Apercu sur les quadarbres et les structures de donnees hierarchiques. Elles sont basees sur le principe de decomposition recursive. L'accentuation est mise sur la representation de donnees dans les applications de traitement d'images, d'infographie, les systemes d'informations geographiques et la robotique. On examine en detail un certain nombre d'operations dans lesquelles de telles structures de donnees trouvent leur utilisation

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The Quadtree and Related Hierarchical Data Structures

System and method for preventing vehicle accidents in which GPS ranging signals relating to a host vehicle's position on a roadway on a surface of the earth are received on a first communication link from a network of satellites and DGPS auxiliary range correction signals for correcting propagation delay errors in the GPS ranging signals are received on a second communication link from a station or satellite. The host vehicle's position on a roadway on a surface of the earth is determined from the GPS, DGPS, and accurate map database signals with centimeter accuracy and communicated to other vehicles. The host vehicle receives position information from other vehicles and determines whether any other vehicle from which position information is received represents a collision threat to the host vehicle based on the position of the other vehicle relative to the roadway and the host vehicle. If so, a warning or vehicle control signal response to control the host vehicle's motion is generated to prevent a collision with the other vehicle.

Accident avoidance system

Object detection in optical remote sensing images, being a fundamental but challenging problem in the field of aerial and satellite image analysis, plays an important role for a wide range of applications and is receiving significant attention in recent years. While enormous methods exist, a deep review of the literature concerning generic object detection is still lacking. This paper aims to provide a review of the recent progress in this field. Different from several previously published surveys that focus on a specific object class such as building and road, we concentrate on more generic object categories including, but are not limited to, road, building, tree, vehicle, ship, airport, urban-area. Covering about 270 publications we survey (1) template matching-based object detection methods, (2) knowledge-based object detection methods, (3) object-based image analysis (OBIA)-based object detection methods, (4) machine learning-based object detection methods, and (5) five publicly available datasets and three standard evaluation metrics. We also discuss the challenges of current studies and propose two promising research directions, namely deep learning-based feature representation and weakly supervised learning-based geospatial object detection. It is our hope that this survey will be beneficial for the researchers to have better understanding of this research field.

A survey on object detection in optical remote sensing images

The authors propose a two-step algorithm for almost unsupervised detection of linear structures, in particular, main axes in road networks, as seen in synthetic aperture radar (SAR) images. The first step is local and is used to extract linear features from the speckle radar image, which are treated as road-segment candidates. The authors present two local line detectors as well as a method for fusing information from these detectors. In the second global step, they identify the real roads among the segment candidates by defining a Markov random field (MRF) on a set of segments, which introduces contextual knowledge about the shape of road objects. The influence of the parameters on the road detection is studied and results are presented for various real radar images.

http://biblio.telecom-paristech.fr/cgi-bin/download.cgi?id=372

Detection of linear features in SAR images: application to road network extraction

Accident avoidance system for a host vehicle includes a global positioning system residing on the host vehicle for determining the host vehicle's location as the host vehicle travels based on signals received from one or more satellites, a map database having digital maps corresponding to an area including the location of the host vehicle as determined by the global positioning system, a vehicle-to-vehicle communication system residing on the host vehicle operative for receiving signals including location information acquired by global positioning systems residing on other vehicles directly from the other vehicles indicating the locations of the other vehicles, and a navigation system including a display residing on the host vehicle for displaying images to an occupant of the host vehicle showing the digital maps and indications of the locations of the host vehicle and the other vehicles on the digital maps. The navigation system also updates the images shown on the navigation system display to reflect changes in the locations of the host vehicle and the other vehicles.

Accident Avoidance Systems and Methods

A description is given of research in digital mapping and image understanding in the area of automated feature extraction from aerial imagery. The authors discuss a system for road tracking, ARF (A Road Follower), that uses multiple cooperative methods for extracting information about road location and structure from complex aerial imagery. This system is a multilevel architecture for image analysis that follows for cooperation among low-level processes and aggregation of information by high-level analysis components. Two low-level road tracking methods have been implemented: road-surface texture correlation and road-edge following. Each works independently to establish a model of the centerline of the road, its width, and other local properties. >

Cooperative methods for road tracking in aerial imagery

In this paper we discuss the use of map descriptions to guide the extraction of man-made and natural features from aerial imagery. An approach to image analysis using a region-based segmentation system is described. This segmentation system has been used to search a database of images that are in correspondence with a geodetic map to find occurrences of known buildings, roads, and natural features. The map predicts the approximate appearance and position of a feature in an image. The map also predicts the area of uncertainty caused by errors in the image to map correspondence. The segmentation process then searches for image regions that satisfy 2dimensional shape and intensity criteria. If no initial region is found, the process attempts to merge together those regions that may satisfy these criteria. Several detailed examples of the segmentation process are given 1. Copyright © 1984 David M. McKeown, Jr. and Jerry L. Denlinger This research was sponsored by the Defense Advanced Research Projects Agency (DOD), ARPA Order No. 3597, monitored by the Air Force Avionics Laboratory Under Contract F33615-81-K-1539. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Defense Advanced Research Projects Agency or the US Government. This paper was presented at the IEEE Workshop on Computer Vision, April 30 May 2,1984 in Annapolis, Maryland. Map-Guided Feature Extraction Table of

Map-guided feature extraction from aerial imagery

This paper describes BROWSE, an interactive raster image display facility which is a major component of a larger integrated Map Assisted Photo-interpretation System (MAPS), being developed as a prototype interactive aid for photo-interpretation. Application areas for this research include image cartography, land use studies and reconnaissance, as well as image database organization, storage, and retrieval.BROWSE is a window-oriented display manager which supports raster image display, overlay of graphical data such as map descriptions and image processing segmentations, and the specification and generation of 3D shaded surface models. Digitized imagery from black and white and color aerial mapping photographs is displayed by BROWSE at multiple levels of resolution and allows for dynamic positioning, zooming, expansion or shrinking of the image window. Map data represented as vectors and polygons can be superimposed on the imagery through image-to-map registration. Access to collateral map databases and terrain models may be accomplished using the BROWSE graphical interface. Finally, the window representation gives a convenient communication mechanism for passing image fragments to image interpretation programs, which generally run as separate processes. The results of such processing can be returned to BROWSE for further processing by the user.We will discuss the rationale behind the design of BROWSE as well as its application to domains including aerial photo-interpretation and 3D cartography.

Jerry L. Denlinger

Papers

Cooperative methods for road tracking in aerial imagery

Map-guided feature extraction from aerial imagery

Graphical tools for interactive image interpretation