Abstract
Shape from Focus (SFF) is one of the passive techniques to recover the shape of an object under consideration. It utilizes the focus cue present in the stack of images, obtained by a single camera. In SFF when the images are acquired, the inter-frame distance, also known as the sampling step size, is assumed to be constant. However, in practice, due to mechanical constraints, sampling step size cannot remain constant. The inconsistency in the sampling step size causes the problem of jitter, and produces Jitter noise in focus curves. This Jitter noise is not visible in images, because each pixel in an image (of the stack) will be subjected to the same error in focus. Thus, traditional image denoising techniques will not work. This paper formulates a model of the Jitter noise, followed by the design of system and measurement models for Kalman filter. Then, the jittering problem for SFF systems is solved using the proposed filtering technique. Experiments are performed on simulated and real objects. Ten noise levels are considered for simulated, and four for real objects. RMSE and Correlation are used to measure the reconstructed shape. The results show the effectiveness of the proposed scheme.
Highlights
Three-dimensional shape recovery using two-dimensional images is a well-established research problem in computer vision applications, robot and machine vision, bio-informatics, medical imaging, consumer cameras, microscopy, and so forth [1]–[6]
All of shape from focus (SFF) methods broadly consist of three main steps; image acquisition, focus measure (FM) application, The associate editor coordinating the review of this manuscript and approving it for publication was Liang Hu
Later the detail analysis of the affects of Jitter noise on SFF is provided at the end of the section
Summary
Three-dimensional shape recovery using two-dimensional images is a well-established research problem in computer vision applications, robot and machine vision, bio-informatics, medical imaging, consumer cameras, microscopy, and so forth [1]–[6]. Jang et al in [28]–[33] considered only symmetric bell-shaped distributions for vibrational noise in translational stage, and their designed measurement model measures only a constant (each step position k) In such case taking the mean of the measurement values on every step position k can provide the similar results.
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