There are many applications for computer vision where a scene observed contains a wide range of brightness. Often, the low dynamic range of a camera limits the accuracy of information that can be extracted from the video. Frames may contain saturated pixels of bright targets, poor resolution and noisy data for dark regions, or both. In this paper, we propose a method for generating high dynamic range (HDR) videos by combining successive frames. The first phase is to set the exposures for each frame contributing to one HDR frame. The exposures are automatically adapted according to image contents to provide a maximum amount of information about the target. HDR frames are then combined in a maximum likelihood manner, based on the noise model of image acquisition. Experiments for texture based classification show that utilizing a proposed methodology, even the HDR videos built in real-time, contribute to many topical vision systems.