Citation

Deng, Qianwei and Wong, Chee Onn and Sitharan, Roopesh and Meng, Xiangbin (2024) Design of image intelligent focusing system based on improved SMD function and RBF algorithm. PLOS ONE, 19 (8). e0307319. ISSN 1932-6203

Text Design of image intelligent focusing system based on improved SMD function and RBF algorithm.pdf - Published Version Restricted to Repository staff only Download (1MB)

Abstract

The utilization of digital statistical processes in images and videos can effectively tackle numerous challenges encountered in optical sensors. This research endeavors to overcome the limitations inherent in traditional focus models, particularly their inadequate accuracy. It aims to bolster the precision of real-time perception and dynamic control by employing enhanced data fusion methodologies. The ultimate objective is to facilitate information services that enable seamless interaction and profound integration between computational and physical processes within an open environment. To achieve this, an enhanced sum-modulus difference (SMD) evaluation function has been proposed. This innovation is founded on the concept of threshold value evaluation, aimed at rectifying the accuracy shortcomings of traditional focusing models. Through the computation of each gray value after threshold segmentation, the method identifies the most suitable threshold for image segmentation. This identified threshold is then applied to the focus search strategy employing the radial basis function (RBF) algorithm. Furthermore, an intelligent focusing system has been developed on the Zynq development platform, encompassing both hardware design and software program development. The test results affirm that the focusing model based on the improved SMD evaluation function rapidly identifies the peak point of the gray variance curve, ascertains the optimal focal plane position, and notably enhances the sensitivity of the focusing model.

Item Type:	Article
Uncontrolled Keywords:	images and videos
Subjects:	N Fine Arts > N Visual arts
Divisions:	Faculty of Creative Multimedia (FCM)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	02 Sep 2024 07:33
Last Modified:	02 Sep 2024 07:33
URII:	http://shdl.mmu.edu.my/id/eprint/12912

Downloads

Edit (login required)