A Bayes-spectral-entropy-based measure of camera focus using a discrete cosine transform

Kristan, Matej; Perš, Janez; Perše, Matej; Kovačič, Stanislav

doi:10.1016/j.patrec.2006.01.016

Cited by 101 publications

(53 citation statements)

References 15 publications

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“…the variance in the neighborhoods. The last ones work in different frequency domains through discrete cosine (Kristan et al, 2006) or Fourier (Boddeke et al, 1994;Malik & Choi, 2008) transforms. The latter exploit more robust band-pass filters but lack sensitivity in return.…”

Section: Neighborhood-based Methodsmentioning

confidence: 99%

Surface Topography and Texture Restoration from Sectional Optical Imaging by Focus Analysis

Fernandes¹,

Gavet²,

Pinoli³

2012

Image Restoration - Recent Advances and Applications

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Section: Neighborhood-based Methodsmentioning

confidence: 99%

Surface Topography and Texture Restoration from Sectional Optical Imaging by Focus Analysis

Fernandes¹,

Gavet²,

Pinoli³

2012

Image Restoration - Recent Advances and Applications

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“…The estimated location of the maxima is indicated by max q . This technique of using a quadratic function as a model and determining the maxima has been the most common approach, [8,12,13,17,19,20,25]. From optics literature, given the properties of the lens we can compute the depth of field for varying lens to object distances, using the relation in Eq.…”

Section: Searching Technique For Focusingmentioning

confidence: 99%

“…This will help discuss and better understand the performance and bottlenecks of the system. t gath = t pos + t settle + t cap (11) t gatherall = N * t gath (12) t loc = t focus + t find + t servo (13) t fp = t loc * N (14) Estimate of the time lapsed between the change in stimulus and the seedlings first response t complete Estimate of the time lapsed between the change in stimulus and the seedling reaches a steady state A few points to note on the variables mentioned above. t pos depends on size and speed of the system and is fixed by design.…”

Section: System Performance Evaluationmentioning

confidence: 99%

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A high throughput robot system for machine vision based plant phenotype studies

Subramanian

Spalding

Ferrier

2012

Machine Vision and Applications

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This work demonstrates how a high throughput robotic machine vision systems can quantify seedling development with high spatial and temporal resolution.The throughput that the system provides is high enough to match the needs of functional genomics research. Analyzing images of plant seedlings growing and responding to stimuli is a proven approach to finding the effects of an affected gene. However, with 10 4 genes in a typical plant genome, comprehensive studies will require high throughput methodologies. To increase throughput without sacrificing spatial or temporal resolution, a 3 axis robotic gantry system utilizing visual servoing was developed. The gantry consists of direct drive linear servo motors that can move the cameras at a speed of 1 m/s with an accuracy of 1 µm, and a repeatability of 0.1 µm. Perpendicular to the optical axis of the cameras was a 1 m 2 sample fixture holds 36 Petri plates in which 144 Arabidopsis thaliana seedlings (4 per Petri plate) grew vertically along the surface of an agar gel. A probabilistic image analysis algorithm was used to locate the root of seedlings and a normalized gray scale variance measure was used to achieve focus by servoing along the optical axis. Rotation of the sample holder induced a gravitropic bending response in the roots, which are approximately 45 µm wide and several millimeter in length. The custom hardware and software described here accurately quantified the gravitropic responses of the seedlings in parallel at approximately 3 min intervals over an 8-h period. Here we present an overview of our system and describe some of the necessary capabilities and challenges to automating plant phenotype studies.

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“…Therefore a focus value is needed to measure the amount of high frequencies in the image. As autofocusing is a longstanding topic, in the literature a wide variety of focus algorithms have been proposed each one with an associated focus measure (Krotkov, 1987;Nayar, 1994;Lee et al, 1995;Subbarao and Tyan, 1998;Choi and Ki, 1999;Lee et al, 2001;Nathaniel et al, 2001;Kehtarnavaz and Oh, 2003;Kristan and Pernus, 2004;Shirvaikar, 2004;Park and Kim, 2005;Kristan et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Comparison of Focus Measures in Face Detection Environments

Lorenzo-Navarro¹,

Déniz²,

Castrillón-Santana³

et al. 2007

Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics

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Abstract:This work presents a comparison among different focus measures used in the literature for autofocusing in a non previously explored application of face detection. This application has different characteristics to those where traditionally autofocus methods have been applied like microscopy or depth from focus. The aim of the work is to nd if the best focus measures in traditional applications of autofocus have the same performance in face detection applications. To do that six focus measures has been studied in four different settings from the oldest to more recent ones. IntroductionIn face detection and face recognition methods (Pentland et al., 1994;Rowley et al., 1998;Gross et al., 2001;Hjelmas and Low, 2001;Yang et al., 2002;Zhao et al., 2003), borders play an important role because they de ne the facial features that appear in the face such as eyes, mouth and nose, which are needed to carry out the task. In a blurred image these facial features are not well de ned, and so the detection or identi cation can not be done. Blurred images can be obtained in a defocused camera because defocusing can be modelled as a low-pass ltering process, opposite to focused images which have a higher frequency content (Nayar, 1994). Thus, it is desirable that the image acquisition system has an autofocus mechanism. (Krotkov, 1987;Nayar, 1994;Lee et al., 1995;Subbarao and Tyan, 1998;Choi and Ki, 1999;Lee et al., 2001;Nathaniel et al., 2001;Kehtarnavaz and Oh, 2003;Kristan and Pernus, 2004;Shirvaikar, 2004;Park and Kim, 2005;Kristan et al., 2006). Most of the published autofocusing algorithmssolve the problem of planar objects like in microscopy applications (Sun et al., 2004) or single object of interest like depth from focus applications (Nayar, 1994). In these applications, focus measures exhibit an ideal curve with a peak with step slopes at the lens position where the object is focused because there is only one object in the image or because it is a planar image. However, in human computer interaction, people do not always hold the same position in the image and exists more objects in the scene so the focus measure does not exhibit a clear maximum. In digital photograhy this drawback is eluded because the photographer selects the object of interest and centers it.

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A Bayes-spectral-entropy-based measure of camera focus using a discrete cosine transform

Cited by 101 publications

References 15 publications

Surface Topography and Texture Restoration from Sectional Optical Imaging by Focus Analysis

Surface Topography and Texture Restoration from Sectional Optical Imaging by Focus Analysis

A high throughput robot system for machine vision based plant phenotype studies

Comparison of Focus Measures in Face Detection Environments

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