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Determining optical flow has been a wide field of research for more than 20 years now that has not been solved satisfactorily yet. In this work, we study the influence of a nonlinear smoothing process based on bilateral filtering on a Lucas & Kanade framework for the estimation of optical flow between two image frames. Different confidence measures are used to improve the computation process and...
In absence of a truly reliable confidence measure for optical flow, this paper points out a few methods, how optical flow estimation results can be tested for reliability and accuracy. We are especially interested in how to make use of complementary information in the visible and infrared spectrum in a multi-sensor environment.
The application of zoom camera lenses in machine vision has gained a lot of attention lately. The main difficulty in their employment lies in the accurate estimation of their intrinsic parameters. In this paper, we propose novel approaches to determine these parameters by estimating continuous models of their variations as the focus and the zoom change. The first method is based on the moving least...
Depth from stereo is an important research field in computer vision due to the wide range of its applications. In this work, we present a stereo matching algorithm based on belief propagation (BP). The algorithm is designed to work on sparse images originating from image content adaptive mesh representation techniques. There, an image is approximated with a mesh. The nodes of the mesh are the non-uniform...
Content adaptive mesh generation is an important research area with many applications in image processing and computer vision. The main issue is to represent an image with the pixels that preserve most of the amount of its information. The obtained pixels are then used to generate a mesh that approximates the original image. This work presents a novel iterative method that simultaneously reduces the...
Meshing the 3D scattered point data is an important task in machine vision since it allows the rendering of the 3D structures on a commodity graphics hardware. In this paper, we develop a fast and adaptive method to approximate an organized set of 3D scattered data points from stereo images by a triangular mesh and which can be applied to systems that require low delay response such as telepresence...
The accuracy of machine vision systems is highly dependent on the correct estimates of the camera intrinsic parameters. This precision is needed in numerous applications like telepresence and robot navigation. In this work, a new technique is proposed, based on the moving least-squares (MLS) approach, to model the intrinsic parameters of an automatic zoom lens camera system. The key issue is to generate...
The accuracy of machine vision systems is highly depending on the correct estimates of the camera intrinsic parameters. This accuracy is needed in numerous applications like telepresence and robot navigation. In this work, a novel technique is proposed based on the moving least-squares approach, to model the variation of the camera internal parameters as a function of focus and zoom. Compared to a...
Extracting the motion parameters of a moving camera is an important issue in computer vision. This is due to the need of numerous emerging applications like telepresence and robot navigation. The key issue is to determine a robust estimate of the (3times3) essential matrix with its five degrees of freedom. In this work, a robust technique to compute the essential matrix is suggested under the assumption...
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