Shape-from-focus (SFF) uses a sequence of space-variantly defocused observations captured with relative motion between camera and scene. It assumes that there is no motion parallax in the frames. This is a restriction and constrains the working environment. Moreover, SFF cannot recover the structure information when there are missing data in the frames due to CCD sensor damage or unavoidable occlusions. The capability of fillingin plausible information in regions devoid of data is of critical importance in many applications. Images of 3D scenes captured by off-the-shelf cameras with relative motion commonly exhibit parallax-induced pixel motion. We demonstrate the interesting possibility of exploiting motion parallax cue in the images captured in SFF with a practical camera to jointly inpaint the focused image and depth map. © 2010 Optical Society of America.

A. N. Rajagopalan

Department of Electrical Engineering

Astrophysics

Cameras

Geometrical optics

3D SCENES

CCD SENSORS

depth map

FILLING IN

Missing data

MOTION PARALLAX

RELATIVE MOTION

SHAPE FROM FOCUS

STRUCTURE INFORMATION

Working environment

three dimensional

Fulltext

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

Joint image and depth completion in shape-from-focus: Taking a cue from parallax

Journal of the Optical Society of America A: Optics and Image Science, and Vision

In this paper, we extend traditional Shape from focus (SFF) to the underwater scenario. Specifically, we show how 3D shape of objects immersed in water can be extracted from images captured under an optical microscope. Traditional SFF employs telecentric optics to achieve geometric registration among the frames. We extend conventional SFF to underwater objects under the assumption of negligible scattering. We establish that the property of telecentricity holds for objects immersed in water provided the numerical aperture is small. By modeling geometrical distortions due to refraction effects on the water surface, we prove that the depth map obtained in the presence of water is a scaled version of the original depth map. We also reveal that this scale factor is directly related to the refractive index of water. We validate performance with real experiments. © 2014 IEEE.

Proceedings - International Conference on Pattern Recognition

Underwater microscopic shape from focus

Depth maps captured by range scanning devices or by using optical cameras often suffer from missing regions due to occlusions, reflectivity, limited scanning area, sensor imperfections, etc. In this paper, we propose a fast and reliable algorithm for depth map inpainting using the tensor voting (TV) framework. For less complex missing regions, local edge and depth information is utilized for synthesizing missing values. The depth variations are modeled by local planes using 3D TV, and missing values are estimated using plane equations. For large and complex missing regions, we collect and evaluate depth estimates from self-similar (training) datasets. We align the depth maps of the training set with the target (defective) depth map and evaluate the goodness of depth estimates among candidate values using 3D TV. We demonstrate the effectiveness of the proposed approaches on real as well as synthetic data. © 2013 Optical Society of America.

Depth inpainting by tensor voting

Traditional depth estimation methods typically exploit the effect of either the variations in internal parameters such as aperture and focus (as in depth from defocus), or variations in extrinsic parameters such as position and orientation of the camera (as in stereo).When operating off-the-shelf (OTS) cameras in a general setting, these parameters influence the depth of field (DOF) and field of view (FOV). While DOF mandates one to deal with defocus blur, a larger FOV necessitates camera motion during image acquisition. As a result, for unfettered operation of an OTS camera, it becomes inevitable to account for pixel motion as well as optical defocus blur in the captured images. We propose a depth estimation framework using calibrated images captured under general camera motion and lens parameter variations. Our formulation seeks to generalize the constrained areas of stereo and shape from defocus (SFD)/focus (SFF) by handling, in tandem, various effects such as focus variation, zoom, parallax and stereo occlusions, all under one roof. One of the associated challenges in such an unrestrained scenario is the problem of removing user-defined foreground occluders in the reference depth map and image (termed inpainting of depth and image). Inpainting is achieved by exploiting the cue from motion parallax to discover (in other images) the correspondence/color information missing in the reference image. Moreover, considering the fact that the observations could be differently blurred, it is important to ensure that the degree of defocus in the missing regions (in the reference image) is coherent with the local neighbours (defocus inpainting). © 2011 Springer Science+Business Media, LLC.

International Journal of Computer Vision

Towards unrestrained depth inference with coherent occlusion filling

Shape from focus is an elegant method that estimates the structure of a 3D object from a video of captured frames using the degree of focus as the principal cue. However, the quality of the estimated structure is vulnerable to scene texture. The effect is particularly pronounced for objects that are smooth relative to the magnification of the optical system. In this paper, the shape estimation process is cast as an inverse problem. We exploit spatial dependencies by modeling the shape of the object with a discontinuity-adaptive Markov random field wherein the focus measure profile is used to judiciously control the degree of smoothness. The 3D information is obtained by minimizing a suitably derived energy function that preserves fine details of the underlying structure. We show by experimentation on several real-world specimens that our method yields state-of-the-art performance. © 2011 The Author Journal of Microscopy © 2011 Royal Microscopical Society.

Journal of Microscopy

Shape extraction of low-textured objects in video microscopy

Shape from focus (SFF) estimates the depth profile of a 3D object using a sequence of observations. Due to the finite depth of field of real aperture cameras and the 3D nature of the object, none of the observations is completely in focus. However, in many applications, it is important to examine finer image details of the underlying object in conjunction with the depth map. We propose an extension to the traditional SFF method to optimally estimate a high-resolution image of the 3D object, given the low-resolution observations and the depth map derived from traditional SFF. Using the observation stack, we show that it is possible to achieve significant improvement in resolution. We also analyze the special case of region of interest superresolution and show analytically that an optional interframe separation exists for which the quality of the estimated high-resolution image is the best. © 2007 Optical Society of America.

Extension of the shape from focus method for reconstruction of high-resolution images

Entropy Minimization for Shadow Removal

Confocal Stereo

2008 IEEE Conference on Computer Vision and Pattern Recognition

Stereoscopic inpainting: Joint color and depth completion from stereo images

2008 3DTV Conference: The True Vision - Capture, Transmission and Display of 3D Video

Augmented 3D Endoscopy Video

Joint Image and depth completion in shape-from-focus: Taking a cue from parallax

Journal	Data powered by TypesetJournal of the Optical Society of America A: Optics and Image Science, and Vision
Publisher	Data powered by TypesetOSA - The Optical Society
ISSN	10847529
Impact Factor	1.230
Open Access	Yes
Citation Style	unsrt
Sherpa RoMEO Archiving Policy	Green