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Both intensity and phase information of images have been the most important similarity measures in solving the general stereo matching problem. Intensity contains most of the imaging information of the scene/object, yet the phase information could reflect the local structure of images, which is more robust than the grayscale value. Plenty of work has been done in intensity-based or phase-based stereo matching methods. However, neither of them could work well enough when process images were taken under varied illuminations. A robust depth recovery method by making use of both intensity and phase information of stereo images properly is proposed. Firstly, 2D signal analysis is conducted by using the multiscale monogenic wavelet transform, from which local phase and intensity amplitude information are extracted into different scales. Secondly, disparity maps are estimated in different scales based on the intensity information. Thirdly, the optimal disparity is obtained by weighted-combining the disparity maps in different scales. The weighted coefficients are computed by making use of the phase information. Extensive experimental evaluation demonstrates the benefits of the proposed method. [ABSTRACT FROM AUTHOR]

A structured light system simplifies three-dimensional reconstruction by illuminating a specially designed pattern to the target object, thereby generating a distinct texture on it for imaging and further processing. Success of the system hinges upon what features are to be coded in the projected pattern, extracted in the captured image, and matched between the projector's display panel and the camera's image plane. The codes have to be such that they are largely preserved in the image data upon illumination from the projector, reflection from the target object, and projective distortion in the imaging process. The features also need to be reliably extracted in the image domain. In this article, a two-dimensional pseudorandom pattern consisting of rhombic color elements is proposed, and the grid points between the pattern elements are chosen as the feature points. We describe how a type classification of the grid points plus the pseudorandomness of the projected pattern can equip each grid point with a unique label that is preserved in the captured image. We also present a grid point detector that extracts the grid points without the need of segmenting the pattern elements, and that localizes the grid points in subpixel accuracy. Extensive experiments are presented to illustrate that, with the proposed pattern feature definition and feature detector, more features points in higher accuracy can be reconstructed in comparison with the existing pseudorandomly encoded structured light systems. [ABSTRACT FROM AUTHOR]