• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.9
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• pp.1697-1702
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• 2017

This paper proposes a forward vehicle movement estimation algorithm for the image-based forward collision warning. The road region in the acquired image is designated as a region of interest (ROI) and a distance look up table (LUT) is made in advance. The distance LUT shows horizontal and vertical real distances from a reference pixel as a test vehicle position to any pixel as a position of a vehicle on the ROI. The proposed algorithm detects vehicles in the ROI, assigns labels to them, and saves their distance information using the distance LUT. And then the proposed algorithm estimates the vehicle movements such as approach distance, side-approaching and front-approaching velocities using distance changes between frames. In forward vehicle movement estimation test using road driving videos, the proposed algorithm makes the valid estimation of average 98.7%, 95.9%, 94.3% in the vehicle movements, respectively.

• Journal of Cadastre & Land InformatiX
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• v.52 no.1
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• pp.95-104
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• 2022

This study aims to find the exact area of the forest fire in Okgye-myeon, Gangneung, April 4, 2019. Since there is a gradient in our country's forests, we should find a surface area that takes into account The 5th numerical clinical map provided by the DEM and the Korea Forest Service provided by the National Geographic Information Service was used. In DEM, the center point of each pixel was created and all points were connected. The length of the connecting line is determined by the spatial resolution of the pixel and the cosine value, and the surface area is obtained along with the height value, which is called the Slope-Aspect algorithm. The surface area and floor area of the forest were shown according to the tree species and types of forest, and their quantitative numerical differences proved the validity of this study.

• Journal of Korea Multimedia Society
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• v.13 no.3
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• pp.417-429
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• 2010

Though background subtraction has been widely studied for last decades, it is still a poorly solved problem especially when it meets real environments. In this paper, we first address some common problems for background subtraction that occur in real environments and then those problems are resolved by improving an existing GMM-based background modeling method. First, to achieve low computations, fixed point operations are used. Because background model usually does not require high precision of variables, we can reduce the computation time while maintaining its accuracy by adopting fixed point operations rather than floating point operations. Secondly, to avoid erroneous backgrounds that are induced by high pedestrian traffic, static levels of pixels are examined using shot-time statistics of pixel history. By using a lower learning rate for non-static pixels, we can preserve valid backgrounds even for busy scenes where foregrounds dominate. Finally, to adapt rapid illumination changes, we estimated the intensity change between two consecutive frames as a linear transform and compensated learned background models according to the estimated transform. By applying the fixed point operation to existing GMM-based method, it was able to reduce the computation time to about 30% of the original processing time. Also, experiments on a real video with high pedestrian traffic showed that our proposed method improves the previous background modeling methods by 20% in detection rate and 5~10% in false alarm rate.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.11 no.2
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• pp.69-77
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• 1993

Investigation is given to the detailed procedure of a computer assisted automatic correction for scanning errors of the digital images of close-range photographs scanned by the CCD camera scanner. After determination of the exterior orientation parameters, photo coordinates of the all pixels were calculated using collinearity equation. For the generation of geometric corrected image from the photo coordinates of the all pixels, inverse-weighted-distance average method was used. And the accuracy of the resulting new image was checked comparing its image coordinates with there corresponding ground coordinates for the check points.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.484-486
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• 2018

Recently, with the increase in use of digital equipment in various fields, the importance of image and signal processing is increasing. However, many types of noise are generated during transmission and reception of digital signal, causing errors. For this reason, noise removal is mandatorily performed during pre-processing phase in many fields. In the present paper, noise is classified through noise evaluation, and noise removal is performed to remove impulse noise and noise with AWGN-added noise. And, proposed is an algorithm which utilizes modified Gaussian filter and directional effective pixels according to noise type. Simulation results show superior noise-removal characteristics, and for objective evaluation, compared with conventional methods.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.5
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• pp.125-134
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• 2004

This paper proposes a method for recognition of traffic signs using wavelet transform and shape information from the segmented traffic sign regions. It first segments traffic sign candidate regions by connected component algorithm from binary images, obtained by utilizing the RGB color ratio of each pixel in the image, and then extracts actual traffic sign regions based on their symmetries on X- and Y-axes. In the recognition stage, it utilizes shape information including moment edge correlogram and the number of crossings which concentric circular patterns from region center intersects with frequency information extracted by wavelet transform It finally performs recognition by measuring similarity with the templates in the database. The experimental results show the validity of the proposed method from geometric transformations and environmental factors.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.9
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• pp.1269-1282
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• 1993

In this paper, we propose to method of recognizing printed chinese characters which combine the coventional deterministic methods and the neural networks. Firstly, we extract four directional vector of strokes from chinese characters. Secondly, we make the mesh of the center of gravity in the vector and then constitute the H x8 feature matrix using black pixel lenth from each meshs. This normalized feature matrix value offer as the input of neural network for classifying into the 14 character types. And this calssified character classify again into Busu group by the Busu recognizing neural network. Finally, we recognize each characters using the distance of similarity between input characters and reference characters. The usefulness of the proposed algorithm is evaluated by experimenting with recognizing the chinese characters.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2004.05b
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• pp.599-602
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• 2004

In this paper, an automatic setting method of control parameters for robot scorer is proposed. First we acquisited some local image lesions including robots and ball patch, and sampled the regions to RCB value. Edge operator is applied to get magnitude of gradient at each pixel. Some effective patch regions can be detected by magnitude of gradient, and YUV value at each pixel in patch lesions can be obtained. We can determine control parameters of robot soccer using luminance component of YUV. The proposed method is applied to robot soccer image to detect initial patch value and get control parameters adaptively in light variation.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.17 no.3
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• pp.225-231
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• 1999

In statistical analysis of remotely sensed data, means and variances of each classes are used as the basis of statistical similarity determination. Therefore, the overall accuracy of classification is affected by the training results. It is assumed that the ideal distributions of pixel values follow normal distributions, but practically they have some aggregations and biases. non anomalies of distribution can affect the classification results greatly as well as the variances of training results. In this study, relationships between the inferential variances of the training sets and the distributions of pixel values are examined. and the resulting changes of classification results are studied. Furthermore, the training method which minimizes the effect of underestimation of variances is proposed.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.5
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• pp.615-621
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• 2009

TFT-LCD image consists of ununiform background, random noises and target defect signal components. Defects in TFT-LCD have some intensity variations compared to background region. It is sometimes difficult for human inspectors to figure out. In this paper, we propose multi-level threshold scheme for detection of the real defect using probability density function with Parzen Window. The experimental results show that the proposed algorithms produce promising results and can be applied to automated inspection systems for finding defects in the TFT-LCD image.