New Robust Obstacle Detection System Using Color Stereo Vision

Intelligent transportation systems (ITS) are divided into intelligent infrastructure systems and intelligent vehicle systems. Intelligent vehicle systems are typically classified in three categories, namely 1) Collision Avoidance Systems; 2) Driver Assistance Systems and 3) Collision Notification Systems. Obstacle detection is one of crucial tasks for Collision Avoidance Systems and Driver Assistance Systems. Obstacle detection systems use vehiclemounted sensors to detect obstuctions, such as other vehicles, bicyclists, pedestrians, road debris, or animals, in a vehicleâs path and alert the driver. Obstacle detection systems are proposed to help drivers see farther and therefore have more time to react to road hazards. These systems also help drivers to get a large visibility area when the visibility conditions is reduced such as night, fog, snow, rain, ... Obstacle detection systems process data acquired from one or several sensors: radar Kruse et al. (2004), lidar Gao & Coifman (2006), monocular vision Lombardi & Zavidovique (2004), stereo vision Franke (2000) Bensrhair et al. (2002) Cabani et al. (2006b) Kogler et al. (2006) Woodfill et al. (2007), vision fused with active sensors Gern et al. (2000) Steux et al. (2002) Mobus & Kolbe (2004)Zhu et al. (2006) Alessandretti et al. (2007)Cheng et al. (2007). It is clear now that most obstacle detection systems cannot work without vision. Typically, vision-based systems consist of cameras that provide gray level images. When visibility conditions are reduced (night, fog, twilight, tunnel, snow, rain), vision systems are almost blind. Obstacle detection systems are less robust and reliable. To deal with the problem of reduced visibility conditions, infrared or color cameras can be used. Thermal imaging cameras are initially used by militaries. Over the last few years, these systems became accessible to the commercial market, and can be found in select 2006 BMW cars. For example, vehicle headlight systems provide between 75 to 140 meters of moderate illumination; at 90 K meters per hour this means less than 4 seconds to react to hazards. When with PathFindIR PathFindIR (n.d.) (a commercial system), a driver can have more than 15 seconds. Other systems still in the research stage assist drivers to detect pedestrians Xu & Fujimura (2002) Broggi et al. (2004) Bertozzi et al. (2007). Color is appropriate to various visibility conditions and various environments. In Betke et al. (2000) and Betke & Nguyen (1998), Betke et al. have demonstrated that the tracking o

Parametric Feature Detection

We propose an algorithm to automatically construct feature detectors for arbitrary parametric features. To obtain a high level of robustness we advocate the use of realistic multi-parameter feature models and incorporate optical and sensing effects. Each feature is represented as a densely sampled parametric manifold in a low dimensional subspace of a Hilbert space. During detection, the brightness distribution around each image pixel is projected into the subspace. If the projection lies sufficiently close to the feature manifold, the feature is detected and the location of the closest manifold point yields the feature parameters. The concepts of parameter reduction by normalization, dimension reduction, pattern rejection, and heuristic search are all employed to achieve the required efficiency. By applying the algorithm to appropriate parametric feature models, detectors have been constructed for five features, namely, step edge, roof edge, line, corner, and circular disc. Detailed experiments are reported on the robustness of detection and the accuracy of parameter estimation. In the case of the step edge, our results are compared with those obtained using popular detectors. We conclude with a brief discussion on the use of relaxation to rene outputs from multiple feature detectors, and sketch a hardware architecture for a general feature detection machine

Deformable kernels for early vision

Caption title.Includes bibliographical references (p. 22-24).Research supported by the U.S. Army Research Office. DAAL01-86-K-0171Pietro Perona

Linear feature detection on SAR images

We aim at defining an automatic or semi-automatic method for the extraction of road network in radar images . To realize the low level detection, line detectors are proposed . They extract linear features which become candidates for road segments . Two loca l structure detectors are developed taking into account the speckle phenomenon, and an extensive study of their behaviour as wel l as the behaviour of the fusion of their responses is made (theoretical detection and false alarm probabilities are evaluated an d computed on real images) . One of this detector is based on an edge detector widely used in coherent imagery, and the secon d one is based on a specific adapted filtering . The line detection is eventually made by the fusion of both detector responses .Dans l'objectif d'établir une méthode de détection semi-automatique du réseau routier sur des images radar, des détecteur de lignes sont développés. Ils ont pour but d'extraire des structures linéaires de l'image, qui seront ensuite considérées comme des segments « candidats routes ». Deux détecteurs de lignes prenant en compte les statistiques du speckle sont proposés, et une étude détaillée de leurs comportements et du comportement de leur fusion (probabilités de détection et de fausse alarme) est réalisée. L'un de ces détecteurs est fondé sur un détecteur de contours largement utilisé en imagerie radar, et l'autre plus original sur un filtrage adapté. La détection de lignes est finalement réalisée par la fusion des réponses de ces deux détecteurs

Using a disk operator to convert raster images of engineering drawings to vector images

Computer Scienc

A microcomputer-based vision system to recognize and locate partially occluded parts in binary and gray level images

This paper presents a microcomputer-based machine vision system to recognize and locate partially occluded parts in binary or gray level images. The recognition process is restricted to untilted, two-dimensional objects;A new edge-tracking technique in conjunction with a straight-line approximation algorithm is used to identify the local features in an image. Corners and holes serve as local features. The local features identified in an image are matched against all the compatible features stored for the model parts. The algorithm computes, for all image and model features matches, a coordinate transformation that maps a model feature onto an image feature. A new clustering algorithm has been developed to identify consistent coordinate transformation clusters that serve as initial match hypotheses. A hypothesis verification process eliminates the match hypotheses that are not compatible with the image information;The system performance was compared to a vision system restricted to recognize nonoverlapping parts. Both systems require the same hardware configuration and share the basic image processing routines

Applications of Circular Array Sensors

Submitted to the Department of Electrical Engineering and Computer Science on January 18, 1980 in partial fulfillment of the requirements for the Degree Master of Science in Electrical Engineering and Computer ScienceThe application of the Reticon RO-64 annular photo-diode array to the task of optical tracking of special targets, direct optical focusing, and automatic printed circuit board inspection were studied. In order to facilitate this work, a digital camera unit incorporating the array was designed and constructed. Of the three applications investigated, the tracking task proved to be the most successful, since multiple targets were tracked in real time using the array. In the focusing application, the digital approach was found to be too slow for real-time use, and suggestions were made for the analog implementation of a focusing algorithm using the array. The printed circuit board inspection algorithm detected errors successfully, but the inefficiency of image acquisition with the array is a serious drawback, leading to the conclusion that linear arrays of similar design would provide faster and less expensive inspection. Thus the annular geometry is best suited to the onetime sampling of points on a circle in an image, as in the case of the tracking and focusing tasks. The focusing task suffers mainly from the amount of computation required to achieve focus, and from its competition with more established indirect focusing techniques.MIT Artificial Intelligence Laborator