• ETRI Journal
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• v.35 no.2
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• pp.207-217
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• 2013

This paper introduces the modular cellular neural network (CNN), which is a new CNN structure constructed from nine one-layer modules with intercellular interactions between different modules. The new network is suitable for implementing many image processing operations. Inputting an image into the modules results in nine outputs. The topographic characteristic of the cell interactions allows the outputs to introduce new properties for image processing tasks. The stability of the system is proven and the performance is evaluated in several image processing applications. Experiment results on texture segmentation show the power of the proposed structure. The performance of the structure in a real edge detection application using the Berkeley dataset BSDS300 is also evaluated.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.5
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• pp.18-24
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• 2008

In this paper we propose a novel method which improves digital image stabilization performance using adoptive selection of motion estimation area. Candidate area for motion estimation is decided and through two processes, multi image reference and edge energy distinction, final motion estimation area is selected. Then Motion estimation and compensation is following in selected area. Experimental results show that proposed method improves performance of digital image stabilization.

• Journal of the Korea Society of Computer and Information
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• v.21 no.12
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• pp.125-130
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• 2016

Automatic quality inspection by computer vision can be applied and give a solution to the pharmaceutical industry field. Pharmaceutical capsule can be easily affected by flaws like dents, cracks, holes, etc. In order to solve the quality inspection problem, it is required computationally efficient image processing technique like thresholding, boundary edge detection and segmentation and some automated systems are available but they are very expensive to use. In this paper, we have developed a dented capsule image processing technique using edge-based image segmentation, TLS(Total Least Squares) curve fitting technique and adopted low cost camera module for capsule image capturing. We have tested and evaluated the accuracy, training and testing time of the classification recognition algorithms like PCA(Principal Component Analysis), ICA(Independent Component Analysis) and SVM(Support Vector Machine) to show the performance. With the result, PCA, ICA has low accuracy, but SVM has good accuracy to use for classifying the dented capsule.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.40 no.4
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• pp.394-401
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• 1991

B-spline function is generally used for an image interpolation because of its smoothness and continuity, but it accompanies a large amount of blurring effect. In this paper, a space-variant B-spline interpolation function is proposed through deblurring process followed by de-aliasing process. The proposed function has parametric expression and performs smoothing and edge-enhancement adaptively in the interpolation process according to local property of the image. Application of this function to image enlargement, rotation, and curve representation producted good results. Even in the presence of noise, noise smoothing effect as well as edge-enhancement were observed in the image interpolation process.

• Journal of Information Processing Systems
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• v.16 no.6
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• pp.1479-1494
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• 2020

As an important part of image processing, image demosaicking has been widely researched. It is especially necessary to propose an efficient interpolation algorithm with good visual quality and performance. To improve the limitations of residual interpolation (RI), based on RI algorithm, minimalized-Laplacian RI (MLRI), and iterative RI (IRI), this paper focuses on adaptive RI (ARI) and proposes an improved ARI (IARI) algorithm which obtains more distinct R, G, and B colors in the images. The proposed scheme fully considers the brightness information and edge information of the image. Since the ARI algorithm is not completely adaptive, IARI algorithm executes ARI algorithm twice on R and B components according to the directional difference, which surely achieves an adaptive algorithm for all color components. Experimental results show that the improved method has better performance than other four existing methods both in subjective assessment and objective assessment, especially in the complex edge area and color brightness recovery.

• Journal of Advanced Marine Engineering and Technology
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• v.24 no.2
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• pp.82-89
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• 2000

Edge detection is the first step and very important step in image analysis. In this paper, proposed edge detection operators based on informations of edge types and it is different from other classical edge detection operators such as gradient and surface fitting operators. The first, we defined characteristics of edge types such as localization, thinness, length. The second, we defined valid edge types and ideal edge pixel positions in $3\times3$window based on edge characteristics of edge types. And we proposed edge detection algorithm and twelve windows based on valid edge types. In specially, proposed algorithm was shown better performence of edge detection than other operators such as gradient operator and the LoG(Laplacian of Gaussian) operator of zero crossings.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.169-171
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• 2001

Unlike rigid objects, the edge intensity of a gaseous object is various along the object boundary (edge intensities of some pixels on a gaseous object boundary are weaker than those of small rigid objects or noise itself). Therefore, the conventional edge detectors may not adequately detect boundary-like edge pixels for gaseous objects. In this paper A new methodology for segmenting gaseous object images is introduced. Proposed method consists of fuzzy-based boundary detector applicable to gaseous as well as rigid objects and concave region filling to recover object regions.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2092-2095
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• 2003

In this paper, we propose an algorithm to extract rectangular object area such 3s Data Matrix two-dimensional barcode using edge tracing-based linear feature detection. Hough transform is usually employed to detect lines of edge map. However, it requires parametric image space, and does not find the location of end points of the detected lines. Our algorithm detects end points of the detected lines using edge tracing and extracts object area using its shape information.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10b
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• pp.589-591
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• 2003

영상에 추출된 윤곽선(Edge)은 물체의 핵심적인 형태정보를 포함하고 있어서 영상인식과 분석의 근간이 되고 있다. 따라서 정확한 윤곽선 검출을 위한 많은 연구가 진행되고 있으며 그 응용분야도 다양하다. 그러나 정작 추출된 윤곽선 정보를 효율적으로 표현하고 활용하기 위한 표준화된 자료구조에 대한 연구는 많지 않아서 연구결과의 공유를 어렵게 하고 있다. 본 논문에서는 검출된 윤곽선을 효율적으로 표현, 관리, 검색, 조작하기 위한 자료클래스를 설계구현 함으로서 윤곽선검출 알고리즘의 표준화와 재사용을 촉진시키고 검출된 다양한 응용을 가능하게 한다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.4
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• pp.317-325
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• 2020

UAV (Unmanned Aerial Vehicle) photogrammetry has recently emerged as a means of obtaining highly precise and rapid spatial information due to its cost-effectiveness and high efficiency. However, current procedures or regulations for quantitative quality verification methods and certification processes for UAV-images are insufficient. In addition, the current verification method for image quality is not evaluated by an MTF (Modulation Transfer Function) analysis or edge response analysis, which can analyze the degree of contrast including image resolution, and only relies on the GSD (Ground Sample Distance) analysis. Therefore, in this study, the edge response analysis using a Slanted edge target was performed along with GSD analysis to confirm the necessity of analyzing edge response analysis in UAV-images quality analysis. Furthermore, a Matlab GUI-based software tool was developed to help streamline the edge response analysis. As a result, we confirmed the need for edge response analysis since the outputs of the edge response analysis from the same GSD had significantly different outcomes. Additionally, we found that the quality of the edge response analysis of UAV-images is proportional to the performance of the camera mounted on the UAV.