Abstract
Three-dimensional (3D) shape reconstruction from one or multiple observations is a primary problem of computer vision. Shape from Focus (SFF) is a passive optical method that uses multiple two-dimensional (2D) images with different focus levels. When obtaining 2D images in each step along the optical axis, mechanical vibrations, referred as jitter noise, occur. SFF techniques are vulnerable to jitter noise that can vary focus values in 2D images. In this paper, new filtering method, which provides high accuracy of 3D shape reconstruction and low computational cost, is proposed. First, jitter noise is modeled as Levy distribution. This assumption makes it possible to show the influence of proposed filtering method in real environment with non-Gaussian jitter noise. Second, focus curves are modeled as Gaussian function to compare the performance of proposed filtering method with those of the conventional filtering methods. Finally, improved maximum correntropy criterion Kalman filter (IMCC-KF) is designed as a post-processing step, and is applied to the modeled focus curves. The experiments are performed on real and synthetic objects and the results demonstrate the effectiveness of proposed method.
Highlights
Obtaining the 3D shape of an object from 2D images is a fundamental purpose of research in computer vision
In Shape from Focus (SFF), the object placed on a translational stage, is moved at a constant step size along the optical axis, for capturing a 2D image sequence with different focus settings, [11]
It has been demonstrated that maximum correntropy criterion Kalman filter (MCC-KF) is effective for removing non-Gaussian noise with heavy tails, [27]
Summary
Obtaining the 3D shape of an object from 2D images is a fundamental purpose of research in computer vision. Three dimensional shape recovery methods based on focus are important due to their low computational cost and easy implementation. These methods have many advantages over other 3D shape recovery methods utilizing other cues, as those encounter correspondence problems. In Shape from Focus (SFF), the object placed on a translational stage, is moved at a constant step size along the optical axis, for capturing a 2D image sequence with different focus settings, [11]. This is followed by a focus measure (FM)
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