in the context of object recognition from point cloud data, we present a thermodynamically-inspired graph theoretic algorithm to address the problem of matching the scene and the model point clouds, when the cardinali...
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ISBN:
(纸本)0769523722
in the context of object recognition from point cloud data, we present a thermodynamically-inspired graph theoretic algorithm to address the problem of matching the scene and the model point clouds, when the cardinalities of the two sets are orders of magnitude different. Such an approach determines a subset of points from the model that is structurally and spatially as similar as possible to the set of points in the scene. A new formulation for graph enthalpy characterizes the structural differences between point sets, which together with the existing notions of graph entropy quantifies the Gibbs' Free Energy. A two-scale approach is proposed, wherein, at the coarse scale, a set of points that comprise the model neighborhood around the scene is identified by minimization of entropy. At the fine scale, the desired correspondence is achieved by a refinement process, aimed at maximizing the Gibbs' Free Energy. The results demonstrate the robustness and efficiency of the approach.
Correlation-based real-time stereo systems have been proven to be effective in applications such as robot navigation, elevation map building etc. This paper provides an in-depth analysis of the major error sources for...
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ISBN:
(纸本)0780342364
Correlation-based real-time stereo systems have been proven to be effective in applications such as robot navigation, elevation map building etc. This paper provides an in-depth analysis of the major error sources for such a real-time stereo system in the context of cross-country navigation of an autonomous vehicle. Three major types of errors: foreshortening error, misalignment error and systematic error, are identified. The combined disparity errors can easily exceed three-tenths of a pixel, which translates to significant range errors. Upon understanding these error sources, we demonstrate different approaches to either correct them or model their magnitudes without excessive additional computations. By correcting those errors, we show that the precision of the stereo algorithm can be improved by 50%.
In this contribution we present an algorithm for tracking non-rigid, moving objects in a sequence of colored images, which were recorded by a non-stationary camera. The application background is vision-based driving a...
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ISBN:
(纸本)0780342364
In this contribution we present an algorithm for tracking non-rigid, moving objects in a sequence of colored images, which were recorded by a non-stationary camera. The application background is vision-based driving assistance in the inner city In an initial step, object parts are determined by a divisive clustering algorithm, which is applied to all pixels in the first image of the sequence. The feature space is defined by the color and position of a pixel. For each new image the clusters of the previous image are adapted iteratively by a parallel k-means clustering algorithm. Instead of tracking single points, edges, or areas over a sequence of images, only the centroids of the clusters are tracked. The proposed method remarkably simplifies the correspondence problem and also ensures a robust tracking behavior.
We present a surface radiance model for diffuse lighting that incorporates shadows, interreflections, and surface orientation. We show that, for smooth surfaces, the model is an excellent approximation of the radiosit...
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ISBN:
(纸本)0818672587
We present a surface radiance model for diffuse lighting that incorporates shadows, interreflections, and surface orientation. We show that, for smooth surfaces, the model is an excellent approximation of the radiosity equation. We present a new data structure and algorithm that uses this model to compute shape-from-shading under diffuse lighting. The algorithm was tested on both synthetic and real images, and performs more accurately than the only previous algorithm for this problem. Various causes of error are discussed, including approximation errors in image modelling, poor local constraints at the image boundary, and ill-conditioning of the problem itself.
\Conventional treatment of visual tracking has been to optimize an objective function in a probabilistic framework. In this formulation, efficient algorithms employing simple prior distributions are usually insufficie...
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ISBN:
(纸本)0769523722
\Conventional treatment of visual tracking has been to optimize an objective function in a probabilistic framework. In this formulation, efficient algorithms employing simple prior distributions are usually insufficient to handle clutters (e.g., Kalman filter). On the other hand, distributions that are complex enough to incorporate all a priori knowledge can make the problem computationally intractable (e.g., Particle filter (PF)). This paper proposes a new formulation of visual tracking where every piece of information, be it from a priori knowledge or observed data, is represented by a set in the solution space and the intersection of these sets, the feasibility set, represents all acceptable solutions. Based on this formulation, we propose an algorithm whose objective is to find a solution in the feasibility set. We show that this set theoretic tracking algorithm performs effective face tracking and is computationally more efficient than standard PF-based tracking.
This paper presents a prediction-and-verification segmentation scheme using attention images from multiple fixations. A major advantage of this scheme is that it can handle a large number of different deformable objec...
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ISBN:
(纸本)0818672587
This paper presents a prediction-and-verification segmentation scheme using attention images from multiple fixations. A major advantage of this scheme is that it can handle a large number of different deformable objects presented in complex backgrounds. The scheme is also relatively efficient since the segmentation is guided by the past knowledge through a prediction-and-verification scheme. The system has been tested to segment hands in the sequences of intensity images, where each sequence represents a hand sign. The experimental result showed a 95% correct segmentation rate with a 3% false rejection rate.
In this paper, we present a Bayesian framework for the fully automatic tracking of a variable number of interacting targets using a fixed camera. This framework uses a joint multi-object state-space formulation and a ...
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We present algorithms for coupling and training hidden Markov models CHMMsl to model interacting processes, and demonstrate their superiority to conventional HMMs in a vision task classifying two-handed actions. HMMs ...
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ISBN:
(纸本)0780342364
We present algorithms for coupling and training hidden Markov models CHMMsl to model interacting processes, and demonstrate their superiority to conventional HMMs in a vision task classifying two-handed actions. HMMs are perhaps the most successful framework in perceptual computing for modeling and classifying dynamic behaviors, popular because they offer dynamic time warping, a training algorithm, and a clear Bayesian semantics. However;the Markovian framework makes strong restrictive assumptions about the system generating the signal-that it is a single process having a smalt number of states and an extremely limited stare memory The single-process model is often inappropriate for vision (and speech) applications, resulting in low ceilings on model performance. Coupled HMMs provide an efficient way to resolve many of these problems, and offer superior training speeds, model likelihoods, and robustness to initial conditions.
Prior work has argued that when a Lambertian surface in fixed pose is observed in multiple images under varying distant illumination, there is an equivalence class of surfaces given by the generalized bas-relief (GBR)...
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We represent local spatial structure in a color image using feature matrices that are computed from an image region. Feature matrices contain significantly more information about local image structure than previous re...
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ISBN:
(纸本)0818672587
We represent local spatial structure in a color image using feature matrices that are computed from an image region. Feature matrices contain significantly more information about local image structure than previous representations. Although feature matrices are useful for surface recognition, this representation depends on the spectral properties of the scene illumination. Using a finite dimensional linear model for surface spectral reflectance with the same number of parameters as the number of color bands, we show that illumination changes correspond to linear transformations of the feature matrices and that surface rotations correspond to circular shifts of the matrices. From these relationships we derive an algorithm for illumination and geometry invariant recognition of local surface structure. We demonstrate the algorithm with a series of experiments on images of real objects.
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