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.
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.
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.
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.
Surveillance system involving hundreds of cameras becomes very popular. Due to various positions and orientations of camera, object appearance changes dramatically in different scenes. Traditional appearance based obj...
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ISBN:
(纸本)9781424439942
Surveillance system involving hundreds of cameras becomes very popular. Due to various positions and orientations of camera, object appearance changes dramatically in different scenes. Traditional appearance based object classification methods tend to fail under these situations. We approach the problem by designing an adaptive object classification framevvork which automatically adjust to different scenes. Firstly, a baseline object classifier is applied to specific scene, generating training samples with extracted scene-specific features (such as object position). Based on that, bilateral weighted LDA is trained under the guide of sample confidence. Moreover we propose a bayesian classifier based method to detect and remove outliers to cope with contingent generalization disaster resulted from utilizing high confidence but incorrectly classified training samples. To validate these ideas, we realize the framework into an intelligent surveillance system. Experimental results demonstrate the effectiveness of this adaptive object classification framework.
Feature indexing techniques are promising for object recognition since they can quickly reduce the set of possible matches for a set of image features. This work exploits another property of such techniques. They have...
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ISBN:
(纸本)0818672587
Feature indexing techniques are promising for object recognition since they can quickly reduce the set of possible matches for a set of image features. This work exploits another property of such techniques. They have inherently parallel structure and connectionist network formulations are easy to develop. Once indexing has been performed, a voting scheme such as geometric hashing can be used to generate object hypotheses in parallel. We describe a framework for the connectionist implementation of such indexing and recognition techniques. With sufficient processing elements, recognition can be performed in a small number of time steps. The number of processing elements necessary to achieve peak performance and the fan-in/fan-out required for the processing elements is examined. These techniques have been simulated on a conventional architecture with good results.
Scene classification is a major open challenge in machine vision. Most solutions proposed so far such as those based on color histograms and local texture statistics cannot capture a scene's global configuration, ...
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ISBN:
(纸本)0780342364
Scene classification is a major open challenge in machine vision. Most solutions proposed so far such as those based on color histograms and local texture statistics cannot capture a scene's global configuration, which is critical in perceptual judgments of scene similarity. We present a novel approach, ''configural recognition'', for encoding scene class structure. The approach's main feature is its use of qualitative spatial and photometric relationships within and across regions in low resolution images. The emphasis on qualitative measures leads to enhanced generalization abilities and the use of low-resolution images renders the scheme computationally efficient. We present results on a large database of natural scenes. We also describe how qualitative scene concepts may be learned from examples.
During a fixed axis camera rotation every image point is moving on a conic section. If the point is a vanishing point the conic section is invariant to possible translations of the observer. Given the rotation axis an...
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ISBN:
(纸本)0818672587
During a fixed axis camera rotation every image point is moving on a conic section. If the point is a vanishing point the conic section is invariant to possible translations of the observer. Given the rotation axis and the inter-frame correspondence of a set of parallel lines we are able to compute the intrinsic parameters without knowledge of the rotation angles. We propagate the error covariances and we remove the bias in the computation of the conic. We experimentally study the sensitivity of calibration to the amount of rotation and we compare our performance to the performance of a recent active calibration technique.
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