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检索条件"任意字段=2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2013"

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A maximum likelihood N-camera stereo algorithm

A maximum likelihood N-camera stereo algorithm

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Cox NEC Research Institute Inc. Princeton NJ USA

This paper extends results of a maximum likelihood two-frame stereo algorithm to the case of N cameras. The N-camera stereo algorithm determines the "best" set of correspondences between a given pair of cameras, referred to as the principal cameras. Knowledge of the relative positions of the cameras allows the 3D point hypothesized by an assumed correspondence of two features in the principal pair to be projected onto the image plane of the remaining N-2 cameras. These N-2 points are then used to verify proposed matches. Not only does the algorithm explicitly model occlusion between features of the principal pair, but the possibility of occlusions in the N-2 additional views is also modelled. The benefits and importance of this are experimentally verified. Like other multi-frame stereo algorithms, the computational and memory costs of this approach increase linearly with each additional view. Experimental results are shown for two outdoor scenes. It is clearly demonstrated that the number of correspondence errors is significantly reduced as the number of views/cameras is increased.< >

关键词： Machine vision Cameras Maximum-likelihood estimation

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Active part-decomposition, shape and motion estimation of articulated objects: a physics-based approach

Active part-decomposition, shape and motion estimation of ar...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Kakadiaris Metaxas Bajcsy GRASP Laboratory Department of Computer and Information Science University of Pennsylvania Philadelphia PA USA

We present a novel, robust, integrated approach to segmentation shape and motion estimation of articulated objects. Initially, we assume the object consists of a single part, and we fit a deformable model to the given data using our physics-based framework. As the object attains new postures, we decide based on certain criteria if and when to replace the initial model with two new models. These criteria are based on the model's state and the given data. We then fit the models to the data using a novel algorithm for assigning forces from the data to the two models, which allows partial overlap between them and determination of joint location. This approach is applied iteratively until all the object's moving parts are identified. Furthermore, we define new global deformations and we demonstrate our technique in a series of experiments, where Kalman filtering is employed to account for noise and occlusion.< >

关键词： Image motion analysis Image segmentation

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Autonomous exploration: driven by uncertainty

Autonomous exploration: driven by uncertainty

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Whaite Ferrie Centre for Intelligent Machines McGill University Montreal QUE Canada

Passively accepting measurements of the world is not enough, as the data we obtain is always incomplete, and the inferences made from it are uncertain to a degree which is often unacceptable. If we are to build machines that operate autonomously they will always be faced with this dilemma, and can only be successful if they play a much more active role. This paper presents such a machine. It deliberately seeks out those parts of the world which maximize the fidelity of its internal representations, and keeps searching until those representations are acceptable. We call this paradigm autonomous exploration, and the machine an a autonomous explorer. This paper has two major contributions. The first is a theory that tells us how to explore, and which confirms the intuitive ideas we have put forward previously. The second is an implementation of that theory. The system is entirely bottom-up and does not depend on a priori knowledge of the environment. To our knowledge it is the first to have successfully closed the loop between gaze planning and the inference of complex 3D models.< >

关键词： Inference mechanisms Machine vision

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Controlled active exploration of uncalibrated environments

Controlled active exploration of uncalibrated environments

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Smith Brandt Papanikolopoulos Artificial Intelligence Robotics and Vision Laboratory Department of Minnesota University of Minnesota Minneapolis MN USA

Flexible operation of a robotic agent in an uncalibrated environment requires the ability to recover unknown or partially known parameters of the workspace through sensing. Of the sensors available to a robotic agent, visual sensors provide information that is richer and more complete than other sensors. In this paper we present robust techniques for the derivation of depth from feature points on a target's surface and for the accurate and high-speed tracking of moving targets. We use these techniques in a system that operates with little or no a priori knowledge of the object- and camera-related parameters to robustly determine such object-related parameters as velocity and depth. Such determination of extrinsic environmental parameters is essential for performing higher level tasks such as inspection, exploration, tracking, grasping, and collision-free motion planning. For both applications, we use the Minnesota Robotic Visual Tracker (a single visual sensor mounted on the end-effector of a robotic manipulator combined with a real-time vision system) to automatically select feature points on surfaces, to derive an estimate of the environmental parameter in question, and to supply a control vector based upon these estimates to guide the manipulator.< >

