• Journal of Biosystems Engineering
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• v.40 no.3
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• pp.296-304
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• 2015

Purpose: The balance between miscanthus production and its cost effectiveness depends greatly on its moisture content during post processing. The objective of this research was to measure the moisture content using a non-destructive and non-contact methodology for in situ applications. Methods: The moisture content of comminuted miscanthus was controlled using a closed chamber, a humidifier, a precision weigher, and a real-time monitoring software developed in this research. A CMOS sensor equipped with $50{\times}$ magnifier lens was used to capture magnified images of the conditioned materials with moisture content level from 5 to 30%. The hypothesis is that when light is incident on the comminuted particles in an inclined manner, higher moisture content results in light being reflected with a higher intensity. Results: A linear regression analysis for an initiative hypothesis based on general histogram analysis yielded insufficient correlations with low significance level (<0.31) for the determination coefficient. A significant relationship (94% confidence level) was determined at level 108 in a reverse accumulative histogram proposed based on a revised hypothesis. A linear regression model with the value at level 108 in the reverse accumulative histogram for a magnified image as the independent variable and the moisture content of comminuted miscanthus as the dependent variable was proposed as the estimation model. The calibrated linear regression model with a slope of 92.054 and an offset of 32.752 yielded 0.94 for the determination coefficient (RMSE = 0.2%). The validation test showed a significant relationship at the 74% confidence level with RMSE 6.4% (n = 36). Conclusions: To compensate the inconsistent significance between calibration and validation, an estimation model robust against various systematic interferences is necessary. The economic efficiency of miscanthus, which is a promising energy resource, can be improved by the real-time measurement of its crucial material properties.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.5
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• pp.555-562
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• 2012

This paper presents a hybrid method for improving the recognition performance, which is based on the nonlinear histogram equalization, features extraction, and statistical correlation of images. The nonlinear histogram equalization based on a logistic function is applied to adaptively improve the quality by adjusting the brightness of the image according to its intensity level frequency. The statistical correlation that is measured by the normalized cross-correlation(NCC) coefficient, is applied to rapidly and accurately express the similarity between the images. The local features based on independent component analysis(ICA) that is used to calculate the NCC, is also applied to statistically measure the correct similarity in each images. The proposed method has been applied to the problem for recognizing the 30-face images of 40*50 pixels. The experimental results show that the proposed method has a superior recognition performances to the method without performing the preprocessing, or the methods of conventional and adaptively modified histogram equalization, respectively.

• Journal of the Korea Society of Computer and Information
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• v.14 no.11
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• pp.179-186
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• 2009

In this paper, we propose a head detection method based on vertical and horizontal pixel histogram analysis in order to overcome drawbacks of the previous head detection approach using Haar-like feature-based face detection. In the proposed method, we create the vertical and horizontal foreground pixel histogram images from the background subtraction image, which represent the number of foreground pixels in the same vertical or horizontal position. Then we extract feature points of a head region by applying Harris corner detection method to the foreground pixel histogram images and by analyzing corner points. The proposal method shows robust head detection results even in the face image covering forelock by hairs or the back view image in which the previous approaches cannot detect the head regions.

• International Journal of Advanced Culture Technology
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• v.6 no.3
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• pp.142-150
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• 2018

This paper describes the algorithm that lowers the dimension, maintains the object recognition and significantly reduces the eigenspace configuration time by combining the edge orientation histogram and principle component analysis. By using the detected object region as a recognition input image, in this paper the object recognition method combined with principle component analysis and the multi-layer network which is one of the intelligent classification was suggested and its performance was evaluated. As a pre-processing algorithm of input object image, this method computes the eigenspace through principle component analysis and expresses the training images with it as a fundamental vector. Each image takes the set of weights for the fundamental vector as a feature vector and it reduces the dimension of image at the same time, and then the object recognition is performed by inputting the multi-layer neural network.

• Journal of Korean Society for Geospatial Information Science
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• v.12 no.2 s.29
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• pp.61-66
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• 2004

By the advent of the high-resolution Satellite imagery, there are increasing needs in image mosaicking technology which can be applied to various application fields such as GIS(Geographic Information system). To mosaic images, various methods such as image matching and histogram modification are needed. In this study, automated image mosaicking is performed using image matching method based on the multi-resolution wavelet analysis(MWA). Specifically, both area based and feature based matching method are embedded in the multi-resolution wavelet analysis to construct seam line.; seam points are extracted then polygon clipping method are applied to define overlapped area of two adjoining images. Before mosaicking, radiometric correction is proceeded by using histogram matching method. As a result, mosaicking area is automatically extracted by using polygon clipping method. Also, seamless image is acquired using multi-resolution wavelet analysis.

• Journal of Korean Academy of Oral and Maxillofacial Radiology
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• v.25 no.2
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• pp.521-533
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• 1995

This study was performed to develop and evaluate the method to detect Quantitatively the serial changes in the size of artificial lesion in the spongious bone by automatic color image analyzer. 15 intraoral radiograms taken before and after endodontic treatment of 5 cases were used for contour line analysis. 30 intraoral radiograms taken by geometrically standardized apparatus before and after serially the formation of artificial lesions of 0.80, 1.20, 1.75, 2.00mm in diameter at the periapical area and interdental area of spongious bone were used. The analysis of image according to the variance of lesion size by 0.25, 0.35, 0.55, and 0.85mm serially was performed by the histogram and the color enhancement with subtraction. The images inputted by CCDcamera were digitized and analyzed by NEXUS QUBE program with NEC PC-9801 computer. The obtained results were as follows: 1. There was no reliability in the analysis of lesions by contour line 2 .. The mean difference of the grey scale at each pixel was 1 step between reference image and the corrected images. 3. In the analysis by histogram of the artificial lesion in spongeous bone, the change over 0.55mm in the mesiodistal size was detectable by the change of the numbers of pixel showing the change in grey scale. 4. In the analysis by histogram of the artificial lesion in spongeous bone, the change over 0.25mm in the buccolingual size was detectable by the change in grey scale. 5. By color enbancement with- subtraction, each lesion was able to be isolated and the change in it's mesiodistal size was detectable visually , but not in it's buccolingual size.

