• Current Optics and Photonics
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• v.4 no.6
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• pp.530-539
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• 2020

Hyperspectral images feature a relatively narrow band and are easily disturbed by noise. Accurate estimation of the types and parameters of noise in hyperspectral images can provide prior knowledge for subsequent image processing. Existing hyperspectral-noise estimation methods often pay more attention to the use of spectral information while ignoring the spatial information of hyperspectral images. To evaluate the noise in hyperspectral images more accurately, we have proposed a spectral-spatial cooperative noise-evaluation method. First, the feature of spatial information was extracted by Gabor-filter and K-means algorithms. Then, texture edges were extracted by the Otsu threshold algorithm, and homogeneous image blocks were automatically separated. After that, signal and noise values for each pixel in homogeneous blocks were split with a multiple-linear-regression model. By experiments with both simulated and real hyperspectral images, the proposed method was demonstrated to be effective and accurate, and the composition of the hyperspectral image was verified.

• Journal of Urban Science
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• v.11 no.2
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• pp.1-8
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• 2022

This study proposed how to identify window defects by using photos uploaded by occupants during the pre-handover inspection of mulch-family housing. A total of 1168 door images were acquired to generate training data and validation data. Subsequently, through the proposed algorithms, every pixel in images labeled a door was binarized using the OTSU threshold, and then dark pixels were identified as defects. Experimental results demonstrated that our computer vision-based defects identification method detects the door with a recall of 57.9%, and door defects with 63.6%. Although it is still a challenge to automatically identify building defects because of the distortion and brightness of photos, this study has the potential to support better defects management. Ultimately, the improved pre-handover inspection may lead to increased customer satisfaction.

• Computers and Concrete
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• v.32 no.6
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• pp.615-623
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• 2023

Crack detection is an essential method to ensure the safety of dam concrete structures. Low-quality crack images of dam concrete structures limit the application of neural network methods in crack detection. This research proposes a modified attentional mechanism model to reduce the disturbance caused by uneven light, shadow, and water spots in crack images. Also, the focal loss function solves the small ratio of crack information. The dataset collects from the network, laboratory and actual inspection dataset of dam concrete structures. This research proposes a novel method for crack detection of dam concrete structures based on the U-Net neural network, namely AF-UNet. A mutual comparison of OTSU, Canny, region growing, DeepLab V3+, SegFormer, U-Net, and AF-UNet (proposed) verified the detection accuracy. A binocular camera detects cracks in the experimental scene. The smallest measurement width of the system is 0.27 mm. The potential goal is to achieve real-time detection and localization of cracks in dam concrete structures.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.1
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• pp.45-52
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• 2015

The interoperability of multi-source data has become more important due to various digital maps, produced from public institutions and enterprises. In this study, the automatic matching algorithm of multi-source building data using hierarchical matching was proposed. At first, we divide digital maps into blocks and perform the primary geometric registration of buildings with the ICP algorithm. Then, corresponding building pairs were determined by evaluating the similarity of overlap area, and the matching threshold value of similarity was automatically derived by the Otsu binary thresholding. After the first matching, we extracted error matching candidates buildings which are similar with threshold value to conduct the secondary ICP matching and to make a matching decision using turning angle function analysis. For the evaluation, the proposed method was applied to representative public digital maps, road name address map and digital topographic map 2.0. As a result, the F measures of matching and non-matching buildings increased by 2% and 17%, respectively. Therefore, the proposed method is efficient for the matching of building polygons from multi-source digital maps.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.6
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• pp.329-336
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• 2016

In the outdoor advertising and industrial sites, are trying to implement the LED electric bulletin board system that is based on image processing in order to express a variety of intention in real time. Recently, in various field, rather than simple text representation, the importance of intuitive communication using images is increasing. Thus, instead of outputting the simple input information for communication, a system that can output a real-time information being sought. Therefore, the system is directed to overcoming by converting the problem of mapping an image on a variety of conventional LED display that can not be output images, the possible image output formats. Using an LED of low power, it has developed to output the efficient messages and images within a limited resources. This paper provides a system capable of managing the LED display on the wireless network. Atmega2560, Wi-Fi module, using the server and Android applications client, rather than printing a text only, it is a system to reduce the load generated image output character output in to the conversion process as can be managed by the server.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.5
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• pp.443-450
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• 2014

To fulfill the recent increasement in the public and private demands for various spatial data, the central and local governments started to produce those data. The low-level land cover map has been produced since 2000, yet the production of high-level land covered map has started later in 2010, and recently, a few regions was completed recently. Although many studies have been carried to improve the quality of land that covered in the map, most of them have been focused on the low-level and mid-level classifications. For that reason, the study for high-level classification is still insufficient. Therefore, in this study, we suggested the automatic extraction of land readjustment for paddy land that updated in the mid-level land mapping. At the study, the RapidEye satellite images, which consider efficient to apply in the agricultural field, were used, and the high pass filtering emphasized the outline of paddy field. Also, the binary images of the paddy outlines were generated from the Otsu thresholding. The boundary information of paddy field was extracted from the image-to-map registrations and masking of paddy land cover. Lastly, the snapped edges were linked, as well as the linear features of paddy outlines were extracted by the regional Hough line extraction. The start and end points that were close to each other were linked to complete the paddy field outlines. In fact, the boundary of readjusted paddy fields was able to be extracted efficiently. We could conclude in that this study contributed to the automatic production of a high-level land cover map for paddy fields.

