Abstract
Shape from Focus (SFF) has been studied extensively in computer vision for 3D shape and depth recovery. The first stage in SFF methods is to compute the focus value of every pixel by converting the colored images into gray scale and then apply the focus measure operator. Converting colored values in the images into gray scale values may lead to imprecise mapping of pixels with different colored values onto the same gray scale value, this affects the overall accuracy of the system. In a colored image, the focused pixels maintain a considerable color difference from their neighboring pixels as compared to the defocused ones, which are blended into their neighborhood. This article presents an alternative method to measure the degree of focus by directly processing colored images. The color differences of the neighbor pixels with respect to the central pixel are obtained and summed together, this is followed by calculating their spread. The sum and the spread are combined to measure the degree of focus of the pixel in consideration. The proposed focus measure is then used for shape recovery of various simulated and real objects and is compared with previous techniques. The comparison results show the proposed method has the highest correlation and smallest RMSE values confirming the effectiveness of using color images for shape recovery.
Highlights
In computer vision, shape-from-X is a passive monocular technique to recover the 3D geometry of a scene or an object from a set of images
Measuring focus quality is an essential step in the Shape from Focus (SFF) process for depth estimation and 3D shape recovery
Use gray scale images to measure the degree of focus
Summary
Shape-from-X is a passive monocular technique to recover the 3D geometry of a scene or an object from a set of images. X is a 2D characteristic, for example, shading, motion, stereo, focus, defocus, used as a cue to infer the 3D shape. This approach has various industrial applications in diagnostics, autonomous vehicle guidance, microscopy etc., [1]– [3]. SFF measures the amount of focus in each pixel, along the optical axis, to identify the best-focused pixels, which are used to recover the depth of the scene, [5]–[7]. In SFF all the images are taken by either moving the object along the optical axis in small steps, each step of size ∆step; or changing the focus of imaging device in small steps. An image (of dimensions l × m) is stored at each step, and n is the total number of images in the image stack, as shown in Fig. 1, and is given by:
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