• Journal of the Korean Society of Radiology
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• v.12 no.2
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• pp.193-199
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• 2018

In this research, 15 patients were diagnosed with 1.5T and 3.0T MRI instruments (Philips, Medical System, Achieva) to minize Ferromagnetic artifact and find the optimized Tesla. Based on the theory that the 3.0T, when compared to 1.5T, show relatively high signal-to-ratio(SNR), Scan time can be shortened or adjust the image resolution. However, when using the 3.0T MRI instruments, various artifact due to the magnetic field difference can degrade the diagnostic information. For the analysis condition, area of interest is set at the background of the T1, T2 sagittal image followed by evaluation of L3, L4, L5 SNR, length of 3 parts with Ferromagnetic artifact, and Histogram. The validity evaluation was performed by using the independent t test. As a result, for the SNR evaluation, mere difference in value was observed for L3 between 1.5T and 3.0T, while big differences were observed for both L4, and L5(p<0.05). Shorter length was observed for the 1.5T when observing 3 parts with Ferromagnetic artifact, thus we can conclude that 3.0T can provide more information on about peripheral tissue diagnostic information(p<0.05). Finally, 1.5T showed higher counts values for the Histogram evaluation(p<0.05). As a result, when we have compared the 1.5T and 3.0T with SNR, length of Ferromagnetic artifact, Histogram, we believe that using a Low Tesla for Spine MRI test can achieve the optimal image information for patients with disk operation like PLIF, etc. in the past.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.10
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• pp.2427-2432
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• 2014

Color-$X^2$ is used as a method for scene change detection. It extracts a violent scene in an elevator and then could be used for real-time surveillance of criminal acts. The scene could be also used to secure after-discovered evidences and to prove analysis processes. Video Forensic is defined as a research on various methods to efficiently analyze evidences upon crime-related visual images in the field of digital forensic. The method to use differences of color-histogram detects the difference values of histogram for RGB color from two frames respectively. Our paper uses Color-$X^2$ histogram that is composed of merits of color histogram and ones of $X^2$ histogram, in order to efficiently extract violent scenes in elevator. Also, we use a threshold so as to find out key frame, by use of existing Color-$X^2$ histogram. To increase the probability that discerns whether a real violent scene or not, we take advantage of statistical judgments with 20 sample visual images.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.2
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• pp.107-114
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• 2022

In order to classify 3D film images as right or wrong, it is necessary to detect the pattern in a 3D film image. However, if the contrast of the pixels in the 3D film image is low, it is not easy to classify as the right and wrong 3D film images because the pattern in the image might not be clear. In this paper, we propose a method of classifying 3D film images as right or wrong by comparing the width at a specific frequency of each histogram after obtaining the histogram. Since, it is classified using the width of the histogram, the analysis process is not complicated. From the experiment, the histograms of right and wrong 3D film images were distinctly different, and the proposed algorithm reflects these features, and showed that all 3D film images were accurately classified at a specific frequency of the histogram. The performance of the proposed algorithm was verified to be the best through the comparison test with the other methods such as image subtraction, otsu thresholding, canny edge detection, morphological geodesic active contour, and support vector machines, and it was shown that excellent classification accuracy could be obtained without detecting the patterns in 3D film images.

• Korean Journal of Radiology
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• v.20 no.4
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• pp.599-608
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• 2019

Objective: To evaluate the effectiveness of computed tomography (CT) Hounsfield unit histogram analysis (HUHA) in postoperative pancreatic fistula (PF) prediction. Materials and Methods: Fifty-four patients (33 males and 21 females; mean age, 65.6 years; age range, 37-89 years) who had undergone preoperative CT and pancreaticoduodenectomy were included in this retrospective study. Two radiologists measured mean CT Hounsfield unit (CTHU) values by drawing regions of interest (ROIs) at the level of the pancreaticojejunostomy site on preoperative pre-contrast images. The HUHA values were arbitrarily divided into three categories, comprising HUHA-A ≤ 0 HU, 0 HU < HUHA-B < 30 HU, and HUHA-C ≥ 30 HU. Each HUHA value within the ROI was calculated as a percentage of the entire area using commercial 3-dimensional analysis software. Pancreas texture was evaluated as soft or hard by manual palpation. Results: Fifteen patients (27.8%) had clinically relevant PFs. The PF group had significantly higher HUHA-A (p < 0.01) and significantly lower mean CTHU (p < 0.01) values than those of the non-PF group. The HUHA-A value had a moderately strong correlation with PF occurrence (r = 0.60, p < 0.01), whereas the mean CTHU had a weak negative correlation with PF occurrence (r = -0.27, p < 0.01). The HUHA-A and mean CTHU areas under the curve (AUCs) for predicting PF occurrence were 0.86 and 0.65, respectively, with significant difference (p < 0.01). The HUHA-A and mean CTHU AUCs for predicting pancreatic softness were 0.86 and 0.64, respectively, with significant difference (p < 0.01). Conclusion: The HUHA-A values on preoperative pre-contrast CT images demonstrate a strong correlation with PF occurrence.