Characterizing digital image acquisition devices

Reichenbach, Stephen E.; Park, Stephen K.; Narayanswamy, Ramkumar

doi:10.1117/12.55783

Cited by 214 publications

(124 citation statements)

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“…Additionally, sampled imaging devices have an anisotropic character (Reichenbach et al, 1991). Therefore, alignment of the feature and the sampling grid have an impact on the detection (Lichti and Jamtsho, 2006;Reichenbach et al, 1991).…”

Section: The Test-bedmentioning

confidence: 99%

Characterization of Laser Scanners for Detecting Cracks for Post-Earthquake Damage Inspection

Anil

Akinci²,

Garrett³

et al. 2013

Proceedings of the 30th International Symposium on Automation and Robotics in Construction and Mining (ISARC 2013): Building Th

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Objective, accurate, and fast assessment of damage to buildings after an earthquake is crucial for timely remediation of material losses and safety of occupants of buildings. Laser scanners are promising sensors for collecting geometrical data regarding the damaged states of buildings, as they are able to provide high coverage and accuracy at long ranges. Yet, we have limited knowledge on the performance of laser scanners for detecting earthquake damage, and requirements of such data collection. This paper focuses on characterizing the performance of laser scanners for detecting thin cracks for damage assessment of reinforced concrete frames. We identified a series of crack parameters based on the state-of-the-art damage assessment codes and standards. Similarly, we identified parameters, which affect the performance of laser scanners for detecting cracks, based on prior research in this area. We studied the width, depth, and orientation of cracks; sampling interval of the scanner, and the range of the laser beam from the surface. Effects of these parameters on the detection of the minimum crack size were determined in an experimental setting. An automated algorithm was used to analyze the data. The results show that it is possible to detect as small as ~1mm cracks.

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Section: The Test-bedmentioning

confidence: 99%

Characterization of Laser Scanners for Detecting Cracks for Post-Earthquake Damage Inspection

Anil

Akinci²,

Garrett³

et al. 2013

Proceedings of the 30th International Symposium on Automation and Robotics in Construction and Mining (ISARC 2013): Building Th

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“…Noise mostly enters into the image while acquisition process. Imaging is of different types based on electromagnetic radiations or other waves that reflect back from the object while acquisition thereby conveying information about the shape, texture and other features of image [2]. For all classes of images, sensor is responsible for conversion of image information into digital form which are processed by the means of a digital computer so that it is outputted as visible -light image.…”

Section: Introductionmentioning

confidence: 99%

Edge Preservation of Enhanced Fuzzy Median Mean Filter Using Decision Based Median Filter

Kaur¹,

Sidhu²

2016

IJSC

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Image noise refers to random variations in the basic characteristics of image like brightness, intensity or color difference. These variations are not present in the image which is captured but may occur due to environmental conditions like sensor temperature or due to circuit of the scanner or other similar issues. Basically noise means unwanted signals in the image. Various filters have been designed for removal of almost all types of noise. It has been seen in most of the cases that as a result of high amount of filtering or repetitive filtering of image for the removal of noise, edges of images mostly get distorted or smeared out. It means that most of the filtering techniques lead to loss of fine edges of the images which needs to be preserved in order to enhance the quality of image. This paper has focused on to improve the enhanced fuzzy median mean filter so that fine edges get preserved in a better way. Experiments have been performed in MATLAB. Comparative analysis have been done on the basis of PSNR, MSE, BER and RMSE and it hasshown that border correction applied on images improves the results of enhanced fuzzy median mean filter.

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“…This avoids the problems due to spatial distortion and provides an accurate PSF estimate in a local area. This technique has more noise than [5], but this problem can be alleviated by acquiring and averaging many scans over the edge transition region. Both techniques are limited to estimating the 1-D PSF; however, the tube may be rotated relative to the plate in order to characterize the 2-D PSF.…”

Section: Introductionmentioning

confidence: 99%

“…al. [5] apply a more sophisticated approach to CCD characterization where the knife-edge is positioned at a very slight.angle with respect to the row of pixels. This yields an ensemble of edge profiles, each slightly shifted, that when properly registered and averaged provide a sub-pixel resolved edge profile with minimal measurement noise.…”

Section: Introductionmentioning

confidence: 99%

“…Unfortunately, pincushion distortion makes this technique impractical for our case. Reichenbach's technique [5] relies heavily on the inherent uniformity of the CCD array, while the non-uniform magnification caused by pincushion distortion and other aberrations [6] prohibits accurate registration of edge scans acquired over a large area of the intensifier screen. Glasser et.…”

Section: Introductionmentioning

confidence: 99%

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