Active, optical range imaging sensors
Machine Vision and Applications
Implementation of a passive automatic focusing algorithm for digital still camera
IEEE Transactions on Consumer Electronics
3D Target Scale Estimation and Target Feature Separation for Size Preserving Tracking in PTZ Video
International Journal of Computer Vision
Auto-focusing in extreme zoom surveillance: a system approach with application to faces
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part I
A new sampling method of auto focus for voice coil motor in camera modules
PSIVT'06 Proceedings of the First Pacific Rim conference on Advances in Image and Video Technology
Adaptive variance based sharpness computation for low contrast images
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing
The Weibull manifold in low-level image processing: An application to automatic image focusing
Image and Vision Computing
A comparison of contrast measurements in passive autofocus systems for low contrast images
Multimedia Tools and Applications
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This paper presents the development and real-time implementation of a rule-based approach to passive auto-focusing for digital still cameras. The implementation is performed on the processor DM310 which is specifically manufactured by Texas Instruments for digital still cameras. The squared-gradient sharpness function is considered to measure the amount of high-frequency content in an out-of-focus image. This sharpness function is then used within a rule-based search algorithm to obtain the focused lens position via moving the lens head step-motor. The developed rule-based approach is compared to the commonly used global search and binary search algorithms in terms of focusing speed and power consumption. It is shown that the introduced rule-based search algorithm achieves a lower number of focusing iterations or faster focusing speed, and a lower number of steps or lower power consumption.