Reliability measure for shape-from-focus

Shape-from-focus (SFF) is a passive technique widely used in image processing for obtaining depth-maps. This technique is attractive since it only requires a single monocular camera with focus control, thus avoiding correspondence problems typically found in stereo, as well as more expensive capturing devices. However, one of its main drawbacks is its poor performance when the change in the focus level is difficult to detect. Most research in SFF has focused on improving the accuracy of the depth estimation. Less attention has been paid to the problem of providing quality measures in order to predict the performance of SFF without prior knowledge of the recovered scene. This paper proposes a reliability measure aimed at assessing the quality of the depth-map obtained using SFF. The proposed reliability measure (the R-measure) analyzes the shape of the focus measure function and estimates the likelihood of obtaining an accurate depth estimation without any previous knowledge of the recovered scene. The proposed R-measure is then applied for determining the image regions where SFF will not perform correctly in order to discard them. Experiments with both synthetic and real scenes are presented.

[1]  Shree K. Nayar,et al.  Real-Time Focus Range Sensor , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Masahiro Watanabe,et al.  Telecentric Optics for Constant-Magnification Imaging , 1995 .

[3]  Domenec Puig,et al.  Analysis of focus measure operators for shape-from-focus , 2013, Pattern Recognit..

[4]  R. Tibshirani,et al.  Additive Logistic Regression : a Statistical View ofBoostingJerome , 1998 .

[5]  Tae-Sun Choi,et al.  Sampling for Shape from Focus in Optical Microscopy , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Shree K. Nayar,et al.  Are textureless scenes recoverable? , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[7]  Jean-Michel Morel,et al.  A non-local algorithm for image denoising , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[8]  Alexey Ignatenko,et al.  Robust Shape from Focus via Markov Random Fields , 2009 .

[9]  B. S. Manjunath,et al.  Texture Features for Browsing and Retrieval of Image Data , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Tae-Sun Choi,et al.  Shape From Focus Using Optimization Technique , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[11]  A. N. Rajagopalan,et al.  Improving Shape From Focus Using Defocus Cue , 2007, IEEE Transactions on Image Processing.

[12]  Jean-Michel Morel,et al.  A Review of Image Denoising Algorithms, with a New One , 2005, Multiscale Model. Simul..

[13]  Gabriel Thomas,et al.  All-in-Focus Imaging Using a Series of Images on Different Focal Planes , 2005, ICIAR.

[14]  K. Shirai,et al.  Shape from Focus using Color Segmentation and Bilateral Filter , 2006, 2006 IEEE 12th Digital Signal Processing Workshop & 4th IEEE Signal Processing Education Workshop.

[15]  Tae-Sun Choi,et al.  Shape from focus using multilayer feedforward neural networks , 2000, IEEE Trans. Image Process..

[16]  Roberto Manduchi,et al.  Bilateral filtering for gray and color images , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[17]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[18]  Dimitri Van De Ville,et al.  Model-Based 2.5-D Deconvolution for Extended Depth of Field in Brightfield Microscopy , 2008, IEEE Transactions on Image Processing.

[19]  Tae-Sun Choi,et al.  An unorthodox approach towards shape from focus , 2011, 2011 18th IEEE International Conference on Image Processing.

[20]  Helder Araújo,et al.  Depth recovery using active focus in robotics (vision) , 1991, Proceedings IROS '91:IEEE/RSJ International Workshop on Intelligent Robots and Systems '91.

[21]  Bernd Jähne,et al.  Practical handbook on image processing for scientific applications , 1997 .

[22]  Tae-Sun Choi,et al.  Focusing techniques , 1992, Other Conferences.

[23]  Andrea Fusiello,et al.  Generation of All-in-Focus Images by Noise-Robust Selective Fusion of Limited Depth-of-Field Images , 2013, IEEE Transactions on Image Processing.

[24]  Homer H. Chen,et al.  Reciprocal Focus Profile , 2012, IEEE Transactions on Image Processing.

[25]  A. N. Rajagopalan,et al.  Improving Shape from Focus Using Defocus Information , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[26]  Muralidhara Subbarao,et al.  Accurate Recovery of Three-Dimensional Shape from Image Focus , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[27]  Lai-Man Po,et al.  A new multidirectional extrapolation hole-filling method for Depth-Image-Based Rendering , 2011, 2011 18th IEEE International Conference on Image Processing.

[28]  Shree K. Nayar,et al.  Shape from Focus , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[29]  Kazuya Kodama,et al.  Simple and Fast All-in-Focus Image Reconstruction Based on Three-Dimensional/Two-Dimensional Transform and Filtering , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[30]  Q. M. Jonathan Wu,et al.  Shape from focus using fast discrete curvelet transform , 2011, Pattern Recognit..

[31]  Zhengyou Zhang,et al.  Flexible camera calibration by viewing a plane from unknown orientations , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[32]  Jing Tian,et al.  Multi-focus image fusion using wavelet-domain statistics , 2010, 2010 IEEE International Conference on Image Processing.

[33]  Marcelo H. Ang,et al.  Practical issues in pixel-based autofocusing for machine vision , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[34]  Earl Wong A New Method for Creating a Depth Map for Camera Auto Focus Using an All in Focus Picture and 2D Scale Space Matching , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[35]  De Xu,et al.  An improved focus measure for MEMS assembly , 2005, IEEE International Conference Mechatronics and Automation, 2005.

[36]  Kuo-Young Cheng,et al.  A Sharpness-Dependent Filter for Recovering Sharp Features in Repaired 3D Mesh Models , 2008, IEEE Transactions on Visualization and Computer Graphics.

[37]  Xiaoyan Hu,et al.  A Quantitative Evaluation of Confidence Measures for Stereo Vision , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[38]  Michael Unser,et al.  Complex wavelets for extended depth‐of‐field: A new method for the fusion of multichannel microscopy images , 2004, Microscopy research and technique.

[39]  Katsushi Ikeuchi,et al.  Hole Filling of a 3D Model by Flipping Signs of a Signed Distance Field in Adaptive Resolution , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[40]  Tae-Sun Choi,et al.  Recovering 3D Shape of Weak Textured Surfaces , 2009, 2009 International Conference on Computational Science and Its Applications.

[41]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.

[42]  Huei-Yung Lin,et al.  A vision system for fast 3D model reconstruction , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[43]  Guillermo Sapiro,et al.  Simultaneous structure and texture image inpainting , 2003, IEEE Trans. Image Process..