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Traffic Signs provide drivers with very valuable information about the road, in order to make driving safer and easier. They are designed to be easily recognized by human drivers mainly because their color and shape are very different from natural environments. Automatic traffic sign detection and recognition is important in the development of unmanned vehicles, and is expected to provide information...
In this paper we study the problem of rotation invariant texture classifications with three methods: Gabor wavelet filter, local binary pattern operators (LBP) and co-occurrence matrices (GLCM). A multiclass Support Vector Machines (SVM) is used as a classifier. Two different databases are used: Brodatz and Outex to bring out a comparative study about the discrimination capabilities of those different...
In this article a construction of hybrid colour space from a set of classic colours spaces by using the algorithm MRMR is proposed. This algorithm is based on the mutual information. Our approach is evaluated on SPOT HRV (XS) image representing two forest areas in the region of Rabat. Feature extraction is done by the cooccurrence matrix. The SVM (Support Vectors Machine) classifier is used.
In this paper, it is intended to accurately separate pixels related to two spectrally similar classes of building and road in Shiraz urban area. To achieve this goal, Support Vector Machine (SVM) classification algorithm has been applied to a Landsat ETM+ image of Shiraz City. In order to assess the accuracy of the results, Maximum Likelihood Classification (MLC) as an approved and conventional algorithm...
In this paper we present a novel LSB matching steganalysis method based on feature vectors derived from co-occurrence matrix in spatial domain, which is sensitive to data embedding process. This matrix is derived from an image that some of its most significant bit planes are removed. By this preprocessing in addition to decrease the size of feature vector also preserve effects of embedding. We investigate...
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