• The Korean Journal of Applied Statistics
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• v.32 no.5
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• pp.721-740
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• 2019

Noise reduction is an important field in image processing and requires a statistical approach. However, it is difficult to assume a specific distribution of noise, and a spatial filter that reflects regional characteristics is a small sample and cannot be accessed in a parametric manner. The first order image differential and the second order image differential show a clear difference according to the noise level included in the image and can be more clearly understood using the canyon edge detector. The Fligner-Killeen test was performed and the bootstrap method was used to statistically check the noise level. The estimated noise level was set between 0 and 1 using the cumulative distribution function of the beta distribution. In this paper, we propose a nonparametric noise reduction algorithm that accounts for the noise level included in the image.

• Atmosphere
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• v.27 no.2
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• pp.119-131
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• 2017

We present and discuss the Tropopause Folding Turbulence Detection (TFTD) algorithm for the Korean Communication, Ocean, Meteorological Satellite (COMS) which is originally developed for the Tropopause Folding Turbulence Product (TFTP) from the Geostationary Operational Environmental Satellite (GOES)-R. The TFTD algorithm assumes that the tropopause folding is linked to the Clear Air Turbulence (CAT), and thereby the tropopause folding areas are detected from the rapid spatial gradients of the upper tropospheric specific humidity. The Layer Averaged Specific Humidity (LASH) is used to represent the upper tropospheric specific humidity calculated using COMS $6.7{\mu}m$ water vapor channel and ERA-interim reanalysis temperature at 300, 400, and 500 hPa. The comparison of LASH with the numerical model specific humidity shows a strong negative correlation of 80% or more. We apply the single threshold, which is determined from sensitivity analysis, for cloud-clearing to overcome strong gradient of LASH at the edge of clouds. The tropopause break lines are detected from the location of strong LASH-gradient using the Canny edge detection based on the image processing technique. The tropopause folding area is defined by expanding the break lines by 2-degree positive gradient direction. The validations of COMS TFTD is performed with Pilot Reports (PIREPs) filtered out Convective Induced Turbulence (CIT) from Dec 2013 to Nov 2014 over the South Korea. The score test shows 0.49 PODy (Probability of Detection 'Yes') and 0.64 PODn (Probability of Detection 'No'). Low POD results from various kinds of CAT reported from PIREPs and the characteristics of high sensitivity in edge detection algorithm.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.5
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• pp.339-345
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• 2016

In general, mosaic blocks are used to hide some specified areas, such as human faces and disgusting objects, in an input image when images are uploaded on a web-site or blog. This paper proposes a new algorithm for robustly detecting grid mosaic areas in an image based on the edge projection. The proposed algorithm first extracts the Canny edges from an input image. The algorithm then detects the candidate mosaic blocks based on horizontal and vertical edge projection. Subsequently, the algorithm obtains real mosaic areas from the candidate areas by eliminating the non-mosaic candidate regions through geometric features, such as size and compactness. The experimental results showed that the suggested algorithm detects mosaic areas in images more accurately than other existing methods. The suggested mosaic detection approach is expected to be utilized usefully in a variety of multimedia-related real application areas.

• Economic and Environmental Geology
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• v.55 no.5
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• pp.551-561
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• 2022

Geophysical exploration methods are very useful for generating high-resolution images of underground structures, and such methods can be applied to investigation of buried cultural properties and for determining their exact locations. In this study, image feature extraction and image segmentation methods were applied to automatically distinguish the structures of buried relics from the high-resolution ground-penetrating radar (GPR) images obtained at the center of Silla Kingdom, Gyeongju, South Korea. The major purpose for image feature extraction analyses is identifying the circular features from building remains and the linear features from ancient roads and fences. Feature extraction is implemented by applying the Canny edge detection and Hough transform algorithms. We applied the Hough transforms to the edge image resulted from the Canny algorithm in order to determine the locations the target features. However, the Hough transform requires different parameter settings for each survey sector. As for image segmentation, we applied the connected element labeling algorithm and object-based image analysis using Orfeo Toolbox (OTB) in QGIS. The connected components labeled image shows the signals associated with the target buried relics are effectively connected and labeled. However, we often find multiple labels are assigned to a single structure on the given GPR data. Object-based image analysis was conducted by using a Large-Scale Mean-Shift (LSMS) image segmentation. In this analysis, a vector layer containing pixel values for each segmented polygon was estimated first and then used to build a train-validation dataset by assigning the polygons to one class associated with the buried relics and another class for the background field. With the Random Forest Classifier, we find that the polygons on the LSMS image segmentation layer can be successfully classified into the polygons of the buried relics and those of the background. Thus, we propose that these automatic classification methods applied to the GPR images of buried cultural heritage in this study can be useful to obtain consistent analyses results for planning excavation processes.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.3 no.2 s.5
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• pp.19-28
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• 2004

