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

Segmentation and semantic labelling of RGBD data with convolutional neural networks and surface fitting

We present an approach for segmentation and semantic labelling of RGBD data exploiting together geometrical cues and deep learning techniques. An initial over-segmentation is performed using spectral clustering and a set of non-uniform rational B-spline surfaces is fitted on the extracted segments. Then a convolutional neural network (CNN) receives in input colour and geometry data together with surface fitting parameters. The network is made of nine convolutional stages followed by a softmax classifier and produces a vector of descriptors for each sample. In the next step, an iterative merging algorithm recombines the output of the over-segmentation into larger regions matching the various elements of the scene. The couples of adjacent segments with higher similarity according to the CNN features are candidate to be merged and the surface fitting accuracy is used to detect which couples of segments belong to the same surface. Finally, a set of labelled segments is obtained by combining the segmentation output with the descriptors from the CNN. Experimental results show how the proposed approach outperforms state-of-the-art methods and provides an accurate segmentation and labelling

Technological Progression and Procedures in Microsoft Kinect Sensor

Kinect is a sensor technology capable of tracking any environmental phenomena by sensing it for identification. And it plays a unique and vital role in the study of identification as it recognizes every object using the sensor. However, Kinect sensors track and sense objects using their color and data with the enhancement of tracking various actions and postures. Improvisations are inhabited within the technology based on identity, digitalization, alpha channels, depth of color and sensors in Autism research. Major concepts of Kinect sensor and its enhancement features are surveyed in this paper

Joint segmentation of color and depth data based on splitting and merging driven by surface fitting

This paper proposes a segmentation scheme based on the joint usage of color and depth data together with a 3D surface estimation scheme. Firstly a set of multi-dimensional vectors is built from color, geometry and surface orientation information. Normalized cuts spectral clustering is then applied in order to recursively segment the scene in two parts thus obtaining an over-segmentation. This procedure is followed by a recursive merging stage where close segments belonging to the same object are joined together. At each step of both procedures a NURBS model is fitted on the computed segments and the accuracy of the fitting is used as a measure of the plausibility that a segment represents a single surface or object. By comparing the accuracy to the one at the previous step, it is possible to determine if each splitting or merging operation leads to a better scene representation and consequently whether to perform it or not. Experimental results show how the proposed method provides an accurate and reliable segmentation

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

Indoor/outdoor navigation system based on possibilistic traversable area segmentation for visually impaired people

Autonomous collision avoidance for visually impaired people requires a specific processing for an accurate definition of traversable area. Processing of a real time image sequence for traversable area segmentation is quite mandatory. Low cost systems suggest use of poor quality cameras. However, real time low cost camera suffers from great variability of traversable area appearance at indoor as well as outdoor environments. Taking into account ambiguity affecting object and traversable area appearance induced by reflections, illumination variations, occlusions (, etc...), an accurate segmentation of traversable area in such conditions remains a challenge. Moreover, indoor and outdoor environments add additional variability to traversable areas. In this paper, we present a real-time approach for fast traversable area segmentation from image sequence recorded by a low-cost monocular camera for navigation system. Taking into account all kinds of variability in the image, we apply possibility theory for modeling information ambiguity. An efficient way of updating the traversable area model in each environment condition is to consider traversable area samples from the same processed image for building its possibility maps. Then fusing these maps allows making a fair model definition of the traversable area. Performance of the proposed system was evaluated on public databases, with indoor and outdoor environments. Experimental results show that this method is challenging leading to higher segmentation rates

Segmentación de imágenes de tiempo de vuelo vía clustering espectral co-regularizado

Las cámaras de tiempo de vuelo (TOF) generan dos imágenes simultáneas, una de intensidad y una de rango. Esto permite abordar problemas de segmentación donde la información de intensidad o de rango separadamente es insuficiente para extraer los objetos de interés de la escena 3D. En este artículo se presenta un método de segmentación espectral, que combina información de ambas imágenes. Modificando la matriz de afinidad de cada una de las imágenes en función de la otra, se mejora la segmentación de los objetos de la escena. El método propuesto explota dos mecanismos, el primero orientado a reducir la demanda computacional en el cálculo de autovectores de cada matriz, y el segundo destinado a mejorar el rendimiento de la segmentación. Se presentan resultados experimentales sobre dos conjuntos de imágenes reales, que permiten evaluar el método propuesto.Red de Universidades con Carreras en Informática (RedUNCI

Joint segmentation of color and depth data based on splitting and merging driven by surface fitting

This paper proposes a segmentation scheme based on the joint usage of color and depth data together with a 3D surface estimation scheme. Firstly a set of multi-dimensional vectors is built from color, geometry and surface orientation information. Normalized cuts spectral clustering is then applied in order to recursively segment the scene in two parts thus obtaining an over-segmentation. This procedure is followed by a recursive merging stage where close segments belonging to the same object are joined together. At each step of both procedures a NURBS model is fitted on the computed segments and the accuracy of the fitting is used as a measure of the plausibility that a segment represents a single surface or object. By comparing the accuracy to the one at the previous step, it is possible to determine if each splitting or merging operation leads to a better scene representation and consequently whether to perform it or not. Experimental results show how the proposed method provides an accurate and reliable segmentation