitutes a skin area where the heat pulsation produced by an artery is most pronounced. The output of the algorithm is a descriptive arterial pulse waveform. To the best of our knowledge, our results are novel and have not been reported before. Some potential applications of this work are in the field of non-contact health state/ physiology monitoring, magnetic resonance imaging, smart rooms, polygraph testing, and intent identification.

关键词： Pulse measurements Image resolution Read only memory Humans Face detection Neck Skin Arteries Physiology Monitoring

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Bilayer Segmentation of Live Video

Bilayer Segmentation of Live Video

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conference on computer vision and pattern recognition (CVPR)

作者： A. Criminisi G. Cross A. Blake V. Kolmogorov Microsoft Research Limited Cambridge UK

This paper presents an algorithm capable of real-time separation of foreground from background in monocular video sequences. Automatic segmentation of layers from colour/contrast or from motion alone is known to be error-prone. Here motion, colour and contrast cues are probabilistically fused together with spatial and temporal priors to infer layers accurately and efficiently. Central to our algorithm is the fact that pixel velocities are not needed, thus removing the need for optical flow estimation, with its tendency to error and computational expense. Instead, an efficient motion vs nonmotion classifier is trained to operate directly and jointly on intensity-change and contrast. Its output is then fused with colour information. The prior on segmentation is represented by a second order, temporal, Hidden Markov Model, together with a spatial MRF favouring coherence except where contrast is high. Finally, accurate layer segmentation and explicit occlusion detection are efficiently achieved by binary graph cut. The segmentation accuracy of the proposed algorithm is quantitatively evaluated with respect to existing groundtruth data and found to be comparable to the accuracy of a state of the art stereo segmentation algorithm. Foreground/ background segmentation is demonstrated in the application of live background substitution and shown to generate convincingly good quality composite video.

关键词： Image segmentation Image motion analysis Stereo vision Optical computing Data mining Application software Computational efficiency Cameras Image sequences Video sequences

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Cinematized Reality: Cinematographic 3D Video System for Daily Life Using Multiple Outer/Inner Cameras

Cinematized Reality: Cinematographic 3D Video System for Dai...

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

作者： Hansung Kim R. Sakamoto K. Kogure I. Kitahara Media Information Science Laboratory ATR Keihanna Science City Kyoto Japan Department of Intelligent Interaction Technologies University of Tsukuba Tsukuba Ibaraki Japan

The purpose of Cinematized Reality is to record unexpected moments in people’s lives and create movies that look as if they were created from real, expertly captured film footage. The approach toward Cinematized Reality is to generate free-view video streams from multiple videos. The proposed system reconstructs 3D models of the capturing space using outer environmental cameras and an inner omni-directional camera, and then generates realistic video of those objects from a designated viewpoint by controlling a virtual camera as an expert would. This prototype system follows the grammar of film language and applies expert knowledge to generate desirable film footage using a sequence of shots taken by the virtual camera.

关键词： Calibration Videoconference Network servers Cities and towns Layout Instruments Shape measurement Smart cameras Information science computer vision

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Object Pose Detection in Range Scan Data

Object Pose Detection in Range Scan Data

引用

conference on computer vision and pattern recognition (CVPR)

作者： J. Rodgers D. Anguelov Hoi-Cheung Pang D. Koller Computer Science Department University of Stanford CA USA

We address the problem of detecting complex articulated objects and their pose in 3D range scan data. This task is very difficult when the orientation of the object is unknown, and occlusion and clutter are present in the scene. To address the problem, we design an efficient probabilistic framework, based on the articulated model of an object, which combines multiple information sources. Our framework enforces that the surfaces and edge discontinuities of model parts are matched well in the scene while respecting the rules of occlusion, that joint constraints and angles are maintained, and that object parts don’t intersect. Our approach starts by using low-level detectors to suggest part placement hypotheses. In a hypothesis enrichment phase, these original hypotheses are used to generate likely placement suggestions for their neighboring parts. The probabilities over the possible part placement configurations are computed using efficient OpenGL rendering. Loopy belief propagation is used to optimize the resulting Markov network to obtain the most likely object configuration, which is additionally refined using an Iterative Closest Point algorithm adapted for articulated models. Our model is tested on several datasets, where we demonstrate successful pose detection for models consisting of 15 parts or more, even when the object is seen from different viewpoints, and various occluding objects and clutter are present in the scene.

关键词： Object detection Layout Markov random fields Detectors Belief propagation Object recognition computer science Iterative closest point algorithm Testing Humans

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A Camera-Projector System for Robot Positioning by Visual Servoing

A Camera-Projector System for Robot Positioning by Visual Se...

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

作者： J. Pages C. Collewet F. Chaumette J. Salvi Institut d'Informàtica i Aplicacions University of Girona Girona Spain IRISA INRIA-Rennes Rennes France IRISA INRIA-Rennes Institut d'Informàtica i Aplicacions

Positioning a robot with respect to objects by using data provided by a camera is a well known technique called visual servoing. In order to perform a task, the object must exhibit visual features which can be extracted from different points of view. Then, visual servoing is object-dependent as it depends on the object appearance. Therefore, performing the positioning task is not possible in presence of nontextured objets or objets for which extracting visual features is too complex or too costly. This paper proposes a solution to tackle this limitation inherent to the current visual servoing techniques. Our proposal is based on the coded structured light approach as a reliable and fast way to solve the correspondence problem. In this case, a coded light pattern is projected providing robust visual features independently of the object appearance.

关键词： Visual servoing Feature extraction Robot sensing systems Robot vision systems Cameras Data mining Robustness computer vision Proposals Robot control

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