This paper shows how to compensate the skew from the traffic sign included in the natural image and extract the text. The research deals with the Process related to the array image. Ail the process comprises four steps. In the first fart we Perform the preprocessing and Canny edge extraction for the edge in the natural image. In the second pan we perform preprocessing and postprocessing for Hough Transform in order to extract the skewed angle. In the third part we remove the noise images and the complex lines, and then extract the candidate region using the features of the text. In the last part after performing the local binarization in the extracted candidate region, we demonstrate the text extraction by using the differences of the features which appeared between the tett and the non-text in order to select the unnecessary non-text. After carrying out an experiment with the natural image of 100 Pieces that includes the traffic sign. The research indicates a 82.54 percent extraction of the text and a 79.69 percent accuracy of the extraction, and this improved more accurate text extraction in comparison with the existing works such as the method using RLS(Run Length Smoothing) or Fourier Transform. Also this research shows a 94.5 percent extraction in respect of the extraction on the skewed angle. That improved a 26 percent, compared with the way used only Hough Transform. The research is applied to giving the information of the location regarding the walking aid system for the blind or the operation of a driverless vehicle

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.9
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• pp.127-135
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• 2013

In order to acquire images in low-light environments, it is usually necessary to adopt long exposure times or resort to flash lights. However, flashes often induce color distortion, cause the red-eye effect and can be disturbing to subjects. On the other hand, long-exposure shots are susceptible to subject-motion, as well as motion-blur due to camera shake when performed hand-held. A recently introduced technique to overcome the limitations of traditional low-light photography is that of multi-spectral flash. Multi-spectral flash images are a combination of UV/IR and visible spectrum information. The general idea is that of retrieving details from the UV/IR spectrum and color from the visible spectrum. However, multi-spectral flash images themselves are subject to color distortion and noise. This works presents a method to compute multi-spectral flash images so that noise can be reduced and color accuracy improved. The proposed approach is a previously seen optimization method, improved by the introduction of a weight map used to discriminate uniform regions from detail regions. The weight map is generated by applying canny edge operator and it is applied to the optimization process for discriminating the weights in uniform region and edge. Accordingly, the weight of color information is increased in the uniform region and the detail region of weight is decreased in detail region. Therefore, the proposed method can be enhancing color reproduction and removing artifacts. The performance of the proposed method has been objectively evaluated using long-exposure shots as reference.

• Journal of IKEEE
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• v.18 no.4
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• pp.471-477
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• 2014

In this paper, we propose a low-cost license plate recognition system based on smart cam system using Android. The proposed system consists of a portable device and server. Potable device Hardware consists of ARM Cortex-A9 (S5PV210) processor control unit, a power supply device, wired and wireless communication, input/output unit. We develope Linux kernel and dedicated device driver for WiFi module and camera. The license plate recognition algorithm is consisted of setting candidate plates areas with canny edge detector, extracting license plate number with Labeling, recognizing with template matching, etc. The number that is recognized by the device is transmitted to the remote server via the user mobile phone, and the server re-transfer the vehicle information in the database to the portable device. To verify the utility of the proposed system, user photographs the license plate of any vehicle in the natural environment. Confirming the recognition result, the recognition rate was 95%. The proposed system was suitable for low cost portable license plate recognition device, it enabled the stability of the system when used long time by using the Android operating system.

• Journal of Advanced Navigation Technology
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• v.16 no.6
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• pp.1101-1108
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• 2012

In this paper, we proposed an algorithm for real-time lane detecting against luminance variation using morphological image processing and edge-based region segmentation. In order to apply the most appropriate threshold value, the adaptive threshold was used in every frame, and perspective transform was applied to correct image distortion. After that, we designated ROI for detecting the only lane and established standard to limit region of ROI. We compared performance about the accuracy and speed when we used morphological method and do not used. Experimental result showed that the proposed algorithm improved the accuracy to 98.8% of detection rate and speed of 36.72ms per frame with the morphological method.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.6
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• pp.599-608
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• 2017

Cracks on the asphalt road surface can affect the speed of the car, the consumption of fuel, the ride quality of the road, and the durability of the road surface. Such cracks in roads can lead to very dangerous consequences for long periods of time. To prevent such risks, it is necessary to identify cracks and take appropriate action. It takes too much time and money to do it. Also, it is difficult to use expensive laser equipment vehicles for initial cost and equipment operation. In this paper, we propose an effective detection method of road surface defect using ROI (Region of Interest) setting and cany edge detection method using UAV image. The results of this study can be presented as efficient method for road surface flaw detection and maintenance using UAV. In addition, it can be used to detect cracks such as various buildings and civil engineering structures such as buildings, outer walls, large-scale storage tanks other than roads, and cost reduction effect can be expected.

• Journal of the Korea Society of Computer and Information
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• v.17 no.4
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• pp.51-58
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• 2012

Recently, 3D technology based on depth image is widely used in various fields including 3D space recognition, image acquisition, interaction, and games. Depth camera is used in order to produce depth image, various types of effort are made to improve quality of the depth image. In this paper, we suggests using area-based Canny edge detector to improve depth image in applying 3D technology based on depth camera. The suggested method provides improved depth image with pre-processing and post-processing by fixing image quality deterioration, which may take place in acquiring depth image in a limited environment. For objective image quality evaluation, we have confirmed that the image is improved by 0.42dB at maximum, by applying and comparing improved depth image to virtual view reference software. In addition, with DSCQS(Double Stimulus Continuous Quality Scale) evaluation method, we are reassured of the effectiveness of improved depth image through objective evaluation of subjective quality.