Search results for "Computer Science::Computer Vision and Pattern Recognition"
showing 10 items of 193 documents
Variable-Radius Offset Surface Approximation on the GPU
2020
Variable-radius offset surfaces find applications in various fields, such as variable brush strokes in 2D and 3D sketching and geometric modeling tools. In forensic facial reconstruction the skin surface can be inferred from a given skull by computing a variable-radius offset surface of the skull surface. Thereby, the skull is represented as a two-manifold triangle mesh and the facial soft tissue thickness is specified for each vertex of the mesh. We present a method to interactively visualize the wanted skin surface by rendering the variable-radius offset surfaces of all triangles of the skull mesh. We have also developed a special shader program which is able to generate a discretized vol…
Color Image Segmentation: The Hypergraph Framework
2006
International audience; Color Image Segmentation: The Hypergraph Framework
Post-processing of Pixel and Object-Based Land Cover Classifications of Very High Spatial Resolution Images
2020
The state of the art is plenty of classification methods. Pixel-based methods include the most traditional ones. Although these achieved high accuracy when classifying remote sensing images, some limits emerged with the advent of very high-resolution images that enhanced the spectral heterogeneity within a class. Therefore, in the last decade, new classification methods capable of overcoming these limits have undergone considerable development. Within this research, we compared the performances of an Object-based and a Pixel-Based classification method, the Random Forests (RF) and the Object-Based Image Analysis (OBIA), respectively. Their ability to quantify the extension and the perimeter…
Space variant vision and pipelined architecture for time to impact computation
2002
Image analysis is one of the most interesting ways for a mobile vehicle to understand its environment. One of the tasks of an autonomous vehicle is to get accurate information of what it has in front, to avoid collision or find a way to a target. This task requires real-time restrictions depending on the vehicle speed and external object movement. The use of normal cameras, with homogeneous (squared) pixel distribution, for real-time image processing, usually requires high performance computing and high image rates. A different approach makes use of a CMOS space-variant camera that yields a high frame rate with low data bandwidth. The camera also performs the log-polar transform, simplifyin…
Speeding-Up Differential Motion Detection Algorithms Using a Change-Driven Data Flow Processing Strategy
2007
A constraint of real-time implementation of differential motion detection algorithms is the large amount of data to be processed. Full image processing is usually the classical approach for these algorithms: spatial and temporal derivatives are calculated for all pixels in the image despite the fact that the majority of image pixels may not have changed from one frame to the next. By contrast, the data flow model works in a totally different way as instructions are only fired when the data needed for these instructions are available. Here we present a method to speed-up low level motion detection algorithms. This method is based on pixel change instead of full image processing and good spee…
A support vector domain method for change detection in multitemporal images
2010
This paper formulates the problem of distinguishing changed from unchanged pixels in multitemporal remote sensing images as a minimum enclosing ball (MEB) problem with changed pixels as target class. The definition of the sphere-shaped decision boundary with minimal volume that embraces changed pixels is approached in the context of the support vector formalism adopting a support vector domain description (SVDD) one-class classifier. SVDD maps the data into a high dimensional feature space where the spherical support of the high dimensional distribution of changed pixels is computed. Unlike the standard SVDD, the proposed formulation of the SVDD uses both target and outlier samples for defi…
A Clustering Approach to texture Classification
1988
In the paper a clustering technique to segment an image in to “homogeneous” regions is studied. The homogeneity of each region is evaluated by means of a “proximity function” computed between the pixels. The main result of such approach is that no-histogramming is required in order to perform segmentation. Possibilistic and probabilistic approaches are, also, combined to evaluate the significativity of the computed regions.
Shape Description for Content-Based Image Retrieval
2000
The present work is focused on a global image characterization based on a description of the 2D displacements of the different shapes present in the image, which can be employed for CBIR applications.To this aim, a recognition system has been developed, that detects automatically image ROIs containing single objects, and classifies them as belonging to a particular class of shapes.In our approach we make use of the eigenvalues of the covariance matrix computed from the pixel rows of a single ROI. These quantities are arranged in a vector form, and are classified using Support Vector Machines (SVMs). The selected feature allows us to recognize shapes in a robust fashion, despite rotations or…
Cluster kernels for semisupervised classification of VHR urban images
2009
In this paper, we present and apply a semisupervised support vector machine based on cluster kernels for the problem of very high resolution image classification. In the proposed setting, a base kernel working with labeled samples only is deformed by a likelihood kernel encoding similarities between unlabeled examples. The resulting kernel is used to train a standard support vector machine (SVM) classifier. Experiments carried out on very high resolution (VHR) multispectral and hyperspectral images using very few labeled examples show the relevancy of the method in the context of urban image classification. Its simplicity and the small number of parameters involved make it versatile and wor…
Automatic analysis of speckle photography fringes
1997
Speckle interferometry is a technique adequate to metrological problems such as the measurement of object deformation. An automatic system of analysis of such measurements is given; it consists of a motorized x-y plate positioner controlled by computer, a CCD video camera, and software for image analysis. A fringe-recognition algorithm determines the spacing and orientation of the fringes and permits the calculation of the magnitude and direction of the displacement of the analyzed object point in images with variable degrees of illumination. For a 256 x 256 pixel image resolution, the procedure allows one to analyze from three fringes to a number of fringes that corresponds to 3 pixels/fri…