A passive auto-focus camera control system

Chen, Chih-Yung; Hwang, Rey-Chue; Chen, Yu‐Ju

doi:10.1016/j.asoc.2009.07.007

Cited by 81 publications

(40 citation statements)

References 8 publications

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“…Most notable are those by [11] and [22]. The obvious drawback, is that these approaches are highly specific to the device used, the object of interest and the imaging conditions, and as such, do not scale very well as the number of possible objects increases.…”

Section: Review Of Passive Image Autofocusmentioning

confidence: 99%

The Weibull manifold in low-level image processing: An application to automatic image focusing

Zografos

Lenz

Felsberg

2013

Image and Vision Computing

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In this paper, we introduce a novel framework for low-level image processing and analysis. First, we process images with very simple, differencebased filter functions. Second, we fit the 2-parameter Weibull distribution to the filtered output. This maps each image to the 2D Weibull manifold. Third, we exploit the information geometry of this manifold and solve lowlevel image processing tasks as minimisation problems on point sets. For a proof-of-concept example, we examine the image autofocusing task. We propose appropriate cost functions together with a simple implicitly-constrained manifold optimisation algorithm and show that our framework compares very favourably against common autofocus methods from literature. In particular, our approach exhibits the best overall performance in terms of combined speed and accuracy.

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Section: Review Of Passive Image Autofocusmentioning

confidence: 99%

The Weibull manifold in low-level image processing: An application to automatic image focusing

Zografos

Lenz

Felsberg

2013

Image and Vision Computing

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“…Chen et al (51) used a well-trained self-organizing map (SOM) neural network to predict the best focus position in order to reduce the searching time. A frequency domain approach called "Discrete Wavelet Transformation (DWT)" was proposed in their paper to search for the highest frequency from the captured images, which corresponds to the best focus position.…”

Section: Machine-learning-based Methodsmentioning

confidence: 99%

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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“…in the antecedents are represented by triangular membership functions similar to those in Figure 5c and Figure 7. Figure 10b shows the values of α versus x and y according to (11) and (12), and Figure 10c shows the approximation provided by the rule base Interpolation.…”

Section: Navigation Toward the Goalmentioning

confidence: 99%