A nonlinear Transform Based Three-Dimensional Shape Recovery from Image Focus

The use of intelligent and sophisticated approaches in the domain of computer vision and pattern recognition is consistently increasing. This paper introduces a novel machine learning based approach for Shape From Focus (SFF), where the in-focus pixels are selected from a sequence of images. In contrast to computing focus values directly in spatial or transform domain, the proposed method first nonlinearly transforms the input space into feature space and then computes the focus value by transforming the data into eigenspace. First, the nonlinear transformation is performed by using kernel function and then Principal Component Analysis (PCA) is applied. This idea is also supported by the fact that out-of-focus is analogous to blurring and is a nonlinear phenomenon. An initial depth map is computed by maximizing the focus measure. To further refine the 3D shape, bilateral filter is applied. The proposed method is experimented using synthetic and real image sequences. The results demonstrate the effectiveness and the robustness of the new method.