• Transactions on Electrical and Electronic Materials
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• v.17 no.6
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• pp.363-368
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• 2016

Rectangular shape detection plays an important role in many image recognition systems. However, it requires continued research for its improved performance. In this study, we propose a strong rectangular shape detection algorithm, which combines the canny edge and line detection algorithms based on the perpendicularity and parallelism of a rectangle. First, we use the canny edge detection algorithm in order to obtain an image edge map. We then find the edge of the contour by using the connected component and find each edge contour from the edge map by using a DP (douglas-peucker) algorithm, and convert the contour into a polyline segment by using a DP algorithm. Each of the segments is compared with each other to calculate parallelism, whether or not the segment intersects the perpendicularity intersecting corner necessary to detect the rectangular shape. Using the perpendicularity and the parallelism, the four best line segments are selected and whether a determined the rectangular shape about the combination. According to the result of the experiment, the proposed rectangular shape detection algorithm strongly showed the size, location, direction, and color of the various objects. In addition, the proposed algorithm is applied to the license plate detecting and it wants to show the strength of the results.

• Journal of Korean Society of Transportation
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• v.26 no.4
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• pp.265-273
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• 2008

Video Image Processing (VIP) for traffic surveillance has been used not only to gather traffic information, but also to detect traffic conflicts and incident conditions. This paper presents a system development of gathering traffic information and conflict detection based on automatic calculation of pixel length within the detection zone on a Video Detection System (VDS). This algorithm improves the accuracy of traffic information using the automatic detailed line segmentsin the detection zone. This system also can be applied for all types of intersections. The experiments have been conducted with CCTV images, installed at a Bundang intersection, and verified through comparison with a commercial VDS product.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.2
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• pp.192-197
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• 2020

Multivariate processes, such as large scale power plants or chemical processes are operated in very hazardous environment, which can lead to significant human and material losses if a fault occurs. On-line monitoring technology, therefore, is essential to detect system faults. In this paper, the ICA-based fault detection method is conducted using three different multivariate process data. Fault detection procedure based on ICA is divided into off-line and on-line processes. The off-line process determines a threshold for fault detection by using the obtained dataset when the system is normal. And the on-line process computes statistics of query vectors measured in real-time. The fault is detected by comparing computed statistics and previously defined threshold. For comparison, the PCA-based fault detection method is also implemented in this paper. Experimental results show that the ICA-based fault detection method detects the system faults earlier and better than the PCA-based method.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.5
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• pp.1033-1039
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• 2012

In this paper, a simple and robust algorithm is proposed for detecting each steel plate from a image which contains several steel plates. Steel plate is characterized by line edge, so line detection is a fundamental task for analyzing and understanding of steel plate images. To detect the line edge, the proposed algorithm uses the small eigenvalue analysis. The proposed approach scans an input edge image from the top left corner to the bottom right corner with a moving mask. A covariance matrix of a set of edge pixels over a connected region within the mask is determined and then the statistical and geometrical properties of the small eigenvalue of the matrix are explored for the purpose of straight line detection. Using the detected line edges, each plate is determined based on the directional information and the distance information of the line edges. The results of the experiments emphasize that the proposed algorithm detects each steel plate from a image effectively.

• Communications for Statistical Applications and Methods
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• v.6 no.3
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• pp.729-748
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• 1999

In this paper we present combined estimator of capture-recapture and line transect model using bivariate detection function and detection probability according to objects being in cluster population. Here bivariate detection function use distance and cluster size. The simulation shows that combined estimator approaches the more true value the larger size parameter. Therefore this estimator using the bivariate detection function is more efficient in estimate the population size and density by size parameter.

• Journal of Korean Institute of Industrial Engineers
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• v.20 no.4
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• pp.113-134
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• 1994

A radial basis hybrid neural network (RHNN) is presented for an on-line detection of machine condition change. Two-phase modeling by RHNN is designed for describing a machine condition process and for predicting future signal. A moving block procedure is also designed for detecting a process change. A fast on-line learning algorithm, the recursive least square estimation, is introduced. Experimental results showed the RHNN could be utilized efficiently for on-line machine condition monitoring.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.6B
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• pp.993-1001
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• 2000

This research features a method that quantitatively evaluates the performance of edge detection algorithms. Contrary to conventional methods that evaluate the performance of edge detection as a function of the amount of noise added to he input image, the proposed method is capable of assessing the performance of edge detection algorithms based on chosen parameters that influence the performance of edge detection. We have proposed a quantitative measure, called average performance index, that compares the average performance of different edge detection algorithms. We have applied the method to the commonly used edge detectors, Sobel, LOG(Laplacian of Gaussian), and Canny edge detectors for noisy images that contain straight line edges and curved line edges. Two kinds of noises i.e, Gaussian and impulse noises, are used. Experimental results show that our method of quantitatively evaluating the performance of edge detection algorithms can facilitate the selection of the optimal dge detection algorithm for a given task.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.411-420
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• 1999

In geology, lineament features have been used to identify geological events, and many of scientists have been developed the algorithm that can be applied with the computer to recognize the lineaments. We choose several edge detection filter, line detection filters and Hough transform to detect an edge, line, and to vectorize the extracted lineament features, respectively. firstly the edge detection filter using a first-order derivative is applied to the original image In this step, rough lineament image is created Secondly, line detection filter is used to refine the previous image for further processing, where the wrong detected lines are, to some extents, excluded by using the variance of the pixel values that is composed of each line Thirdly, the thinning process is carried out to control the thickness of the line. At last, we use the Hough transform to convert the raster image to the vector one. A Landsat image is selected to extract lineament features. The result shows the lineament well regardless of directions. However, the degree of extraction of linear feature depends on the values of parameters and patterns of filters, therefore the development of new filter and the reduction of the number of parameter are required for the further study.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.44 no.6
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• pp.36-42
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• 2007

According to the increased demand of high quality multimedia content, it needs to recover an old movies. But the film of old movie is damaged with line scratches and dust. In this paper, the detection and restoration algorithm of B/W line scratch is proposed. Our scheme estimates and interpolates the damaged partial information of line scratch using the linear recursive curve trace which consider the intensity values of left and right region of line scratch and then median filtering processed. As a result, the film image PSNR 44.68 with B/W line scratch is increased up to 48.60 and the intensity of the interpolate pixel is approached about 14 against the pixel of original image.

• 한국지형공간정보학회:학술대회논문집
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• 2002.11a
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• pp.133-140
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• 2002

This paper presents a method for line segment grouping used for detection of various building roofs. First, by using edge preserving filtering. noise is eliminated and then images are segmented by watershed algorithm, which preserves location of edge pixels. To extract line segments between control points from boundary of each region, we calculate curvature of each pixel on the boundary and then find the control points. Line linking is performed according to direction and length of line segments and finally the location of line segments is adjusted using gradient magnitudes of all pixels of the line segment. The algorithm has been applied to aerial imagery and the results show accurate building roof detection.