Accurate Recovery of Three-Dimensional Shape from Image Focus
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Observing Shape from Defocused Images
ICIAP '99 Proceedings of the 10th International Conference on Image Analysis and Processing
Dynamic Programming
Three-dimensional shape recovery from focused image surface
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06
Shape retrieval based on dynamic programming
IEEE Transactions on Image Processing
Shape from focus using multilayer feedforward neural networks
IEEE Transactions on Image Processing
A heuristic approach for finding best focused shape
IEEE Transactions on Circuits and Systems for Video Technology
DCT and PCA Based Method for Shape from Focus
ICCSA '08 Proceedings of the international conference on Computational Science and Its Applications, Part II
Depth Estimation by Finding Best Focused Points Using Line Fitting
ICISP '08 Proceedings of the 3rd international conference on Image and Signal Processing
HK Segmentation of 3D Micro-structures Reconstructed from Focus
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues
Comparison of polymers: a new application of shape from focus
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
3D shape recovery from image focus using kernel regression in eigenspace
Image and Vision Computing
Noise analysis for depth estimation
BVAI'07 Proceedings of the 2nd international conference on Advances in brain, vision and artificial intelligence
Accurate 3D shape estimation based on combinatorial optimization
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A novel iterative shape from focus algorithm based on combinatorial optimization
Pattern Recognition
Shape from focus using fast discrete curvelet transform
Pattern Recognition
Optimal depth estimation by combining focus measures using genetic programming
Information Sciences: an International Journal
Automatic micro-manipulation based on visual servoing
ICIRA'10 Proceedings of the Third international conference on Intelligent robotics and applications - Volume Part I
A novel algorithm for segmentation of lung images
ISBMDA'06 Proceedings of the 7th international conference on Biological and Medical Data Analysis
Intelligent reversible watermarking and authentication: Hiding depth map information for 3D cameras
Information Sciences: an International Journal
3D shape from focus using LULU operators
ACIVS'12 Proceedings of the 14th international conference on Advanced Concepts for Intelligent Vision Systems
Analysis of focus measure operators for shape-from-focus
Pattern Recognition
3D shape from focus using LULU operators and discrete pulse transform in the presence of noise
Journal of Visual Communication and Image Representation
A comparison of contrast measurements in passive autofocus systems for low contrast images
Multimedia Tools and Applications
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Obtaining an accurate and precise depth map is the ultimate goal for 3D shape recovery. For depth map estimation, one of the most vital parts is the initial selection of the focus measure and processing the images with the selected focus measure. Although, many focus measures have been proposed in the literature but not much attention has been paid to the factors affecting those focus measures as well as the manner the images are processed with those focus measures. In this paper, for accurate calculation of depth map, we consider the effects of illumination on the depth map as well as the selection of the window size for application of the focus measures. The resulting depth map can further be used in techniques and algorithms leading to recovery of three-dimensional structure of the object which is required in many high-level vision applications. It is shown that the illumination effects can directly result in incorrect estimation of depth map if proper window size is not selected during focus measure computation. Further, it is shown that the images need some kind of pre-processing to enhance the dark regions and shadows in the image. For this purpose, an adaptive enhancement algorithm is proposed for pre-processing. In this paper, we prove that without such pre-processing for image enhancement and without the use of proper window size for the estimation of depth maps, it is not possible to obtain the accurate depth map.