robust watermarking method for stereo-pair images

The wide-baseline stereo problem, i.e. the problem of establishing correspon-dences between a pair of images taken from different viewpoints is studied. A new set of image elements that are put into correspondence, the so called extremal regions, is introduced. Extremal regions possess highly de

developing a dynamic 3D whole body scanner. The basic concept is to equip a studio space such that the "working volume" of the space is imaged from all directions using fixed stereo-pairs of TV cameras. The stereo-pair images, collected by the camera pairs at a frame rate of 25 Hz

developing a dynamic 3D whole body scanner. The basic concept is to equip a studio space such that the "working volume" of the space is imaged from all directions using fixed stereo-pairs of TV cameras. The stereo-pair images, collected by the camera pairs at a frame rate of 25 Hz

A stereo vision system for detecting 3D-objects is presented. The method uses a pair of externally uncalibrated cameras and is able to detect objects located on a staircase. Detection is based on differencing stereo pair images, where a warping transform is used to overlay the left onto the right

Introduction and motivations Given a pair of stereo images, rectification determines a transformation of each image plane such that pairs of conjugate epipolar lines become collinear and parallel to one of the image axes (usually the horizontal one). The rectified images can be thought of as acquired by a new

of these straight linear features or directions in determining the 3D ground coordinates of some object points, which are not presented in the overlap area of a certain stereo-pair. The developed approach deals with the image of the interest point in the first photo only, along with a specified direction, on which

high-complexity stereo image pairs with pixel-accurate correspondence information using structured light. Unlike traditional range-sensing approaches, our method does not require the calibration of the light sources and yields registered disparity maps between all pairs of cameras and illumination

matching between stereo pair images.. These techniques allow us to automatically produce seamless, highly accurate digital elevation models from multiple stereo image pairs while significantly reducing the influence of image noise. Our technique is demonstrated on a set of 71 high resolution scanned images

database (normalized-uniform version), which contains stereo-pair images of objects under different lighting conditions and viewpoints. Our model achieves 6.5 % error on the test set, which is close to the best published result for NORB (5.9%) using a convolutional neural net that has built-in knowledge