Shape from focus (SFF), which relies on image focus as a cue within sequenced images, represents a passive technique in recovering object shapes in scenes. Although numerous methods have been recently proposed, less attention has been paid to particular factors affecting them. In regard to SFF, one such critical factor impacting system application is the total number of images. A large data set requires a huge amount of computation power, whereas decreasing the number of images causes shape reconstruction to be crude and erroneous. The total number of images is inversely proportional to interframe distance or sampling step size. In this paper, interframe distance (or sampling step size) criteria for SFF systems have been formulated. In particular, light ray focusing is approximated by the use of a Gaussian beam followed by the formulation of a sampling expression using Nyquist sampling. Consequently, a fitting function for focus curves is also obtained. Experiments are performed on simulated and real objects to validate the proposed schemes.

Although numerous methods have been recently proposed in recovering shape from image focus, all follow similar precedent of focus measure application after image acquisition and depth is recovered by maximizing the focus curves obtained via this application. This is followed by utilizing approximation techniques to refine the initial depth map. In this paper we have followed an unorthodox approach to recover object shapes using image focus as cue, not following the conventional manner. At first the focused image is obtained, pursued by computing depth along the edges and their neighbors present in scene. Empty spaces in the final depth map are then calculated by surface interpolation. Experimentation and results are then discussed to support the proposed scheme.

In the past recent years, shape recovery using image focus has been investigated a lot. Traditionally, generalized Gaussian models are used to approximate the focus curves obtained after applying the focus measure. In this paper, we have proposed a new approximation model for the focus curve, using Lorentzian-Cauchy function. The focusing of light rays, by the imaging device, are approximated by using a Gaussian beam, which has the intensity profile along the propagation axis defined by Lorentzian-Cauchy function. Experiments performed on the simulated and real objects have shown the effectiveness of the proposed scheme.

Estimating 3D shape of the object is an important research topic in the area of computer vision, with a wide range of applications. This paper introduces a new method for focused-based passive methods (like SFF) to approximate the 3D shape of the object using Bezier surface. The discrete nature of image sampling results in the loss of information between two consecutive images. Conventional approximation methods optimize or interpolate focus values. We have suggested interpolating depth values instead of focus value over a small patch on the object surface. The method approximates the surface more accurately and also reduces any noise caused by focus measure. The proposed method is tested and analyzed to demonstrate the effectiveness against traditional methods.

In the area of machine vision, shadows occur frequently in a wide variety of scenes. An inherent weakness of focus-based methods is that they require the imaged scene to have significant texture. In many real-world applications, surfaces can be smoothly shaded and lacking in detectable texture. In such cases, Shape-Form-Focus (SFF) generates inaccurate and sparse depth maps. For accurate calculation of depth map, we consider the affects of illumination. In SFF the images are taken with one light source and the shadows occur in each frame. Thus, the more bright regions of the images give more accurate depth map, whereas, the less bright regions give less accurate depth map. We have also shown the results and compared them with the previous results. From simulation results we see that the results improved with the proposed algorithm.