Showing papers on "Range segmentation published in 1985"

Goal-Directed Textured-Image Segmentation

Abstract: The SLICE segmentation system identifies image regions that differ in gray-level distribution, color, spatial texture, or some other local property. This report concentrates on textured-image segmentation using local texture-energy measures and user delimited training regions. Knowledge of target textures or signatures is combined with knowledge of background textures by using histogram-similarity transforms to identify regions that are similar to a target texture and dissimilar to other textures.

Image segmentation at contour intersections in human focal vision.

Abstract: It would be efficient for the visual image to be broken initially into small segments, each of which could be analyzed separately before being related to the others to build a representation of the whole. The places in the image where such segmentation should occur will depend on the image and will not be fixed points in retinal space. In this study, it is shown that high-precision-shape information cannot be integrated across contour intersections. It is suggested that contour intersections are a particular feature of the retinal image at which segmentation inevitably occurs.

Adaptive hierarchical algorithm for accurate image segmentation

Abstract: A conceptually new algorithm is presented for segmenting textured images into regions in each of which data is modelled as one of C 2-D Markov Random Field (MRF). The algorithm is designed to operate in real time when implemented on new parallel architecture. Gaussian MRF is used to model textures in visible light images of outdoor and indoor scenes. Image segmentation is realized as a maximum likelihood estimation. To simplify the segmentation algorithm, the image is partitioned into disjoint square windows in each of which there will be one or atmost two different texture regions. In any given window the segmentation algorithm is hierarchical and uses a pyramid-like structure. This paper is an extension to material introduced in [1,2] and concentrates on exploring the segmentation accuracy of the algorithm and addressing more fully the question of how the algorithm can operate in adaptive modes when the parameters of the texture field are partially or totally unknown.

Segmentation of three dimensional range data

Abstract: A new approach to segmentation of range image data into quadric surfaces is treated here. The segmentation consists of three stages. First, an initial segmentation produces coarse surface regions classed as plane, cylinder, or sphere. The algorithm operates on contours, or 2D slices, of the data rather than the 3D range image data. Second, constrained surface fitting is used to obtain surface parameter estimates for each segmented region. And third, a high accuracy segmentation results in both a high resolution boundary between surface regions and accurate quadric surface parameters. The algorithm is hierarchical and works in stages from low to high image resolution while updating surface parameter estimates at each stage. Experimental results with synthetic and real range images are given.

Method for data reduction, especially data which are obtained during detection of optically detected rows, and a circuit arrangement for carrying out the method

Abstract: A method and device for carrying out the method for data reduction of an optoelectronic image-recording device, with the aid of autocorrelation of the individual pixel signals of each image frame, in that the difference between the present pixel signal of each pixel in an image frame and a pixel signal of the same pixel in the previous image frame, which is in each case delayed by one image frame and is updated by a factor F which is selected as a function of the relative speed between an object which is to be mapped and the image recording device, is determined and the difference is continuously added to the respective sum signal, which is likewise delayed by one image frame, and is output as a signal sequence which is related to time axes and relates to the same selected pixels of the successive image frames.