• Journal of the Optical Society of Korea
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• v.15 no.2
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• pp.152-154
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• 2011

We propose a novel method which detects convex and concave corners using radial projection. The sum of two neighbors' differences at the local maxima or minima of the radial projection is compared with the angle threshold for detecting corners. In addition, the use of oriented bounding box trees and partial circles makes it possible to detect the corners of complex shapes. The experimental results show that the proposed method can separately detect the convex and concave corners, and that this method is scale invariant.

• Journal of the Optical Society of Korea
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• v.16 no.3
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• pp.221-227
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• 2012

We propose optical proximity corrections (OPCs) for digital micromirror device (DMD)-based maskless lithography. A pattern writing scheme is analyzed and a theoretical model for obtaining the dose distribution profile and resulting structure is derived. By using simulation based on this model we were able to reduce the edge placement error (EPE) between the design width and the critical dimension (CD) of a fabricated photoresist, which enables improvement of the CD. Moreover, by experiments carried out with the parameter derived from the writing scheme, we minimized the corner-rounding effect by controlling light transmission to the corners of a feature by modulating a DMD.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.1
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• pp.160-170
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• 2010

This paper proposes a Haar Wavelet Feature Detector (HWFD) based on the Haar wavelet transform and average box filter. By decomposing the original image using the Haar wavelet transform, the proposed detector obtains the variance information of the image, making it possible to extract more distinctive features from the original image. For detection of interest points that represent the regions whose variance is the highest among their neighbor regions, we apply the average box filter to evaluate the local variance information and use the integral image technique for fast computation. Due to utilization of the Haar wavelet transform and the average box filter, the proposed detector is robust to illumination change, scale change, and rotation of the image. Experimental results show that even though the proposed method detects fewer interest points, it achieves higher repeatability, higher efficiency and higher matching accuracy compared with the DoG detector and Harris corner detector.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.36 no.5
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• pp.117-124
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• 2020

This study examined ornamental metals used as architectural members among metal artifacts excavated from ancient Buddhist temples and palaces in Korea. Through this, we approached the decorative characteristics of ancient architecture eaves. 1. The decorations used in eaves of Korean ancient architecture include roof-end tiles and ornamental metal. Through excavation examples, the technique of attaching ornamental metal to the rafters and corner rafters of high-ranking architectures in the 7th and 8th centuries (ornamental metal for rafter end, ornamental metal for corner rafter end), and tosu iron in the 10th century It seems to be fashionable. 2. Several buildings were built in ancient Buddhist temples and palaces. At this time, they differentiated ornamental metal according to the hierarchy of the building. The higher the hierarchy, the greater the difference in the number of ornamental metal installations, materials, and decoration techniques. In addition, ornamental metal used in eaves is an important factor in the discrimination of the times as the type, number of members, and patterns change depending on the era. 3. The great feature of the eaves metal decoration excavated in the 7th and 8th centuries is the attachment of ornamental metal to the rafters and horsetails. This seems to create a sense of grandeur by removing the weight of the roof and giving the impression constructed regardless of gravity by supporting it with non-material materials.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.8 no.2
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• pp.36-44
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• 2009

In this paper, we suggest a system of detecting a vehicle with lane violation, which can detect the vehicle with lane violation, by using the feature point tracking. The whole algorithm in the suggested system of detecting a vehicle with lane violation is composed of three stages such as feature extraction, register and tracking in feature for the tracking-targeted vehicle, and detecting a vehicle with lane violation. In the stage of feature extraction, the feature is extracted from the inputted image by sing the feature-extraction algorithm available for the real-time processing. The extracted features are again selected the racking-targeted feature. The registered feature is tracked by using NCC(normalized cross correlation). Finally, whether or not lane violation is finally detected by using information on the tracked features. As a result of experimenting the suggested system by using the acquired image in the section with a ban on intervention, the excellent performance was shown with 99.09% for positive recognition ratio and 0.9% for error ratio. The fast processing speed could be obtained in 34.48 frames per second available for real-time processing.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.6
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• pp.3086-3103
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• 2017

