Gaussian Mixture Model (GMM) is a widely used approach for the background subtraction and the moving objects detection. However, the classical GMM probably detects incorrectly and cannot deal with the shadows with a pixel-level and time-domain classification, and thus it cannot monitor the water surface floats effectively. To solve this problem, an improved GMM-based automatic segmentation method (IGASM) is proposed to detect the water surface floats in this paper, where the background updating strategy is improved to segment the water surface floats more effectively. Besides, the GMM results are mapped into an HSV color space, and a light-shadow discriminant function is applied to solve the problems of light and shadow. Then, a morphological method is used to smooth the extracted foregrounds. Finally, Graph Cuts algorithm is applied to optimized the segmentation results according to the spatial information of video images. Experimental results demonstrate that IGASM can detect the water surface floats quickly and accurately, and the influences of light, shadows and ripples of water surface can be eliminated as much as possible. INDEX TERMS Background subtraction, Gaussian mixture model, graph cuts, light-shadow discriminant function, video segmentation, water surface floats.

The technique of vehicle license plate recognition can recognize and count the vehicles automatically, and thus many applications regarding the vehicles are greatly facilitated. However, the recognitions of vehicle license plates are extremely difficult especially in some fog‐haze environments because the fog and haze blur the boundaries and characters of license plates significantly, which makes the license plates hard to be detected or recognised. To this end, this paper proposes a vehicle License Plate Recognition method for Fog‐Haze environments (LPRFH). In LPRFH, a dark channel prior algorithm based on the local estimation of atmospheric light value is applied to dehaze the blurred images preliminarily. Then, the images are further dehazed, and the license plate regions are detected through a Joint Further‐dehazing and Region‐extracting Model on basis of an object detection convolution neural network. Finally, the image super‐resolution is accomplished with a convolution‐enhanced super‐resolution convolutional neural network, and hence the characters of license plates can be recognised successfully. Extensive experiments have been conducted, and the results indicate that LPRFH can recognise the license plates accurately even in some severe fog‐haze environments.

Abstract. Cloud computing service is a new computing paradigm which consists of distributed and large scale computing resources. Effective classification managements for the resources is necessary. In this paper, we describe some concept and principle about classification, and present a classification algorithm based on non-uniform granularity. Experiments results carried on the blog posts illustrate the effectiveness of the new algorithm.

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