Range image segmentation using regularization

This paper describes the application of regularization techniques to the problem of segmenting range images. We propose a new energy functional that varies the amount of smoothing according to the gradient of the data. An iterative application of reconstruction using this new functional improves the signal/noise ratio of the noisy input image with good preservation of discontinuities. By employing reconstruction using this new energy functional, the difficulty in applying regularization techniques to the segmentation problem due to smoothing over discontinuities is circumvented. The results indicate that the algorithm performs especially well on noisy range images. Reconstruction using the new energy functional shows the possibility of its application to the problem of image enhancement. An algorithm is described for the detection of zeroth order discontinuities and surface reconstruction. We also discuss how the same algorithm can be applied to detect first order discontinuities and be applied to gradient reconstruction.