1,069 research outputs found

    Disparity refinement process based on RANSAC plane fitting for machine vision applications

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    This paper presents a new disparity map refinement process for stereo matching algorithm and the refinement stage that will be implemented by partitioning the place or mask image and re-projected to the preliminary disparity images. This process is to refine the noise and sparse of initial disparity map from weakly textured. The plane fitting algorithm is using Random Sample Consensus. Two well-known stereo matching algorithms have been tested on this framework with different filtering techniques applied at disparity refinement stage. The framework is evaluated on three Middlebury datasets. The experimental results show that the proposed framework produces better-quality and more accurate than normal flow state-of-the-art stereo matching algorithms. The performance evaluations are based on standard image quality metrics i.e. structural similarity index measure, peak signal-to-noise ratio and mean square error.Keywords: computer vision; disparity refinement; image segmentation; RANSAC; stereo.

    Dynamic 3D Sensors: Data Characterization and Post- Processing

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    After a brief introduction about Time–of–Flight range cameras and 3D sensor of Microsoft Kinect characteristics, a deep analysis on statistical distribution of data retrieved from these sensors, is performed. A set of algorithms and procedures are designed and implemented to improve the general quality of the depth–maps acquired, on the basis of the problems highlighted. They are computed in particular denoising and upscaling operations, through the use of an innovative and smart smoothing filter, the trilateral filter. The main attention is focused towards Kinect sensor, but the procedure can be adapted to other setting of utilizations. In the end they are presented experimental results, applications and further improvement

    Fast sparse coding for range data denoising with sparse ridges constraint

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    Light detection and ranging (LiDAR) sensors have been widely deployed on intelligent systems such as unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs) to perform localization, obstacle detection, and navigation tasks. Thus, research into range data processing with competitive performance in terms of both accuracy and efficiency has attracted increasing attention. Sparse coding has revolutionized signal processing and led to state-of-the-art performance in a variety of applications. However, dictionary learning, which plays the central role in sparse coding techniques, is computationally demanding, resulting in its limited applicability in real-time systems. In this study, we propose sparse coding algorithms with a fixed pre-learned ridge dictionary to realize range data denoising via leveraging the regularity of laser range measurements in man-made environments. Experiments on both synthesized data and real data demonstrate that our method obtains accuracy comparable to that of sophisticated sparse coding methods, but with much higher computational efficiency

    High-ISO long-exposure image denoising based on quantitative blob characterization

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    Blob detection and image denoising are fundamental, sometimes related tasks in computer vision. In this paper, we present a computational method to quantitatively measure blob characteristics using normalized unilateral second-order Gaussian kernels. This method suppresses non-blob structures while yielding a quantitative measurement of the position, prominence and scale of blobs, which can facilitate the tasks of blob reconstruction and blob reduction. Subsequently, we propose a denoising scheme to address high-ISO long-exposure noise, which sometimes spatially shows a blob appearance, employing a blob reduction procedure as a cheap preprocessing for conventional denoising methods. We apply the proposed denoising methods to real-world noisy images as well as standard images that are corrupted by real noise. The experimental results demonstrate the superiority of the proposed methods over state-of-the-art denoising methods

    Categorization of indoor places by combining local binary pattern histograms of range and reflectance data from laser range finders

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    This paper presents an approach to categorize typical places in indoor environments using 3D scans provided by a laser range finder. Examples of such places are offices, laboratories, or kitchens. In our method, we combine the range and reflectance data from the laser scan for the final categorization of places. Range and reflectance images are transformed into histograms of local binary patterns and combined into a single feature vector. This vector is later classified using support vector machines. The results of the presented experiments demonstrate the capability of our technique to categorize indoor places with high accuracy. We also show that the combination of range and reflectance information improves the final categorization results in comparison with a single modality
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