Texture is an important spatial feature useful for identifying objects or regions of interest in an image. The present paper derives a new set of texture features, which are morphological shape components derived from the fuzzy texture elements of a 3x3 mask. The proposed fuzzy texture element patterns (FTP's) extract textural information of an image with a more complete respect of texture characteristics in all the eight directions instead of only one displacement vector. The proposed FTP's retains discriminating power of texture elements. In the present paper, five simple morphological shape components are evaluated on each of the derived FTP. The experimental results on the five groups of texture images clearly show the efficacy and simplicity of the present method.
In this paper, a classification method of four moving objects including car, people, motorcycle and bicycle in surveillance video was presented by using machine learning idea. The method can be described as three steps: feature selection, training of Support Vector Machine(SVM) classifier and performance evaluation. Firstly, a feature vector to represent the discriminabilty of an object is described. From the profile of object, the ratio of width to height and trisection ratio of width to height are firstly adopted as the distinct feature. Moreover, we use external rectangle to approximate the object mask, which leads to a feature of rectangle degree standing for the ratio between the area of object to the area of external rectangle. To cope with the invariance to scale, rotation and so on, Hu moment invariants, Fourier descriptor and dispersedness were extracted as another kind of features. Secondly, a multi-class classifier were designed based on two-class SVM. The idea behind the classifier structure is that the multi-class classification can be converted to the combination of two-class classification. For our case, the final classification is the vote result of six twoclass classifier. Thirdly, we determine the precise feature selection by experiments. According to the classification result, we select different features for each two-class classifier. The true positive rate, false positive rate and discriminative index are taken to evaluate the performance of the classifier. Experimental results show that the classifier achieves good classification precision for the real and test data.
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