Sharpness Matching in Stereo Images

When stereo images are captured under less than ideal conditions, there may be inconsistencies between the two images in brightness, contrast, blurring, etc. When stereo matching is performed between the images, these variations can greatly reduce the quality of the resulting depth map. In this paper we propose a method for correcting sharpness variations in stereo image pairs which is performed as a pre-processing step to stereo matching. Our method is based on scaling the 2D discrete cosine transform (DCT) coefficients of both images so that the two images have the same amount of energy in each of a set of frequency bands. Experiments show that applying the proposed correction method can greatly improve the disparity map quality when one image in a stereo pair is more blurred than the other.

Interactive Augmentation of Live Images using a HDR Stereo Camera

Adding virtual objects to real environments plays an important role in todays computer graphics: Typical examples are virtual furniture in a real room and virtual characters in real movies. For a believable appearance, consistent lighting of the virtual objects is required. We present an augmented reality system that displays virtual objects with consistent illumination and shadows in the image of a simple webcam. We use two high dynamic range video cameras with fisheye lenses permanently recording the environment illumination. A sampling algorithm selects a few bright parts in one of the wide angle images and the corresponding points in the second camera image. The 3D position can then be calculated using epipolar geometry. Finally, the selected point lights are used in a multi pass algorithm to draw the virtual object with shadows. To validate our approach, we compare the appearance and shadows of the synthetic objects with real objects.