Minimal operator set for passive depth from defocus

• Published in: Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition pp. 431 - 438
• Main Authors: Watanabe, M., Nayar, S.K.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 1996
• Subjects: Computer science; Filters; Focusing; Frequency; Image analysis; Layout; Lenses; Optical imaging; Optical sensors; Production engineering
• ISBN: 9780818672590; 0818672595
• ISSN: 1063-6919
• DOI: 10.1109/CVPR.1996.517108

A fundamental problem in depth from defocus is the measurement of relative defocus between images. We propose a class of broadband operators that, when used together, provide invariance to scene texture and produce accurate and dense depth maps. Since the operators are broadband, a small number of them are sufficient for depth estimation of scenes with complex textural properties. Experiments are conducted on both synthetic and real scenes to evaluate the performance of the proposed operators. The depth detection gain error is less than 1%, irrespective of texture frequency. Depth accuracy is found to be 0.5/spl sim/1.2% of the distance of the object from the imaging optics.