Robust depth-from-defocus for autofocusing in the presence of image shifts

Kang, Younsik; Tu, Xue; Dutta, S.; Subbarao, Murali

doi:10.1117/12.792769

Cited by 3 publications

(2 citation statements)

References 7 publications

(23 reference statements)

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“…Focus shift methods use multiple image planes, each focused at a different distance or depth. [5][6][7][8][9][10][11] The 3D data can be based upon either noting a pattern projected onto the part, or just using the surface texture of the part if that texture is evident enough, such as the ridges on fingerprints. In either case, multiple images with a shallow depth are captured, then analyzed by one of the two methods.…”

Section: Non-contact Fingerprint Collection Approachesmentioning

confidence: 99%

Mobile, contactless, single-shot, fingerprint capture system

et al. 2010

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In some applications such as field stations, disaster situations or similar conditions, it is desirable to have a contactless, rugged means to collect fingerprint information. The approach described in this paper enables acceleration of the capture process by eliminating an otherwise system and finger cleanup procedure, minimizes the chance of the spread of disease or contaminations, and uses an innovative optical system able to provide rolled equivalent fingerprint information desirable for reliable 2D matching against existing databases. The approach described captures highresolution fingerprints and 3D information simultaneously using a single camera. Liquid crystal polarization rotators combined with birefringent elements provide the focus shift and a depth from focus algorithm extracts the 3D data. This imaging technique does not involve any moving parts, thus reducing cost and complexity of the system as well as increasing its robustness. Data collection is expected to take less than 100 milliseconds, capturing all four-finger images simultaneously to avoid sequencing errors. This paper describes the various options considered for contactless fingerprint capture, and why the particular approach was ultimately chosen.

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Section: Non-contact Fingerprint Collection Approachesmentioning

confidence: 99%

Mobile, contactless, single-shot, fingerprint capture system

et al. 2010

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“…[1][2][3][4][5][6][7][8][9][10] The image-based passive autofocus algorithms are widely studied for their high accuracy and no requirement for extra equipment. [1][2][3][4][5][6][7][8][9][10] The image-based passive autofocus algorithms are widely studied for their high accuracy and no requirement for extra equipment.…”

Section: Introductionmentioning

confidence: 99%

Analysis of image motion on autofocus precision for aerial cameras

et al. 2015

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Downloaded From: http://opticalengineering.spiedigitallibrary.org/ on 08/17/2015 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspxAbstract. Aerial cameras often suffer from image motion due to the severe aerial environments that decrease the autofocus precision. An analysis of the influence of image motion on autofocus precision is provided to obtain the optimal focus position. First, the focus measure function based on discrete Fourier transform is applied to evaluate the sharpness of the collected image for autofocusing. Then, two forms of image motion model, including linear shift and jitter, are discussed and analyzed. Finally, a quadratic fitting curve is employed on the focus measure function for image sequence. The maximum of the fitted curve corresponds to the optimal focus position. Experimental results demonstrate that the maximum value of the focus measure function decreases more than 10% approximately when the image motion increases to five pixels, and the optimal focus position computed by quadratic fitted curve generated a good performance in autofocus techniques. The proposed autofocus algorithm can be applied to aerial cameras.

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