Vehicle identification based on image processing is the basic key behind this paper. This technology has been obtained world wide attention nowadays due to low cost,flexibility,ease of access,potential towards collision avoidance and accuracy. In most cases the vehicles are identified on the basis of colour,texture,histogram,hue,saturation,contrast etc. Gabor filter obtained from Gaussian filters are mainly used in image processing due to its better performance. But the main drawback of Gabor filters is related to the frequency response. The bandwidth is limited to reduce the DC noise components. Moreover filter banks have to be used. So a novel idea called Log Gabor filter has been suggested to overcome the drawbacks. Log Gabor filters are designed as Gaussian functions on log axis, which is in fact spatial frequency response of visual neurons. The result expected is that the frequency response concentrates on both lower and higher frequencies It helps to represent uneven frequency content of the image and redundancy of lower frequencies will be reduced. In this paper,a comparison between Gabor and Log Gabor filter is proposed. The classification is done using SVM and neural networks.

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Gabor Filters log Gabor Filters svm neural Networks