A camera-based color calibration of tiled display systems under various illumination environments

Lee, Dong-Hyeok; Yang, Chang-Mo; Park, Yohan; Kim, Choon-Woo

doi:10.1080/15980316.2017.1291454

Cited by 5 publications

(5 citation statements)

References 8 publications

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“…When the slip gauge's position changes, the values of 'a1' and 'b1' also change to 'a2' and 'b2', respectively. It is seen that the pixel length of the slip gauge decreases as the distance between the camera lens and the slip gauge increases, indicating the inverse relationship that is expressed by equation (6).…”

Section: Distance and Length Measurement Of Measuring Objectmentioning

confidence: 94%

“…The suggested algorithm improves performance in visual identification tasks by correctly optimizing camera settings using 'particle swarm optimization' and 'differential evolution'. Lee et al [6] proposed an efficient, positiondependent, and independently applied each sub-display camera-based technique for color calibration on tiled display systems. The method put forth can reduce both the spatial non-uniformity within each sub-display and the non-uniformity in the differences in color and luminance across sub-displays.…”

Section: Introductionmentioning

confidence: 99%

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Exploring machine vision measurements through innovative cyclic-lead-follower statistical technique: an experimental study

Zende,

Pawade

2024

Eng. Res. Express

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In metrology systems, machine vision systems are often utilized for non-contact inspection. The most important phase in ensuring measurement accuracy is camera calibration and estimation of pixel measurement errors, which establish the correspondence between image coordinates and object coordinates. Multiple calibration techniques improve the effectiveness of machine vision systems. However, a number of factors lead to variations in the camera calibration procedure, which must be optimized. This study explains a novel 'Cyclic-Lead-Follower' statistical methodology proposed for camera calibration and measurement to estimate the errors in pixel measurement, employing four slip gauges for measurement. Several multi-criteria decision-making techniques, including WSM, WPM, WASPAS, and TOPSIS, were used to optimize the results of the proposed Cyclic-Lead-Follower methods. The proposed Cyclic-Lead-Follower method improves the accuracy of the camera calibration and measurement system, which employs the exponential moving average statistical method when compared to the traditional calibration method. The proposed calibration method produces lower exponential moving average values than the traditional calibration method, with an average percentage error of approximately 46% in the exponential moving average. The use of an exponential moving average in a validation experiment of the Cyclic-Lead-Follower method decreased the measurement percentage errors, which were estimated to be 0.57%. The proposed method can be used in machine vision systems due to its robustness, accuracy, and cost-effectiveness.

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Section: Distance and Length Measurement Of Measuring Objectmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Exploring machine vision measurements through innovative cyclic-lead-follower statistical technique: an experimental study

Zende,

Pawade

2024

Eng. Res. Express

View full text Add to dashboard Cite

show abstract

“…The common vignetting correction methods include the fitting function method and look-up table (LUT) [7][8][9]. The fitting function method uses a polynomial model, an exponential polynomial model, a hyperbolic cosine model, a Gaussian function, or a radial polynomial model to fit the vignetted images obtained under scenes with uniform luminance [10][11][12][13]; thus, the correction factors for the entire image plane can be calculated.…”

Section: Introductionmentioning

confidence: 99%

“…The LUT method provides a correction factor for each pixel and has the highest accuracy for vignetting correction. It is a common method to generate the LUT for vignetting correction, where reference images obtained under uniform illumination are used to extract the vignetting background [7,16]. Subsequently, the vignetting background is used to generate the LUT, which is typically the ratio of the maximum value of the reference image to the value of each pixel, given the impact of image noise.…”

Section: Introductionmentioning

confidence: 99%

Vignetting Correction Based on a Two-Dimensional Gaussian Filter With Harmony for Area Array Sensors

Cao

Zhang³

et al. 2022

IEEE Trans. Comput. Imaging

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Vignetting is the main factor causing uneven brightness in an image due to the inherent characteristics of the camera sensor. In radiometric correction-based remote sensing sensors, vignetting can lead to uncomparable radiation signals within and between images, and in the processing of remote sensing images, it leads to an unbalanced color when images are mosaiced. Due to distortions of optical devices, uneven response of detectors and other factors, it is difficult to extract the vignetting from complex reference images and correct it accurately. In this study, a low-pass Gaussion filter was selected to extract the vignetting from reference image, and the standard deviation (STD) and mean of the reference images were used as the factor of harmony to evaluate the strength of the filter for attaining optimal vignetting background. In experiments, the method proposed was compared with polynomial fitting method and general Gaussian filter mothed. The results show that Gaussian filter with harmony method achieves the best effect of vignetting correction compared with the other two methods.

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“…Several studies have been conducted on the quantitative determination and correction of colors [20][21][22]. However, it is still unclear whether the corrected colors are suitable for harvest determination.…”

Section: Introductionmentioning

confidence: 99%

Appropriate grape color estimation based on metric learning for judging harvest timing

et al. 2022

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The color of a bunch of grapes is a very important factor when determining the appropriate time for harvesting. However, judging whether the color of the bunch is appropriate for harvesting requires experience and the result can vary by individuals. In this paper, we describe a system to support grape harvesting based on color estimation using deep learning. To estimate the color of a bunch of grapes, bunch detection, grain detection, removal of pest grains, and color estimation are required, for which deep learning-based approaches are adopted. In this study, YOLOv5, an object detection model that considers both accuracy and processing speed, is adopted for bunch detection and grain detection. For the detection of diseased grains, an autoencoder-based anomaly detection model is also employed. Since color is strongly affected by brightness, a color estimation model that is less affected by this factor is required. Accordingly, we propose multitask learning that uses metric learning. The color estimation model in this study is based on AlexNet. Metric learning was applied to train this model. Brightness is an important factor affecting the perception of color. In a practical experiment using actual grapes, we empirically selected the best three image channels from RGB and CIELAB (L*a*b*) color spaces and we found that the color estimation accuracy of the proposed multi-task model, the combination with “L” channel from L*a*b color space and “GB” from RGB color space for the grape image (represented as “LGB” color space), was 72.1%, compared to 21.1% for the model which used the normal RGB image. In addition, it was found that the proposed system was able to determine the suitability of grapes for harvesting with an accuracy of 81.6%, demonstrating the effectiveness of the proposed system.

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A camera-based color calibration of tiled display systems under various illumination environments

Cited by 5 publications

References 8 publications

Exploring machine vision measurements through innovative cyclic-lead-follower statistical technique: an experimental study

Exploring machine vision measurements through innovative cyclic-lead-follower statistical technique: an experimental study

Vignetting Correction Based on a Two-Dimensional Gaussian Filter With Harmony for Area Array Sensors

Appropriate grape color estimation based on metric learning for judging harvest timing

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