High-speed autofocusing of multisized microobjects

Abstract

This paper proposes a new high-speed autofocus algorithm for observing multisized microobjects under a transmitted light microscope. The method estimates the blurriness of the object by analyzing the intensity variation of a defined region around the object border in the frequency domain. A defocus function is proposed so that the best focused position of the object is found when the defocus function reaches its minimum within the close region around the focal plane. Experiments for execution time and accuracy evaluation have been performed to compare its performance to other autofocus algorithms. Experimental results showed that the proposed algorithm outperformed other algorithms in terms of execution time and accuracy and that it is more robust to environmental change.

High-Speed Automated Manipulation of Microobjects Using a Two-Fingered Microhand

“…The additional time required for the new search (+10 ms) is not significant since the modified algorithm needs to run only a few more search steps from the first found minimum candidate to find the reliable termination criteria if another minimum exists. In term of computational cost, object detection in 3D could be performed by DFBIV within 1.53 ms with available 85 images of a 113 µm microsphere [26]. The average added time using the modified DFBIV algorithm on 90 captured images was recorded 10 ms.…”

“…4); • Defining the region of interest of 100 x 140 pixels so that its top-left coordinate is (x tip − 20, y tip − 20). 2) Object Detection: The position of the target microobject is found by applying the modified "Depth from border intensity variation" (DFBIV) [26] to find the best focused image of the microobject.…”

“…We realized that failure cases were due to small quantities of dust in the microscopic field of view. The accuracy assessment of the DFBIV was carried out and the experimental results were presented in [26]. The largest absolute average deviation of 1.4 µm and 2.0 µm were observed with the DFBIV algorithm to detect the z-position of an 17 um NIH-3T3 cell and an 113 µm microsphere, respectively.…”

Machine Vision System Utilizing Black Silicon CMOS Camera for Through-Silicon Alignment

“…The performance analysis indicates that the implemented autofocusing method compares favorably with other known autofocusing algorithms with execution time from 2 to 700 ms per frame [34]. The subsystem performance can be further increased by the implementation of improved scanning (search) techniques, shortening the scanning distance, or narrowing the image area to focus [35] - [37]. These approaches reduce the number of taken images and the amount of data to analyze.…”

“…Finally, the optimal focusing point is then determined by the largest width of the spectrum, as the best in-focus image is the most detailed. Although Fourier analysis requires significant computing power, the key advantage of this method is it avoids using any sophisticated image processing algorithms, such as object recognition, edge detection, global or local variance analysis, contrast or gradient estimation, derivative, histogram or correlation analysis, as well as the usage of any additional hardware [26], [35] - [37]. The only merit for finding the best in-focus image is the maximum value of the two-dimensional Fourier transform components sum.…”