• The KIPS Transactions:PartB
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• v.17B no.5
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• pp.363-374
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• 2010

In this paper, we propose the method for extracting Homogeneity Threshold($H_T$) and for segmenting homogeneous regions by USRG(Unseeded Region Growing) with $H_T$. The $H_T$ is a criterion to distinguish homogeneity in neighbor pixels and is computed automatically from the original image by proposed method. Theoretical background for proposed method is based on the Otsu's single level threshold method. The method is used to divide a small local part of original image int o two classes and the sum($\sigma_c$) of standard deviations for the classes to satisfy special conditions for distinguishing as different regions from each other is used to compute $H_T$. To find validity for proposed method, we compare the original image with the image that is regenerated with only the segmented homogeneous regions and show up the fact that the difference between two images is not exist visually and also present the steps to regenerate the image in order the size of segmented homogeneous regions and in order the intensity that includes pixels. Also, we show up the validity of proposed method with various results that is segmented using the homogeneity thresholds($H^*_T$) that is added a coefficient ${\alpha}$ for adjusting scope of $H_T$. We expect that the proposed method can be applied in various fields such as visualization and animation of natural image, anatomy and biology and so on.

• The KIPS Transactions:PartB
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• v.9B no.2
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• pp.245-254
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• 2002

IC package marking inspection system using machine vision locates and identifies the target elements from input image, and decides the quality of marking by comparing the extracted target elements with the standard patterns. This paper proposes an adaptive multi-level thresholding (AMLT) method which is suitable for a series of operations such as locating the target IC package, extracting the characters, and detecting the Pinl dimple. It also proposes a dynamic matching unit selection (DMUS) method which is robust to noises as well as effective to catch out the local marking errors. The main idea of the AMLT method is to restrict the inputs of Otsu's thresholding algorithm within a specified area and a partial range of gray values. Doing so, it can adapt to the specific domain. The DMUS method dynamically selects the matching unit according to the result of character extraction and layout analysis. Therefore, in spite of the various erroneous situation occurred in the process of character extraction and layout analysis, it can select minimal matching unit in any environment. In an experiment with 280 IC package images of eight types, the correct extracting rate of IC package and Pinl dimple was 100% and the correct decision rate of marking quality was 98.8%. This result shows that the proposed methods are effective to IC package marking inspection.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.3
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• pp.223-235
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• 2020

In this study, we propose an approach for calibrating the elevation of a DEM (Digital Elevation Model), one of the key data in realizing unmanned aerial vehicle image-based precision agriculture. First of all, radiometric correction is performed on the orthophoto, and then ExG (Excess Green) is generated. The non-vegetation area is extracted based on the threshold value estimated by applying the Otsu method to ExG. Subsequently, the elevation of the DEM corresponding to the location of the non-vegetation area is extracted as EIFs (Elevation Invariant Features), which is data for elevation correction. The normalized Z-score is estimated based on the difference between the extracted EIFs to eliminate the outliers. Then, by constructing a linear regression model and correcting the elevation of the DEM, high-quality DEM is produced without GCPs (Ground Control Points). To verify the proposed method using a total of 10 DEMs, the maximum/minimum value, average/standard deviation before and after elevation correction were compared and analyzed. In addition, as a result of estimating the RMSE (Root Mean Square Error) by selecting the checkpoints, an average RMSE was derivsed as 0.35m. Comprehensively, it was confirmed that a high-quality DEM could be produced without GCPs.

• Korean Journal of Remote Sensing
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• v.35 no.5_2
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• pp.809-818
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• 2019

The Republic of Korea is very vulnerable to damage from storm and flood due to the rainfall phenomenon in summer and the topography of the narrow peninsula. The damage is recently getting worse because of the concentration rainfall. The accurate damage information production and analysis is required to prepare for future disaster. In this study, we analyzed the water surface area changes of Byeokjeong, Sajeom, Subu and Boryeong using Sentinel-1 satellite imagery. The surface area of the Sentinel-1 satellite, taken from May 2015 to August 2019, was preprocessed using RTC and image binarization using Otsu. The water surface area of reservoir was compared with the storage capacity from WAMIS and RIMS. As a result, Subu and Boryeong showed strong correlations of 0.850 and 0.941, respectively, and Byeokjeong and Sajeom showed the normal correlation of 0.651 and 0.657. Thus, SAR satellite imagery can be used to objective data as disaster management.