Depth information from image sequences using two-dimensional cepstrum

Currently existing methods for three-dimensional (3-D) reconstruction of an object are computationally intensive and lacking in accuracy. The research work comprising this thesis presents a new motion stereo model that is computationally less demanding and yields more accurate depth information than the existing methods. One of the traditional techniques for extracting depth information is to find the disparities of corresponding points in stereo images following a biological model of 3-D vision. A normal binocular stereo system uses two images to determine which point in one image corresponds to a given point in the other, i.e., to find the disparity between two images. The resolution of the disparity depends on the baseline used. High resolution in disparity is achieved by increasing the baseline and decreasing the window size. Based on this idea, a new motion stereo model using a sequence of a number of images has been developed that can provide accurate depth information that is not available from a stereo vision system.

The disparity, i.e., the translational difference between an image pair, has been computed precisely using a recently developed power cepstrum technique that is more robust and noise tolerant than the usual phase correlation technique. The computation time required by the power cepstrum has been further reduced by using a Hartley-like transform that maps a real-valued sequence to a real-valued spectrum while preserving the useful properties of the Fourier transform.

This new motion stereo vision model matches the corresponding points in two images with several intermediate images to reduce the error in matching from widely different perspectives and uses a Hartley-like transform to compute the power cepstrum for finding the disparities. The depth information extracted from the disparities of a sequence of images by the cepstrum technique is less computationally intensive yet avoids the occlusion problem in a stereo vision model. This new motion stereo model provides a unique method of range data acquisition and visualization of 3-D data.