Shape from focus (SFF) is a widely used technique for determining the 3D structure of textured microscopic objects. However, SFF output depends critically on the number of observations used and the focus measure operator adopted. In this paper, we propose a new SFF method that can provide rich structure information given limited number of observations. We observe that depth is non-linearly related to the observations and pose the shape estimation as a minimization problem within a Maximum A Posteriori (MAP) - Markov Random Field (MRF) framework. We incorporate a discontinuity-adaptive MRF prior for the underlying structure. The resulting cost function is non-convex in nature which we minimize using Graduated non-convexity algorithm. When tested on synthetic as well as real objects, the results obtained are quite impressive. © 2009 Springer Berlin Heidelberg.

A. N. Rajagopalan

Department of Electrical Engineering

Krishnamurthy Ramnath

3D structure

FOCUS MEASURE OPERATOR

Markov random field

Maximum a posteriori

MICROSCOPIC OBJECTS

Minimization problems

NONCONVEXITY

REAL OBJECTS

Rich structure

Shape estimation

SHAPE FROM FOCUS

Hidden Markov models

Mathematical operators

Mobile telecommunication systems

Pattern Recognition

Structural Frames

three dimensional

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

Discontinuity-Adaptive Shape from Focus Using a Non-convex Prior

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Space-variantly blurred images of a scene contain valuable depth information. In this paper, our objective is to recover the 3-D structure of a scene from motion blur/optical defocus. In the proposed approach, the difference of blur between two observations is used as a cue for recovering depth, within a recursive state estimation framework. For motion blur, we use an unblurred-blurred image pair. Since the relationship between the observation and the scale factor of the point spread function associated with the depth at a point is nonlinear, we propose and develop a formulation of unscented Kalman filter for depth estimation. There are no restrictions on the shape of the blur kernel. Furthermore, within the same formulation, we address a special and challenging scenario of depth from defocus with translational jitter. The effectiveness of our approach is evaluated on synthetic as well as real data, and its performance is also compared with contemporary techniques. © 1992-2012 IEEE.

IEEE Transactions on Image Processing

Depth from motion and optical blur with an unscented Kalman filter

Discontinuity-Adaptive shape from focus using a non-convex prior

Journal	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN	03029743
Open Access	No