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Real-time foreground segmentation via range and color imaging

Real-time foreground segmentation via range and color imagin...

引用

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

作者： Ryan Crabb Colin Tracey Akshaya Puranik James Davis University of California Santa Cruz Santa Cruz CA USA Canesta Inc. Sunnyvale CA USA

This paper describes a real-time method for foreground/background segmentation of a color video sequence based primarily on range data of a time-of-flight sensor. This method uses depth information of a TOF-sensor paired with a high resolution color video camera to efficiently segment foreground from background in a two-step process. First a trimap is produced using only range data: areas are located in each frame that have a high probability of being background or foreground, respectively. Pixels which cannot be definitively classified as foreground or background, typically about 1-2% of the frame, are assigned alpha-matte values using a cross bilateral filtering, applied directly to an estimate of the alpha-matte.

关键词： Image segmentation Color Cameras Layout TV Production Stereo vision Video sequences Sensor phenomena and characterization Filtering

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3D tracking in unknown environments using on-line keypoint learning for mobile augmented reality

3D tracking in unknown environments using on-line keypoint l...

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

作者： Gerhard Schall Helmut Grabner Michael Grabner Paul Wohlhart Dieter Schmalstieg Horst Bischof Institute for Computer Graphics and Vision Graz University of Technology Austria

In this paper we present a natural feature tracking algorithm based on on-line boosting used for localizing a mobile computer. Mobile augmented reality requires highly accurate and fast six degrees of freedom tracking in order to provide registered graphical overlays to a mobile user. With advances in mobile computer hardware, vision-based tracking approaches have the potential to provide efficient solutions that are non-invasive in contrast to the currently dominating marker-based approaches. We propose to use a tracking approach which can use in an unknown environment, i.e. the target has not be known beforehand. The core of the tracker is an on-line learning algorithm, which updates the tracker as new data becomes available. This is suitable in many mobile augmented reality applications. We demonstrate the applicability of our approach on tasks where the target objects are not known beforehand, i.e. interactive planing.

关键词： Augmented reality Target tracking Mobile computing Boosting Planing Robustness Data visualization computer graphics computer vision Handheld computers

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Realtime phase-based optical flow on the GPU

Realtime phase-based optical flow on the GPU

引用

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

作者： Karl Pauwels Marc M. Van Hulle Laboratorium voor Neuro en Psychofysiologie Katholieke Universiteit Leuven Belgium

Phase-based optical flow algorithms are characterized by high precision and robustness, but also by high computational requirements. Using the CUDA platform, we have implemented a phase-based algorithm that maps exceptionally well on the GPUpsilas architecture. This optical flow algorithm revolves around a reliability measure that evaluates the consistency of phase information over time. By exploiting efficient filtering operations, the high internal bandwidth of the GPU, and the texture units, we obtain dense and reliable optical flow estimates in realtime at high resolutions (640 times 512 pixels and beyond). Even though the algorithm is local and does not involve iterative regularization, highly accurate results are obtained on synthetic and complex real-world sequences.

关键词： Image motion analysis Optical filters Robustness Optical computing computer architecture Fluid flow measurement Phase measurement Time measurement Filtering Bandwidth

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Face pose estimation and tracking using automatic 3D model construction

Face pose estimation and tracking using automatic 3D model c...

引用

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

作者： Pedro Jimenez Jesus Nuevo Luis M. Bergasa Department of Electronics University of Alcala Alcala de Henares Spain

This paper presents a method for robustly tracking and estimating the face pose of a person in both indoor and outdoor environments. The method is invariant to identity and that does not require previous training. A face model is automatically initialized and constructed on-line, when the face is frontal to the stereo camera system. To build the model, a fixed point distribution is superposed over the frontal face, and several appropriate points close to those locations are chosen for tracking. Using the stereo correspondence of the two cameras, the 3D coordinates of these points are extracted, and the 3D model is created. RANSAC and POSIT are used for tracking and 3D pose calculation at each frame. The approach runs in real time, and has been tested on sequences recorded in the laboratory and in a moving car.

关键词： Face detection Cameras Testing Eyes Robustness Support vector machines computer vision Change detection algorithms Application software Stereo vision

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Visual detection of lintel-occluded doors from a single image

Visual detection of lintel-occluded doors from a single imag...

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

作者： Zhichao Chen Stanley T. Birchfield Electrical and Computer Engineering Department Clemson University Clemson SC USA

Doors are important landmarks for indoor mobile robot navigation. Most existing algorithms for door detection use range sensors or work in limited environments because of restricted assumptions about color, pose, or lighting. We present a vision-based door detection algorithm that achieves robustness by utilizing a variety of features, including color, texture, and intensity edges. We introduce two novel geometric features that increase performance significantly: concavity and bottom-edge intensity profile. The features are combined using Adaboost to ensure optimal linear weighting. On a large database of images collected in a wide variety of conditions, the algorithm achieves more than 90% detection with a low false positive rate. Additional experiments demonstrate the suitability of the algorithm for real-time applications using a mobile robot equipped with an off-the-shelf camera and laptop.

