Real-Time Lane Region Detection Using a Combination of Geometrical and Image Features

Over the past few decades, pavement markings have played a key role in intelligent vehicle applications such as guidance, navigation, and control. However, there are still serious issues facing the problem of lane marking detection. For example, problems include excessive processing time and false detection due to similarities in color and edges between traffic signs (channeling lines, stop lines, crosswalk, arrows, etc.). This paper proposes a strategy to extract the lane marking information taking into consideration its features such as color, edge, and width, as well as the vehicle speed. Firstly, defining the region of interest is a critical task to achieve real-time performance. In this sense, the region of interest is dependent on vehicle speed. Secondly, the lane markings are detected by using a hybrid color-edge feature method along with a probabilistic method, based on distance-color dependence and a hierarchical fitting model. Thirdly, the following lane marking information is extracted: the number of lane markings to both sides of the vehicle, the respective fitting model, and the centroid information of the lane. Using these parameters, the region is computed by using a road geometric model. To evaluate the proposed method, a set of consecutive frames was used in order to validate the performanceOver the past few decades, pavement markings have played a key role in intelligent vehicle applications such as guidance, navigation, and control. However, there are still serious issues facing the problem of lane marking detection. For example, problems include excessive processing time and false detection due to similarities in color and edges between traffic signs (channeling lines, stop lines, crosswalk, arrows, etc.). This paper proposes a strategy to extract the lane marking information taking into consideration its features such as color, edge, and width, as well as the vehicle speed. Firstly, defining the region of interest is a critical task to achieve real-time performance. In this sense, the region of interest is dependent on vehicle speed. Secondly, the lane markings are detected by using a hybrid color-edge feature method along with a probabilistic method, based on distance-color dependence and a hierarchical fitting model. Thirdly, the following lane marking information is extracted: the number of lane markings to both sides of the vehicle, the respective fitting model, and the centroid information of the lane. Using these parameters, the region is computed by using a road geometric model. To evaluate the proposed method, a set of consecutive frames was used in order to validate the performanc

Detection of arcs in Saturn's F ring during the 1995 Sun ring-plane crossing

Observations of the November 1995 Sun crossing of the Saturn's ring-plane made with the 3.6m CFH telescope, using the UHAO adaptive optics system, are presented here. We report the detection of four arcs located in the vicinity of the F ring. They can be seen one day later in HST images. The combination of both data sets gives accurate determinations of their orbits. Semi-major axes range from 140020 km to 140080 km, with a mean of 140060 +- 60 km. This is about 150 km smaller than previous estimates of the F ring radius from Voyager 1 and 2 data, but close to the orbit of another arc observed at the same epoch in HST images.Comment: 8 pages, 3 figures, 1 table, To appear in A&A, for comments : [email protected]

Global detection and analysis of coastline associated rainfall using an objective pattern recognition technique

Coastally associated rainfall is a common feature especially in tropical and subtropical regions. However, it has been difficult to quantify the contribution of coastal rainfall features to the overall local rainfall. We develop a novel technique to objectively identify precipitation associated with land-sea interaction and apply it to satellite based rainfall estimates. The Maritime Continent, the Bight of Panama, Madagascar and the Mediterranean are found to be regions where land-sea interactions plays a crucial role in the formation of precipitation. In these regions $\approx$ 40% to 60% of the total rainfall can be related to coastline effects. Due to its importance for the climate system, the Maritime Continent is a particular region of interest with high overall amounts of rainfall and large fractions resulting from land-sea interactions throughout the year. To demonstrate the utility of our identification method we investigate the influence of several modes of variability, such as the Madden-Julian-Oscillation and the El Ni\~no Southern Oscillation, on coastal rainfall behavior. The results suggest that during large scale suppressed convective conditions coastal effects tend modulate the rainfall over the Maritime Continent leading to enhanced rainfall over land regions compared to the surrounding oceans. We propose that the novel objective dataset of coastally influenced precipitation can be used in a variety of ways, such as to inform cumulus parametrization or as an additional tool for evaluating the simulation of coastal precipitation within weather and climate models

Detecting Extrasolar Planets with Integral Field Spectroscopy

Observations of extrasolar planets using Integral Field Spectroscopy (IFS), if coupled with an extreme Adaptive Optics system and analyzed with a Simultaneous Differential Imaging technique (SDI), are a powerful tool to detect and characterize extrasolar planets directly; they enhance the signal of the planet and, at the same time, reduces the impact of stellar light and consequently important noise sources like speckles. In order to verify the efficiency of such a technique, we developed a simulation code able to test the capabilities of this IFS-SDI technique for different kinds of planets and telescopes, modelling the atmospheric and instrumental noise sources. The first results obtained by the simulations show that many significant extrasolar planet detections are indeed possible using the present 8m-class telescopes within a few hours of exposure time. The procedure adopted to simulate IFS observations is presented here in detail, explaining in particular how we obtain estimates of the speckle noise, Adaptive Optics corrections, specific instrumental features, and how we test the efficiency of the SDI technique to increase the signal-to-noise ratio of the planet detection. The most important results achieved by simulations of various objects, from 1 M_J to brown dwarfs of 30 M_J, for observations with an 8 meter telescope, are then presented and discussed.Comment: 60 pages, 37 figures, accepted in PASP, 4 Tables adde

Automated Road Lane Detection for Intelligent Vehicles

Automated road lane detection is the crucial part of vision-based driver assistance system of intelligent vehicles. This driver assistance system reduces the road accidents, enhances safety and improves the traffic conditions. In this paper, we present an algorithm for detecting marks of road lane and road boundary with a view to the smart navigation of intelligent vehicles. Initially, it converts the RGB road scene image into gray image and employs the flood-fill algorithm to label the connected components of that gray image. Afterwards, the largest connected component which is the road region is extracted from the labeled image using maximum width and no. of pixels. Eventually, the outside region is subtracted and the marks or road lane and road boundary are extracted from connected components. The experimental results show the effectiveness of the proposed algorithm on both straight and slightly curved road scene images under different day light conditions and the presence of shadows on the roads