Research On Thresholding Segmentation For Intelligent Traffic Images

With the increase of vihecles, the intelligent traffic becomes to a new research direction. Applying image processing technology to intelligent traffic system is a challenging field which has great theoretical significance and practical value. This paper concerns on the thresholding segmentation on the images of licese plate, vihecle and road.Firstly, the intelligent traffic techinology is introduced. On the basis of reviewing the method of image thresholding segmemntation, we point out the shortages of the existing algorithms and propose several new algorithms.The two-dimensional Shannon's information entropy is a classical and commonly used image segmentation method, but there are still some disadvantages involved. Then, a two-dimensional exponent information entropy for threshold selection is proposed here, which could overcome the disadvantages in the Shannon's entropy. Meaanwhile, a fast algorithm of two-dimensional exponent entropy thresholding method is also given, which changed the two-dimensional threshold into one-dimensional. The results of the experiment indicate that the proposed algorithm has high speed of calculation and good segmentation quality.The Minimum Within-Cluster Variance algorithm (Otsu) has a good segmentation quality and wide suitable scope, which is actually the Least-Squares algorithm (L2-Norm). Two algorithm for thresholding are proposed in this paper, which are based on Minimum Within-Cluster Absolute Difference (L1-Norm) and Minimum Within-Cluster Maximum Difference (L∞-Norm). And the corresponding two-dimensional algorithms of those two new methods are also presented. The results show that those two new methods have much better perfoemance for some kinds of images, and each of the two-dimensional algorithms is better than its own one-dimensional algorithm.Thresholding algorithm based on entropy is one of the most famous methods. In this paper, two fast recurring two-dimensional Renyi entropic thresholding algorithms, whose computational complexities are both only O(L2), are proposed, while the computational complexity of the original algorithm is O(L4). Experimental results show that these two recurring algorithms can both greatly reduce the processing time of images, which is less than 0.1% of the original algorithm.Baed on the obvious wrong segmentation in the existing two-dimensional histogram vertical segmentation method, a two-dimensional histogram oblique segmentation method is proposed. Then the formula and its fast recursive algorithm of the maximum Shannon entropy thresholding are deduced based on the two-dimensional histogram oblique segmentation. Experimental results show that the proposed method makes the inner part uniform and the edge accurate in the threshold image, and it has a better anti-noise property with the increase of its speed.In this paper, the fish-swarm algorithm of swarm intelligence is also introduced in image thresholding, and the two-dimensional Otsu thresholding algorithm baes on the fish-swarm algorithm is proposed. Comparition with single genetic algorithm and the elitist strategy genetic algorithm shows that this algorithm could select the best threshold accurately with a faster convergent speed.