Microsoft Kinect

Postprint (published version

Time of Flight Image Segmentation through Co-Regularized Spectral Clustering

Time of Flight (TOF) cameras generate two simultaneous images, one for intensity and one for range. This allows tackling segmentation problems where the information pertaining to intensity or range alone is not enough to extract objects of interest from a 3D scene. In this paper, we present a spectral segmentation method that combines information from both images. By modifying the affinity matrix of each of the images based on the other, the segmentation of objects in the scene is improved. The proposed method exploits two mechanisms, one for reducing the computational demand when calculating the eigenvectors for each matrix, and another for improving segmentation performance. The experimental results obtained with two sets of real images are presented and used to assess the proposed method.Publicado en Feierherd, Guillermo; Pesado, Patricia; Spositto, Osvaldo (eds.). Computer Science & Technology Series. XX Argentine Congress of Computer Science. Selected papers. La Plata, Editorial de la Universidad Nacional de La Plata, 2015.Red de Universidades con Carreras en Informática (RedUNCI

Time of Flight Image Segmentation through Co-Regularized Spectral Clustering

Time of Flight (TOF) cameras generate two simultaneous images, one for intensity and one for range. This allows tackling segmentation problems where the information pertaining to intensity or range alone is not enough to extract objects of interest from a 3D scene. In this paper, we present a spectral segmentation method that combines information from both images. By modifying the affinity matrix of each of the images based on the other, the segmentation of objects in the scene is improved. The proposed method exploits two mechanisms, one for reducing the computational demand when calculating the eigenvectors for each matrix, and another for improving segmentation performance. The experimental results obtained with two sets of real images are presented and used to assess the proposed method.Publicado en Feierherd, Guillermo; Pesado, Patricia; Spositto, Osvaldo (eds.). Computer Science & Technology Series. XX Argentine Congress of Computer Science. Selected papers. La Plata, Editorial de la Universidad Nacional de La Plata, 2015.Red de Universidades con Carreras en Informática (RedUNCI

Plane-extraction from depth-data using a Gaussian mixture regression model

We propose a novel algorithm for unsupervised extraction of piecewise planar models from depth-data. Among other applications, such models are a good way of enabling autonomous agents (robots, cars, drones, etc.) to effectively perceive their surroundings and to navigate in three dimensions. We propose to do this by fitting the data with a piecewise-linear Gaussian mixture regression model whose components are skewed over planes, making them flat in appearance rather than being ellipsoidal, by embedding an outlier-trimming process that is formally incorporated into the proposed expectation-maximization algorithm, and by selectively fusing contiguous, coplanar components. Part of our motivation is an attempt to estimate more accurate plane-extraction by allowing each model component to make use of all available data through probabilistic clustering. The algorithm is thoroughly evaluated against a standard benchmark and is shown to rank among the best of the existing state-of-the-art methods.Comment: 11 pages, 2 figures, 1 tabl

Ground plane detection using an RGB-D sensor

Ground plane detection is essential for successful navigation of vision based mobile robots. We introduce a very simple but robust ground plane detection method based on depth information obtained using anRGB-Depth sensor. We present two different variations of the method: the simplest one is robust in setups where the sensor pitch angle is fixed and has no roll, whereas the second one can handle changes in pitch and roll angles. Our comparisons show that our approach performs better than the vertical disparity approach. It produces accurate ground plane-obstacle segmentation for difficult scenes, which include many obstacles, different floor surfaces, stairs, and narrow corridors.Publisher's VersionAuthor Post Prin