• Journal of the Korea Society of Computer and Information
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• v.24 no.6
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• pp.57-65
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• 2019

In this paper, we study on finding the rail space in elevators by analyzing each image captured with CCD camera. We propose a method that applies one-dimensional matched filter to the pixels of a selected search space in the vertical line at a horizontal position and decides the position with the thickness of the space being represented by a black thick line in captured images. The pattern similarity representing how strongly the associated image pixels resemble with the thick line is defined and calculated with respect to each position along the vertical line of pixels. The position and thickness of the line are decided from the point having the maximum in pattern similarity graph. In the experiments of the proposed method under different illuminational conditions, it is observed that all the pattern similarity graphs show similar shape around door area independent of the conditions and the method can effectively detect the rail space if the rails are illuminated with even weak light. The method can be used for real-time embedded systems because of its simple algorithm, in which it is implemented in simple structure of program with small amount of operations in comparison with the conventional approaches using Canny edge detection and Hough transform.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.382-383
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• 2016

Canny edge detection of the image is composed of four kinds of Gaussian filter, gradient calculation, Non-maximum suppression, and Hypothesis Thresholding. Feature is the ratio between the vehicle body, the windows, and the wheels obtained from the edge image. Features that make the proportion of these vehicles are different for each respective model. We have identified by application of this algorithm where only a small vehicle.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.220-228
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• 2022

Image always carry useful information, detecting a text from scene images is imperative. The proposed work's purpose is to recognize scene text image, example boarding image kept on highways. Scene text detection on highways boarding's plays a vital role in road safety measures. At initial stage applying preprocessing techniques to the image is to sharpen and improve the features exist in the image. Likely, morphological operator were applied on images to remove the close gaps exists between objects. Here we proposed a two phase algorithm for extracting and recognizing text from scene images. In phase I text from scenery image is extracted by applying various image preprocessing techniques like blurring, erosion, tophat followed by applying thresholding, morphological gradient and by fixing kernel sizes, then canny edge detector is applied to detect the text contained in the scene images. In phase II text from scenery image recognized using MSER (Maximally Stable Extremal Region) and OCR; Proposed work aimed to detect the text contained in the scenery images from popular dataset repositories SVT, ICDAR 2003, MSRA-TD 500; these images were captured at various illumination and angles. Proposed algorithm produces higher accuracy in minimal execution time compared with state-of-the-art methodologies.

• Journal of Korea Multimedia Society
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• v.25 no.5
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• pp.661-669
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• 2022

The purpose of this study is to compare and analyze Deep Learning (DL) and Digital Image Processing (DIP) techniques using the results of the glottis segmentation of the two methods followed by the quantification of the asymmetric degree of the vocal cord mucosa. The data consists of 40 normal and abnormal images. The DL model is based on Deeplab V3 architecture, and the Canny edge detector algorithm and morphological operations are used for the DIP technique. According to the segmentation results, the average accuracy of the DL model and the DIP was 97.5% and 94.7% respectively. The quantification results showed high correlation coefficients for both the DL experiment (r=0.8512, p<0.0001) and the DIP experiment (r=0.7784, p<0.0001). In the conclusion, the DL model showed relatively higher segmentation accuracy than the DIP. In this paper, we propose the clinical applicability of this technique applying the segmentation and asymmetric quantification algorithm to the glottal area in the laryngoscopic images.

• Journal of the Korea Society of Computer and Information
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• v.14 no.10
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• pp.93-99
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• 2009

The color harmony in video was no way to know for giving pleasure. By identifying these color harmony, it gives order, clarity, similarity, contrast, etc. Therefore, to identify the color balance is very important. Color Harmony identify the color is whether the harmony by color harmony theory of Munsell, Ostwald, Firren, Moon & Spenser, Itten, Chevreul, and Judd etc. One of these methods, we identify color harmonies of 2 colors, 3 colors, 4 colors, 5 colors and 6 colors using Itten's color balance. Identification is using by Canny edge extraction, labeling and clustering, and color extraction and harmony etc. By identifying this color harmonies, we have laid the foundation of emotional database construction and emotional recognition.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.399-408
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• 2020

The construction industry is suffering from aging workers, frequent accidents, as well as low productivity. With the rapid development of information technologies in recent years, automatic construction, especially automatic cranes, is regarded as a promising solution for the above problems and attracting more and more attention. However, in practice, limited by the complexity and dynamics of construction environment, manual inspection which is time-consuming and error-prone is still the only way to recognize the search object for the operation of crane. To solve this problem, an image-processing-based automated object recognition approach is proposed in this paper, which is a fusion of Convolutional-Neutral-Network (CNN)-based and traditional object detections. The search object is firstly extracted from the background by the trained Faster R-CNN. And then through a series of image processing including Canny, Hough and Endpoints clustering analysis, the vertices of the search object can be determined to locate it in 3D space uniquely. Finally, the features (e.g., centroid coordinate, size, and color) of the search object are extracted for further recognition. The approach presented in this paper was implemented in OpenCV, and the prototype was written in Microsoft Visual C++. This proposed approach shows great potential for the automatic operation of crane. Further researches and more extensive field experiments will follow in the future.

• 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 Korea Academia-Industrial cooperation Society
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• v.13 no.2
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• pp.872-877
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• 2012

We proposes the method to detect the Hangul character region from natural image using topological structural feature of Hangul grapheme. First, we transform a natural image to a gray-scale image. Second, feature extraction performed with edge and connected component based method, Edge-based method use a Canny-edge detector and connected component based method applied the local range filtering. Next, if features are not corresponding to the heuristic rule of Hangul character, extracted features filtered out and select candidates of character region. Next, candidates of Hangul character region are merged into one Hangul character using Hangul character merging algorithm. Finally, we detect the final character region by Hangul character class decision algorithm. Experimental result, proposed method could detect a character region effectively in images that contains a complex background and various environments. As a result of the performance evaluation, A proposed method showed advanced results about detection of Hangul character region from mobile image.

• 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 Institute of Information and Communication Engineering
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• v.19 no.12
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• pp.2949-2955
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• 2015

Recently, with the sharp increase of digital visual media such as photographs, animations, and digital videos, it has been necessary to generate mosaic blocks in a static or dynamic image intentionally or unintentionally. In this paper, we suggest a new method for detecting mosaic blocks contained in a color image using boundary features. The suggested method first extracts Canny edges in the image and finds candidate mosaic blocks with the boundary features of mosaic blocks. The method then determines real mosaic blocks after filtering out non-mosaic blocks using geometric features like size and elongatedness features. Experimental results show that the proposed method can detect mosaic blocks robustly rather than other methods in various types of input images.