Improving the accuracy and low-light performance of contrast-based autofocus using supervised machine learning

Chen, Rudi; Beek, Peter van der

doi:10.1016/j.patrec.2015.01.010

Cited by 22 publications

(13 citation statements)

References 18 publications

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“…A recent learning-based approach was proposed by Chen and van Beek. They introduced a supervised machine learning approach, (34) in which two decision tree classifiers are defined to decide the state of the focusing process and locate the best focus positions. They used two sets of feature vectors, and each set includes many different features.…”

Section: Machine-learning-based Methodsmentioning

confidence: 99%

“…16 conducted further research in 2015. (34) They expanded the image sets to 32 benchmarks for evaluation, a total of 5344 images. Additionally, 11 more sets were captured in a dark room to evaluate FMFs under low light condition.…”

Section: Focus Measure Function (Fmf)mentioning

confidence: 99%

“…The majority of the AF method (34,(45)(46)(47)(48)(49) employs a coarse to fine search scheme. As mentioned above, this scheme can effectively reduce the time for searching the best focus position.…”

Section: Coarse To Fine Searchmentioning

confidence: 99%

“…(45) The first step uses a coarse but fast search method based on the low spatial frequencies of the image, which followed by a fine but slower search method based on the high frequencies of the image. Chen et al (34) followed He et al (48) and Li's (49) work but they employed two additional fine steps at the beginning to predict the direction where a peak most probably exists.…”

Section: Coarse To Fine Searchmentioning

confidence: 99%

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Autofocus System and Evaluation Methodologies: A Literature Review

2018

Sensors and Materials

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The autofocus (AF) system has gained popularity in over 40 years. Embedded in a camera, it can bring the best focused image to the viewer within a few seconds, which frees users from focusing manually. An AF system usually consists of a motor, a lens, and the processing and control units. Within years of development, the AF system has become mature in terms of both technology and market, and many fast and accurate AF systems have been introduced and widely installed in compact cameras and digital single lens reflex (DSLR) cameras. On the other hand, the market for thermal infrared cameras has been increasingly growing in recent years owing to their decreasing cost and wide use by both civilians and the military. The AF system in those cameras adopted similar but more complex mechanisms. This article serves as a literature review of the state of the art of the AF system in both visible light digital cameras and thermal infrared cameras.

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Section: Machine-learning-based Methodsmentioning

confidence: 99%

Section: Focus Measure Function (Fmf)mentioning

confidence: 99%

Section: Coarse To Fine Searchmentioning

confidence: 99%

Section: Coarse To Fine Searchmentioning

confidence: 99%

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Autofocus System and Evaluation Methodologies: A Literature Review

2018

Sensors and Materials

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“…Most of these studies have been motivated to improve the focusing accuracy, reduce the computation time, and enhance the robustness to noise and variations, from the two important perspectives: the image definition evaluation and the focus search algorithm [3,4]. Image definition evaluation, which measures the image quality (definition), is one of the important steps for the image-based auto-focusing.…”

Section: Introductionmentioning

confidence: 99%

An image auto-focusing algorithm for industrial image measurement

Liu

Yang

2016

EURASIP J. Adv. Signal Process.

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Auto-focusing task, which automatically obtains the best image focus, plays an important role to improve the image definition for the industrial image measurement application. Image-based auto-focusing is one of the widely used methods for this task because of its fast response, convenience, and intelligence. In general, the image-based auto-focusing algorithm often consists of two important steps which are the image definition evaluation and the search strategy. In this paper, we have developed an image auto-focusing algorithm for industrial image measurement. First, we propose a new image definition evaluation method based on the fuzzy entropy, which can reduce the negative effects of noise and variations of light intensity and lens magnification. Second, a combined search method is proposed to combine the multi-scale global search and fine-level curve fitting method, which can avoid the disturbance of the local peaks and obtain the best image focus. The proposed image auto-focusing algorithm has the advantages of high focusing accuracy, high repeatability and stability under the variations of lens magnification, and light intensity index, which make it applicable for the industrial image measurement. Experimental results and comparisons on the practical industrial image measurement system have been presented to show the effectiveness and superiority of the proposed algorithm.

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Rapid, automated, and reliable antimicrobial susceptibility test from positive blood culture by CAST‐R

Zhu

Ren

Zhu

et al. 2022

mLife

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Antimicrobial susceptibility tests (ASTs) are pivotal in combating multidrug resistant pathogens, yet they can be time‐consuming, labor‐intensive, and unstable. Using the AST of tigecycline for sepsis as the main model, here we establish an automated system of Clinical Antimicrobials Susceptibility Test Ramanometry (CAST‐R), based on D2O‐probed Raman microspectroscopy. Featuring a liquid robot for sample pretreatment and a machine learning‐based control scheme for data acquisition and quality control, the 3‐h, automated CAST‐R process accelerates AST by >10‐fold, processes 96 paralleled antibiotic‐exposure reactions, and produces high‐quality Raman spectra. The Expedited Minimal Inhibitory Concentration via Metabolic Activity is proposed as a quantitative and broadly applicable parameter for metabolism‐based AST, which shows 99% essential agreement and 93% categorical agreement with the broth microdilution method (BMD) when tested on 100 Acinetobacter baumannii isolates. Further tests on 26 clinically positive blood samples for eight antimicrobials, including tigecycline, meropenem, ceftazidime, ampicillin/sulbactam, oxacillin, clindamycin, vancomycin, and levofloxacin reveal 93% categorical agreement with BMD‐based results. The automation, speed, reliability, and general applicability of CAST‐R suggest its potential utility for guiding the clinical administration of antimicrobials.

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Improving the accuracy and low-light performance of contrast-based autofocus using supervised machine learning

Cited by 22 publications

References 18 publications

Autofocus System and Evaluation Methodologies: A Literature Review

Autofocus System and Evaluation Methodologies: A Literature Review

An image auto-focusing algorithm for industrial image measurement

Rapid, automated, and reliable antimicrobial susceptibility test from positive blood culture by CAST‐R

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