In many application areas, there exists a crucial need for capturing 3D videos of fast moving and/or deforming objects. A 3D video is a sequence of 3D representations at high time and space resolution. Although many 3...
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In this paper we present a practical approach for 3D measurements in gamma-ray (or X-ray) cargo inspection. The linear pushbroom sensor model is used for such a gamma-ray scanning system. Thanks to the constraints of ...
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In this paper we describe a practical approach to processor selection for embedded computervision applications. This approach is based on expected production volumes and other requirements. We then present several po...
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In this paper we describe a practical approach to processor selection for embedded computervision applications. This approach is based on expected production volumes and other requirements. We then present several popular lowlevel computervision techniques that have been algorithmically optimized for implementation on an emerging class of embedded architectures, media processors.
Robust regression methods, such as RANSAC, suffer from a sensitivity to the scale parameter used for generating the inlier-outlier dichotomy. Projection based M-estimators (pbM) offer a solution to this by reframing t...
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Robust regression methods, such as RANSAC, suffer from a sensitivity to the scale parameter used for generating the inlier-outlier dichotomy. Projection based M-estimators (pbM) offer a solution to this by reframing the regression problem in a projection pursuit framework. In this paper we modify the pbM formulation to obtain an improved pbM algorithm. Furthermore, the modified algorithm is easily generalized to handle heteroscedastic data . The superior performance of heteroscedastic pbM, as compared to simple pbM, is experimentally verified.
Several recent efforts in multi-class feature-based object recognition employ shared features, or features that simultaneously belong to multiple class models. These approaches claim a considerable time savings by red...
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Several recent efforts in multi-class feature-based object recognition employ shared features, or features that simultaneously belong to multiple class models. These approaches claim a considerable time savings by reducing the total number of features used by all models, thereby lessening the concomitant computational effort of finding the features in images. In this paper we derive a Bayesian framework for predicting and evaluating the performance of shared feature-based recognition systems. We then use this framework to predict the performance of several instances of a simple multi-class object detector.
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