654 research outputs found

    Evolving a rule system controller for automatic driving in a car racing competition

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    IEEE Symposium on Computational Intelligence and Games. Perth, Australia, 15-18 December 2008.The techniques and the technologies supporting Automatic Vehicle Guidance are important issues. Automobile manufacturers view automatic driving as a very interesting product with motivating key features which allow improvement of the car safety, reduction in emission or fuel consumption or optimization of driver comfort during long journeys. Car racing is an active research field where new advances in aerodynamics, consumption and engine power are critical each season. Our proposal is to research how evolutionary computation techniques can help in this field. For this work we have designed an automatic controller that learns rules with a genetic algorithm. This paper is a report of the results obtained by this controller during the car racing competition held in Hong Kong during the IEEE World Congress on Computational Intelligence (WCCI 2008).Publicad

    A hybrid content based image retrieval system using log-gabor filter banks

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    In this paper, a new efficient image retrieval system using sequential process of three stages with filtering technique for the feature selection is proposed. In the first stage the color features are extracted using color histogram method and in the second stage the texture features are obtained using log-Gabor filters and in the third stage shape features are extracted using shape descriptors using polygonal fitting algorithm. The proposed log-Gabor filter in the second stage has advantages of retrieving images over regular Gabor filter for texture. It provides better representation of the images. Experimental evaluation of the proposed system shows improved performance in retrieval as compared to other existing systems in terms of average precision and average recall

    A ROBUST GA/KNN BASED HYPOTHESIS VERIFICATION SYSTEM FOR VEHICLE DETECTION

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    ABSTRACT Vehicle detection is an important issue in driver assistance systems and self-guided vehicles that include

    Advanced Feature Learning and Representation in Image Processing for Anomaly Detection

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    Techniques for improving the information quality present in imagery for feature extraction are proposed in this thesis. Specifically, two methods are presented: soft feature extraction and improved Evolution-COnstructed (iECO) features. Soft features comprise the extraction of image-space knowledge by performing a per-pixel weighting based on an importance map. Through soft features, one is able to extract features relevant to identifying a given object versus its background. Next, the iECO features framework is presented. The iECO features framework uses evolutionary computation algorithms to learn an optimal series of image transforms, specific to a given feature descriptor, to best extract discriminative information. That is, a composition of image transforms are learned from training data to present a given feature descriptor with the best opportunity to extract its information for the application at hand. The proposed techniques are applied to an automatic explosive hazard detection application and significant results are achieved

    INTELLIGENT ROAD MAINTENANCE: A MACHINE LEARNING APPROACH FOR SURFACE DEFECT DETECTION

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    The emergence of increased sources for Big Data through consumer recording devices gives rise to a new basis for the management and governance of public infrastructures and policy de-sign. Road maintenance and detection of road surface defects, such as cracks, have traditionally been a time consuming and manual process. Lately, increased automation using easily acquirable front-view digital natural scene images is seen to be an alternative for taking timely maintenance decisions; reducing accidents and operating cost and increasing public safety. In this paper, we propose a machine learning based approach to handle the challenge of crack and related defect detection on road surfaces using front-view images captured from driver’s viewpoint under diverse conditions. We use a superpixel based method to first process the road images into smaller coherent image regions. These superpixels are then classified into crack and non-crack regions. Various texture-based features are combined for the classification mod-el. Classifiers such as Gradient Boosting, Artificial Neural Network, Random Forest and Linear Support Vector Machines are evaluated for the task. Evaluations on real datasets show that the approach successfully handles different road surface conditions and crack-types, while locating the defective regions in the scene images

    Dynamic obstacle detection of road scenes using equi-height mosaicking image

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    Advisor: Soon Ki Jung. Date and location of PhD thesis defense: 3 December 2013, Kyungpook National UniversityMany automobile companies and researchers have developed various safety systems to reduce fatalities by traffic accidents. In order to prevent traffic accidents by distracted driving, therefore, we present the vehicle and pedestrian detection using a novel image representation called equi-height mosaicking system. Furthermore, we additionally suggest the part-based side detection method using equi-height peripheral mosaicking image to detect approaching vehicles while driving

    Efficient Real Time Vehicles Detection Approach for Complex Urban Traffic Management

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    Vehicles now a day are becoming very necessary part of our life this is leading to increase in congested traffic conditions around the world. With our research in this domain of vehicle detection with image processing we are focusing on detection of vehicles. This is then combined with car size detection. Our approach is to first work on the feature of four wheeler i.e., windshield of the car. Then to work with saliency generation of the region of interest which will be containing only windshield part for the vehicles in the frame. With conversion of this frame into HSV color model. From HSV the saturation value within certain limit is kept for the segmentation then on from that leading to the detection of the vehicles in the given frame. Vehicle size detection we have considered eagle eye view for taking out area and on the basis of that deciding threshold for the vehicles into different categories. Categories of vehicles include small, medium, large vehicles for four wheelers

    Histograms of oriented gradients for fast on-board vehicle verification

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    Histograms of Oriented Gradients (HoGs) provide excellent results in object detection and verification. However, their demanding processing requirements bound their applicability in some critical real-time scenarios, such as for video-based on-board vehicle detection systems. In this work, an efficient HOG configuration for pose-based on-board vehicle verification is proposed, which alleviates both the processing requirements and required feature vector length without reducing classification performance. The impact on classification of some critical configuration and processing parameters is in depth analyzed to propose a baseline efficient descriptor. Based on the analysis of its cells contribution to classification, new view-dependent cell-configuration patterns are proposed, resulting in reduced descriptors which provide an excellent balance between performance and computational requirements, rendering higher verification rates than other works in the literature

    Real Time Weed Detection using a Boosted Cascade of Simple Features

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    Weed detection is a crucial issue in precision agriculture. In computer vision, variety of techniques are developed to detect, identify and locate weeds in different cultures. In this article, we present a real-time new weed detection method, through an embedded monocular vision. Our approach is based on the use of a cascade of discriminative classifiers formed by the Haar-like features. The quality of the results determines the validity of our approach, and opens the way to new horizons in weed detection
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