关键词： Mobile robots, motion planning Robots, vision systems

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Surface curvature from integrability

Surface curvature from integrability

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Fan Wolff Computer Vision Laboratory/Department of Computer Science Johns Hopkins University Baltimore MD USA

We present a multiple illumination technique that directly recovers the viewer-centered curvature matrix up to a scalar factor, at each mutually illuminated point on a smooth object surface. This technique is completely independent of knowledge of incident illumination orientation, local surface orientation, or diffuse surface albedo. The cornerstone of this technique is the use of the integrability constraint which is a fundamental mathematical property of smooth surfaces. The integrability constraint allows the derivation of an equation at each object point, which is linear in terms of quantities involving the initially unknown parameters of incident illumination orientation. These quantities, which we call the gradient ratio constants, can be simultaneously solved for from five or more equations arising from the same number of object points. We show that deriving these gradient ratio constants provides just enough calibration information about incident illumination geometry to compute the viewer-centered curvature matrix at each object point, up to a scalar multiple. We demonstrate two important applications of this technique: segmentation of the object surface by sign of Gaussian curvature, and further segmentation of non-negative Gaussian curvature into convexity and concavity.< >

关键词： Machine vision Image segmentation Lighting Computational geometry

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The extruded generalized cylinder: a deformable model for object recovery

The extruded generalized cylinder: a deformable model for ob...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： O'Donnell Boult Xi-Sheng Fang Gupta Siemens Corporate Research Inc. Princeton NJ USA Department of Computer Science Columbia University New York NY USA

There is increasing interest in the recovery of generalized cylinders (GCs) with curved spines. However, existing formulations of such GCs, for example those based an the Frenet-Serret frame or the tube model, suffer serious drawbacks: discontinuities, a lack of expressive power, "narrowing" in the plane normal to the spine, non-intuitive twisting behavior, and/or off-axis nonorthogonality of their local coordinate systems. We discuss some of the problems associated with the non-orthogonality of the coordinate system based on the Frenet-Serret frame. This non-orthogonality is induced by torsion effects and we show how to correct for it. We then introduce a new model, the extruded GC (EGC) model, which overcomes all the problems mentioned above. For complex axes, the EGC model is also simpler to understand and use than existing models. The EGC model is further extended by including local surface deformations. Recovery of the deformable EGC via a physically-motivated paradigm is demonstrated on pre-segmented data from a human carotid artery.< >

关键词： Machine vision Object recognition Computational geometry

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2D matching of 3D moving objects in color outdoor scenes

2D matching of 3D moving objects in color outdoor scenes

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Dubuisson Jain Department of Computer Science Michigan State University East Lansing MI USA

This paper describes an object matching system which is able to extract objects of interest from outdoor scenes and match them. Our application (in the domain of IVHS) involves measuring the average travel time in a road network. The extraction of the object of interest is performed by fusing multiple cues including motion, color, edges, and model information. Two objects extracted from images captured by two independent cameras at different times are then matched to evaluate their similarity. Color indexing based on histogram matching is used to avoid matching all possible pairs of objects. To resolve ambiguities, further matching is done by measuring the Hausdorff distance between two sets of edge points. The object matching system was given 2 sets of 40 vehicles. It was able to identify 23 of the 30 correct matches and all the false matches were rejected. Color indexing reduced the number of candidates for a match from 40 to 2. This matching accuracy is adequate to obtain a reliable estimate of the average travel time.< >

关键词： Image matching Image motion analysis Image color analysis Road transportation, automated highways Object recognition

