Motion stereo using ego-motion complex logarithmic mapping
IEEE Transactions on Pattern Analysis and Machine Intelligence
A New Sense for Depth of Field
IEEE Transactions on Pattern Analysis and Machine Intelligence
Camera Geometries for Image Matching in 3-D Machine Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Markov random field modeling in computer vision
Markov random field modeling in computer vision
Telecentric Optics for Focus Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recovering Affine Motion and Defocus Blur Simultaneously
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimation of motion parameters from blurred images
Pattern Recognition Letters
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Variational Approach to Shape from Defocus
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Depth Estimation and Image Restoration Using Defocused Stereo Pairs
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Geometric Approach to Shape from Defocus
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Graph Cut Algorithm for Generalized Image Deconvolution
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Projection defocus analysis for scene capture and image display
ACM SIGGRAPH 2006 Papers
Minimizing Nonsubmodular Functions with Graph Cuts-A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image and depth from a conventional camera with a coded aperture
ACM SIGGRAPH 2007 papers
Shape from Defocus via Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Towards Unrestrained Depth Inference with Coherent Occlusion Filling
International Journal of Computer Vision
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When there is relative motion along the optical axis between a real-aperture camera and a 3D scene, the sequence of images captured will not only be space-variantly defocused but will also exhibit pixel motion due to motion parallax. Existing single viewpoint techniques such as shape-from-focus (SFF)/depth-from-defocus (DFD) and axial stereo operate in mutually exclusive domains. SFF and DFD assume no pixel motion and use the focus and defocus information, respectively, to recover structure. Axial stereo, on the other hand, assumes a pinhole camera and uses the disparity cue to infer depth. We show that in real-aperture axial stereo, both blur and pixel motion are tightly coupled to the underlying shape of the object. We propose an algorithm which fuses the twin cues of defocus and parallax for recovering 3D structure. The effectiveness of the proposed method is validated with many examples.