5,974 research outputs found
A Cosmic Watershed: the WVF Void Detection Technique
On megaparsec scales the Universe is permeated by an intricate filigree of
clusters, filaments, sheets and voids, the Cosmic Web. For the understanding of
its dynamical and hierarchical history it is crucial to identify objectively
its complex morphological components. One of the most characteristic aspects is
that of the dominant underdense Voids, the product of a hierarchical process
driven by the collapse of minor voids in addition to the merging of large ones.
In this study we present an objective void finder technique which involves a
minimum of assumptions about the scale, structure and shape of voids. Our void
finding method, the Watershed Void Finder (WVF), is based upon the Watershed
Transform, a well-known technique for the segmentation of images. Importantly,
the technique has the potential to trace the existing manifestations of a void
hierarchy. The basic watershed transform is augmented by a variety of
correction procedures to remove spurious structure resulting from sampling
noise. This study contains a detailed description of the WVF. We demonstrate
how it is able to trace and identify, relatively parameter free, voids and
their surrounding (filamentary and planar) boundaries. We test the technique on
a set of Kinematic Voronoi models, heuristic spatial models for a cellular
distribution of matter. Comparison of the WVF segmentations of low noise and
high noise Voronoi models with the quantitatively known spatial characteristics
of the intrinsic Voronoi tessellation shows that the size and shape of the
voids are succesfully retrieved. WVF manages to even reproduce the full void
size distribution function.Comment: 24 pages, 15 figures, MNRAS accepted, for full resolution, see
http://www.astro.rug.nl/~weygaert/tim1publication/watershed.pd
A graph-based mathematical morphology reader
This survey paper aims at providing a "literary" anthology of mathematical
morphology on graphs. It describes in the English language many ideas stemming
from a large number of different papers, hence providing a unified view of an
active and diverse field of research
Automatic Main Road Extraction from High Resolution Satellite Imagery
Road information is essential for automatic GIS (geographical information system) data acquisition, transportation and urban planning. Automatic road (network) detection from high resolution satellite imagery will hold great potential for significant reduction of database development/updating cost and turnaround time. From so called low level feature detection to high level context supported grouping, so many algorithms and methodologies have been presented for this purpose. There is not any practical system that can fully automatically extract road network from space imagery for the purpose of automatic mapping. This paper presents the methodology of automatic main road detection from high resolution satellite IKONOS imagery. The strategies include multiresolution or image pyramid method, Gaussian blurring and the line finder using 1-dimemsional template correlation filter, line segment grouping and multi-layer result integration. Multi-layer or multi-resolution method for road extraction is a very effective strategy to save processing time and improve robustness. To realize the strategy, the original IKONOS image is compressed into different corresponding image resolution so that an image pyramid is generated; after that the line finder of 1-dimemsional template correlation filter after Gaussian blurring filtering is applied to detect the road centerline. Extracted centerline segments belong to or do not belong to roads. There are two ways to identify the attributes of the segments, the one is using segment grouping to form longer line segments and assign a possibility to the segment depending on the length and other geometric and photometric attribute of the segment, for example the longer segment means bigger possibility of being road. Perceptual-grouping based method is used for road segment linking by a possibility model that takes multi-information into account; here the clues existing in the gaps are considered. Another way to identify the segments is feature detection back-to-higher resolution layer from the image pyramid
A reconfigurable real-time morphological system for augmented vision
There is a significant number of visually impaired individuals who suffer sensitivity loss to high spatial frequencies, for whom current optical devices are limited in degree of visual aid and practical application. Digital image and video processing offers a variety of effective visual enhancement methods that can be utilised to obtain a practical augmented vision head-mounted display device. The high spatial frequencies of an image can be extracted by edge detection techniques and overlaid on top of the original image to improve visual perception among the visually impaired. Augmented visual aid devices require highly user-customisable algorithm designs for subjective configuration per task, where current digital image processing visual aids offer very little user-configurable options. This paper presents a highly user-reconfigurable morphological edge enhancement system on field-programmable gate array, where the morphological, internal and external edge gradients can be selected from the presented architecture with specified edge thickness and magnitude. In addition, the morphology architecture supports reconfigurable shape structuring elements and configurable morphological operations. The proposed morphology-based visual enhancement system introduces a high degree of user flexibility in addition to meeting real-time constraints capable of obtaining 93 fps for high-definition image resolution
A mathematical morphology based approach for vehicle detection in road tunnels
A novel approach to automatically detect vehicles in road tunnels is presented in this paper. Non-uniform and poor illumination conditions prevail in road tunnels making difficult to achieve robust vehicle detection. In order to cope with the illumination issues, we propose a local higher-order statistic filter to make the vehicle detection invariant to illumination changes, whereas a morphological-based background subtraction is used to generate a convex hull segmentation of the vehicles. An evaluation test comparing our approach with a benchmark object detector shows that our approach outperforms in terms of false detection rate and overlap area detection
2D operators on topographic and non-topographic architectures-implementation, efficiency analysis, and architecture selection methodology
Topographic and non-topographic image processing architectures and chips, developed within the CNN community recently, are analysed and compared. It is achieved on a way that the 2D operators are collected to classes according to their implementation methods on the different architectures, and the main implementation parameters of the different operator classes are compared. Based on the results, an efficient architecture selection methodology is formalized
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