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Noise can significantly impact the effectiveness of video processing algorithms. This paper proposes a fast white-noise variance estimation that is reliable even in images with large textured areas. This method finds intensity-homogeneous blocks first and then estimates the noise variance in these blocks, taking image structure into account This paper proposes a new measure to determine homogeneous blocks and a new structure analyzer for rejecting blocks with structure. This analyzer is based on high-pass operators and special masks for corners to stabilize the homogeneity estimation. For typical video quality (PSNR of 29-40 dB), the proposed method outperforms other methods significantly and the worst-case estimation error is 3 dB, which is suitable for real applications such as video broadcasts. The method performs well both in highly noisy and good-quality images. It also works well in images including few uniform blocks. [ABSTRACT FROM AUTHOR]

This paper addresses the automatic analysis of court-net sports video content. We extract information about the players, the playing-field in a bottom-up way until we reach scene-level semantic concepts. Each part of our framework is general, so that the system is applicable to several kinds of sports. A central point in our framework is a camera calibration module that relates the a-priori information of the geometric layout in the form of a court model to the input image. Exploiting this information, several novel algorithms are proposed, including playing-frame detection, players segmentation and tracking. To address the player-occlusion problem, we model the contour map of the player silhouettes using a nonlinear regression algorithm, which enables to locate the players during the occlusions caused by players in the same team. Additionally, a Bayesian-based classifier helps to recognize predefined key events, where the input is a number of real-world visual features. We illustrate the performance and efficiency of the proposed system by evaluating it for a variety of sports videos containing badminton, tennis and volleyball, and we show that our algorithm can operate with more than 91% feature detection accuracy and 90% event detection. [ABSTRACT FROM AUTHOR]