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For any autonomous system it is very important to acquire the knowledge of the surrounding environment. Images and videos acquired by the vision based sensors can provide meaningful information about the environment, which can be very useful for the navigation of autonomous system like mobile robots. To extract road information from image frames for navigation purpose they have to be classified. Classification...
This paper presents a novel traffic sign recognition system comprising of: (i) Color/shape classification, (ii) Pictogram extraction, (iii) Features selection and, (iv) Lyapunov Theory-based Radial Basis Function neural network (RBFNN). In the proposed system, traffic signs are first segmented and classified with regard to its unique color and shape in order to partition a large set of data into smaller...
Navigation is a broad topic that has been receiving considerable attention from the mobile robotic community. In order to execute a autonomous driving on outdoors, like street and roads, it is necessary that the vehicle identify parts of the terrain that can be traversed and parts that should be avoided. This paper describes an analyses of many multi-layer perceptron neural networks(ANN) used for...
High-Definition video can provide useful information regarding distant objects. For example, when trying to detect traffic signs. However, current detection methods have not been thoroughly tested in terms of their ability to detect signs over changing distance. In this work, we present such an experimental comparison of detection methods. We also present a novel object detection method based on colour...
Non-linearity of color changing in various lighting conditions is one of the primary factors which make lane color recognition difficult. This paper introduces an illumination invariant lane color recognition method which can recognize two lane colors (white, yellow) and copes with the non-linearity by using neural networks. Our method utilizes the road texture as the indicator of illumination condition...
We present a novel image operator that seeks to find the value of stroke width for each image pixel, and demonstrate its use on the task of text detection in natural images. The suggested operator is local and data dependent, which makes it fast and robust enough to eliminate the need for multi-scale computation or scanning windows. Extensive testing shows that the suggested scheme outperforms the...
Vision-based road detection is very challenging since the road is in an outdoor scenario imaged from a mobile platform. In this paper, a new top-down road detection algorithm is proposed. The method is based on scene (road) classification which provides the probability that an image contains certain type of road geometry (straight, left/right curve, etc.). During the training of the classifier a road...
This article focuses on the problem of terrain classification from aerial imagery with the intention to increase unmanned ground vehicle (UGV) road and off-road performance by providing means to analyze data from unmanned aerial vehicle (UAV).
This paper describes a method for classifying road signs based on a single color camera mounted on a moving vehicle. The main focus will be on the final neural network based classification stage of the candidates provided by an existing traffic sign detection algorithm. Great attention is paid to image preprocessing in order to provide a more simple and clear input to the network: candidate color...
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