KEYWORDS: Point clouds, 3D modeling, Clouds, Matrices, Databases, Computer simulations, Reconstruction algorithms, Singular value decomposition, Sensors, Mobile robots
Point cloud registration is a central problem in many mapping and monitoring applications such as 3D model reconstruction, computer vision, autonomous driving, and others. Generating maps of the environment is often referred to as the Simultaneous Localization and Mapping (SLAM) problem. Note that some point clouds from the considered set may not have intersections. In this paper, we propose an algorithm to align the multiple point clouds based on an effective pairwise registration and a global refinement algorithm. The global refinement algorithm is non-iterative. Computer simulation results are provided to illustrate the performance of the proposed method.
Over the past two decades, methods have been proposed for deaerating images, and most of them use a method of improving or restoring images. An image without haze should have a higher contrast than the original hazed image. It is possible remove haze by increasing the local contrast of the restored image. Some haze removal approaches estimate a hazed image from the observed hazed scene by solving an objective function whose parameters are adapted to the local statistics of the hazed image inside a moving window. Common image dehazing techniques use only one observed image for processing. Various variants of local adaptive algorithms for single image dehazing are known. A dehazing method based on spatially displaced sensors is also described. In this presentation, we propose a new dehazing algorithm that uses several scene images. Using a set of observed images, the dehazing of the image is carried out by solving a system of equations, which is derived from the optimization of the objective function. These images are made in such a way that they are spatially offset relative to each other and made in different time. Computer simulation results of are presented to illustrate the performance of the proposed algorithm for the restoration of hazed images.
This presentation deals with restoration of the image corrupted by impulsive noise using a novel cascade switching algorithm. The algorithm processes iteratively the observed degraded image changing adaptively parameters of the switching filter. With the help of computer simulation, we show that the proposed algorithm is able to effectively remove impulse noise from a highly contaminated image. The performance of the proposed algorithm is compared with that of common successful algorithms in terms of image restoration metrics.
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