Video images captured by vehicle cameras often contain blurry or dithering frames due to inadvertent motion from bumps in the road or by insufficient illumination during the morning or evening, which greatly reduces the perception of objects expression and recognition from the records. Therefore, a real-time electronic stabilization method to correct fuzzy video from driving recorders has been proposed. In the first stage of feature detection, a coarse-to-fine inspection policy and a scale nonlinear diffusion filter are proposed to provide more accurate keypoints. Second, a new antiblurry binary descriptor and a feature point selection strategy for unintentional estimation are proposed, which brought more discriminative power. In addition, a new evaluation criterion for affine region detectors is presented based on the percentage interval of repeatability. The experiments show that the proposed method exhibits improvement in detecting blurry corner points. Moreover, it improves the performance of the algorithm and guarantees high processing speed at the same time.

• Journal of IKEEE
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• v.18 no.4
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• pp.536-543
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• 2014

Terrain classification is still a challenging issue in image processing, especially with high resolution satellite images. The well-known obstacles include low accuracy in the detection of targets, especially for the case of man-made structures, such as buildings and roads. In this paper, we present an efficient approach to classify and detect building footprints, foliage, grass and road from high resolution grayscale satellite images. Our contribution is to build a strong classifier using AdaBoost based on a combination of co-occurrence and Haar-like features. We expect that the inclusion of Harr-like feature improves the classification performance of the man-made structures, since Haar-like feature is extracted from corner features and rectangle features. Also, the AdaBoost algorithm selects only critical features and generates an extremely efficient classifier. Experimental result indicates that the classification accuracy of AdaBoost classifier is much higher than that of the conventional classifier using back propagation algorithm. Also, the inclusion of Harr-like feature significantly improves the classification accuracy. The accuracy of the proposed method is 98.4% for the target detection and 92.8% for the classification on high resolution satellite images.

• International Journal of CAD/CAM
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• v.9 no.1
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• pp.37-46
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• 2010

Existing methods for the registration of blurred images are efficient for the artificially blurred images or a planar registration, but not suitable for the naturally blurred images existing in the real image mosaic process. In this paper, we attempt to resolve this problem and propose a method for a distortion-free stitching of naturally blurred images for image mosaic. It adopts a multi-resolution and robust feature based inter-layer mosaic together. In each layer, Harris corner detector is chosen to effectively detect features and RANSAC is used to find reliable matches for further calibration as well as an initial homography as the initial motion of next layer. Simplex and subspace trust region methods are used consequently to estimate the stable focal length and rotation matrix through the transformation property of feature matches. In order to stitch multiple images together, an iterative registration strategy is also adopted to estimate the focal length of each image. Experimental results demonstrate the performance of the proposed method.

• Proceedings of the Korea Information Processing Society Conference
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• 2006.05a
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• pp.661-664
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• 2006

본 논문에서는 특징기반 물체추적을 위해 많이 사용되고 있는 KLT(Kanade-Lucas-Tomasi) 알고리즘을 소개하고, 이 알고리즘을 이용한 특징점(corner) 추출시, 영상에서 잡음의 영향이 KLT 알고리즘의 성능에 어떤 영향을 미치는지 잡음이 포함된 영상과 포함되지 않은 영상을 이용하여 안정된 특징점 추출을 위한 실험을 실시하고 비교 분석하였다.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.4
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• pp.116-123
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• 2009

Feature point matching, which is finding the corresponding points from two images with different viewpoint, has been used in various vision-based applications and the demand for the real-time operation of the matching is increasing these days. This paper presents a real-time feature point matching method by using a local descriptor derived by Zernike moments. From an input image, we find a set of feature points by using an existing fast corner detection algorithm and compute a local descriptor derived by Zernike moments at each feature point. The local descriptor based on Zernike moments represents the properties of the image patch around the feature points efficiently and is robust to rotation and illumination changes. In order to speed up the computation of Zernike moments, we compute the Zernike basis functions with fixed size in advance and store them in lookup tables. The initial matching results are acquired by an Approximate Nearest Neighbor (ANN) method and false matchings are eliminated by a RANSAC algorithm. In the experiments we confirmed that the proposed method matches the feature points in images with various transformations in real-time and outperforms existing methods.