关键词： Mobile robots Navigation Detection algorithms Robustness Image edge detection Image databases Spatial databases Robot vision systems Cameras Portable computers

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Towards understanding what makes 3D objects appear simple or complex

Towards understanding what makes 3D objects appear simple or...

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

作者： Sreenivas R. Sukumar David L. Page Andreas F. Koschan Mongi A. Abidi Imaging Robotics and Intelligent Systems Laboratory University of Tennessee Knoxville USA

Humans perceive some objects more complex than others and learning or describing a particular object is directly related to the judged complexity. Towards the goal of understanding why the geometry of some 3D objects appear more complex than others, we conducted a psychophysical study and identified contributing attributes. Our experiments conclude that surface variation, symmetry, part count, simpler part decomposability, intricate details and topology are six significant dimensions that influence 3D visual shape complexity. With that knowledge, we present a method of quantifying complexity and show that the informational aspect of Shannonpsilas theory agrees with the human notion of shape complexity.

关键词： Humans computer vision Shape measurement Layout Size measurement Intelligent robots Image analysis Surface morphology Intelligent systems computer aided engineering

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Dense linear-time correspondences for tracking

Dense linear-time correspondences for tracking

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

作者： Stepan Obdrzalek Michal Perd'och Jiri Matas Center for Machine Perception Czech Technical University Prague Czech Republic Center for Machine Perception Czech Technical University Prague

A novel method is proposed for the problem of frame-to-frame correspondence search in video sequences. The method, based on hashing of low-dimensional image descriptors, establishes dense correspondences and allows large motions. All image pixels are considered for matching, the notion of interest points is reviewed. In our formulation, points of interest are those that can be reliably matched. Their saliency depends on properties of the chosen matching function and on actual image content. Both computational time and memory requirements of the correspondence search are asymptotically linear in the number of image pixels, irrespective of correspondence density and of image content. All steps of the method are simple and allow for a hardware implementation. Functionality is demonstrated on sequences taken from a vehicle moving in an urban environment.

关键词： Pixel Object detection Cameras Geometry Remotely operated vehicles Video compression computer vision Mobile robots Simultaneous localization and mapping Motion detection

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Fast and exact solution of Total Variation models on the GPU

Fast and exact solution of Total Variation models on the GPU

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

作者： Thomas Pock Markus Unger Daniel Cremers Horst Bischof Institute for Computer Graphics and Vision Graz University of Technology Austria Department of Computer Science University of Bonn Germany

This paper discusses fast and accurate methods to solve total variation (TV) models on the graphics processing unit (GPU). We review two prominent models incorporating TV regularization and present different algorithms to solve these models. We mainly concentrate on variational techniques, i.e. algorithms which aim at solving the Euler Lagrange equations associated with the variational model. We then show that particularly these algorithms can be effectively accelerated by implementing them on parallel architectures such as GPUs. For comparison we chose a state-of-the-art method based on discrete optimization techniques. We then present the results of a rigorous performance evaluation including 2D and 3D problems. As a main result we show that the our GPU based algorithms clearly outperform discrete optimization techniques in both speed and maximum problem size.

关键词： computer vision computer graphics TV Lagrangian functions Acceleration Optical computing Iterative algorithms Image denoising Equations Parallel architectures

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Camera localization and building reconstruction from single monocular images

Camera localization and building reconstruction from single ...

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

作者： Ruisheng Wang Frank P. Ferrie Centre for Intelligent Machines McGill University Montreal QUE Canada

This paper presents a new method for reconstructing rectilinear buildings from single images under the assumption of flat terrain. An intuition of the method is that, given an image composed of rectilinear buildings, the 3D buildings can be geometrically reconstructed by using the image only. The recovery algorithm is formulated in terms of two objective functions which are based on the equivalence between the vector normal to the interpretation plane in the image space and the vector normal to the rotated interpretation plane in the object space. These objective functions are minimized with respect to the camera pose, the building dimensions, locations and orientations to obtain estimates for the structure of the scene. The method potentially provides a solution for large-scale urban modelling using aerial images, and can be easily extended to deal with piecewise planar objects in a more general situation.

关键词： Image reconstruction Layout Image edge detection Large-scale systems Urban planning Iterative algorithms Smart cameras Buildings computer vision Detection algorithms

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Anytimeness avoids parameters in detecting closed convex polygons

Anytimeness avoids parameters in detecting closed convex pol...

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

作者： Michael Zillich Markus Vincze Institute of Automation and Control University of Technology Vienna Austria

Many perceptual grouping algorithms depend on parameters one way or another. It is always difficult to set these parameters appropriately for a wide range of input images, and parameters tend to be tuned to a small set of test cases. Especially certain thresholds often seem unavoidable to limit search spaces in order to obtain reasonable runtime complexity. Furthermore early pruning of less salient hypotheses is often necessary to avoid exponential growth of the number of hypotheses. In the presented work we show how the adoption of a simple anytime algorithm, i. e. an algorithm which returns the best answer possible when interrupted and may improve on the answer if allowed to run longer, for finding closed convex polygons eliminates the need for parameter tuning. Furthermore it quite naturally allows the incorporation of attentional mechanisms into the grouping process.

关键词： Runtime Image segmentation Costs Turning Image edge detection Automation Automatic control Testing computer vision Jacobian matrices

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