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Representation and computation of the spatial environment for indoor navigation

Representation and computation of the spatial environment fo...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Dongsung Kim Ramakant Nevatia Institute for Robotics and Intelligent Systems University of Southern California Los Angeles CA USA

ISBN: (纸本)0818658258

We introduce a spatial representation, s-map, for an indoor navigation robot. The s-map represents the locations of obstacles in a planar domain, where obstacles are defined as any objects that can block movement of the robot. In building the s-map, the viewing triangle constraint and the stability constraint are introduced for efficient verification of vertical surfaces. These verified vertical surfaces and 3-D segments of obstacles smaller than a robot, are mapped to the s-map by simply dropping height information. Thus, the s-map is made directly from 3-D segments with simple verification, and represents obstacles in a planar domain so that it becomes a navigable map for the robot without further processing. In addition to efficient map building, the s-map represents the environment more realistically and completely. Furthermore, the s-map converts many navigation problems in 3-D, such as map fusion and path planning, into 2-D ones. We present the analysis of the s-map in terms of complexity and reliability, and discuss its pros and cons. Moreover, we show the results of the s-maps for indoor environments.< >

关键词： Mobile robots, motion planning Machine vision Data structures

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Segmentation of surface curvature using a photometric invariant

Segmentation of surface curvature using a photometric invari...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Wolff Fan Computer Vision Laboratory Department of Computer Science Johns Hopkins University Baltimore MD USA

Gaussian curvature is an intrinsic local shape characteristic of a smooth object surface that is invariant to orientation of the object in 3-D space and viewpoint. Accurate determination of the sign of Gaussian curvature at each point on a smooth object surface (i.e., the identification of hyperbolic, elliptical and parabolic points) can provide very important information for both recognition of objects in automated vision tasks and manipulation of objects by a robot. We present a multiple illumination technique that directly identifies elliptical, hyperbolic, and parabolic points from diffuse reflection from a smooth object surface. This technique is based upon a photometric invariant involving the behavior of the image intensity gradient under varying illumination. The nature of this photometric invariant allows direct segmentation of a smooth object surface according to the sign of Gaussian curvature independent of knowledge of local surface orientation, independent of diffuse surface albedo, and with only approximate knowledge of the geometry of multiple incident illumination. In comparison with photometric stereo, this new technique determines the sign of Gaussian curvature directly from image features without having to derive local surface orientation, is invariant to incident orientation errors of two of three light sources, and is invariant to the relative strength of incident radiance with respect to each of these light sources. We demonstrate how this segmentation technique works under conditions of simulated image noise, and actual experimental imaging results.< >

关键词： Image segmentation Machine vision Stereo vision

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Efficient indexing techniques for model based sensing

Efficient indexing techniques for model based sensing

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Wallack Canny Computer Science Division University of California Berkeley CA USA

Indexing is a model-based recognition technique, in which unknown objects are identified using lookup tables. Indexing coordinates are extracted from sensed features, and the indexing coordinates specify a table entry containing the object's identity. Usually, only a small fraction of the possible indexing coordinates correspond to modeled objects, and hash tables are often used to save space. In this paper, we present a new indexing data structure called a tree grid which has two advantages over hash tables. (i) The tree grid preserves spatial ordering, so that nearby indexing entries can be retrieved efficiently (ii) The tree grid compacts the storage size of the table by a factor of as much as two orders of magnitude. k coordinates index an ordering of the interpretations, and 1 coordinate determines the consistent interpretations for objects with k degrees of freedom. We also show that for almost all model sets, 2k+1 indexing coordinates care sufficient to discriminate between two generic models, implying that 2k+1 indexing coordinates specify a unique in interpretation. We have implemented an indexing algorithm for recognizing 3D objects from pairs of image rays using the tree grid technique, and the results are reported.< >

关键词： computer instructions Object recognition Data structures

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