• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.3D
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• pp.519-525
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• 2006

The traditional area-based matching or efficient matching methods using epipolar geometry and height restriction of stereo images, which have a confined search space for image matching, have still some disadvantages such as mismatching and timeconsuming, especially in the dense metropolitan city that very high and similar buildings exist. To solve these problems, a new image matching method through building recognition has been presented. This paper described building recognition in color stereo images using edge and color information as a elementary study of new matching scheme. We introduce the modified Hausdorff distance for using edge information, and the modified color indexing with 3-D RGB histogram for using color information. Color information or edge information alone is not enough to find conjugate building pairs. For edge information only, building recognition rate shows 46.5%, for color information only, 7.1%. However, building recognition rate distinctly increase 78.5% when both information are combined.

• Journal of the Korea Society of Computer and Information
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• v.3 no.2
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• pp.85-97
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• 1998

The existing edge detection methods can not represent the real edge of object at fitting point or detect the edge which has unsufficient connecting trait. Especially, the two-fold thick edge detected by these methods cannot coincide real boundary of subject and it's location. To overcome these problems, we introduce the Genetic Algorithm(GA) in edge detection. The energy function is the value of fixel's satisfaction degree to edge condition. And it consists of the fitness value to image formation type, fitness value to connecting trait to it's neighboring edge and evalulation function which can represents the edge at fitting point as one fixel. This method is superior to remove the noise in edge detection than the existing methods. And it also detects the clear and exact edge because it can find the one fixel which is located at fitting point and has strong connecting trait.

• Journal of Information Processing Systems
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• v.9 no.1
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• pp.141-156
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• 2013

In this paper we propose a method to detect human faces in color images. Many existing systems use a window-based classifier that scans the entire image for the presence of the human face and such systems suffers from scale variation, pose variation, illumination changes, etc. Here, we propose a lighting insensitive face detection method based upon the edge and skin tone information of the input color image. First, image enhancement is performed, especially if the image is acquired from an unconstrained illumination condition. Next, skin segmentation in YCbCr and RGB space is conducted. The result of skin segmentation is refined using the skin tone percentage index method. The edges of the input image are combined with the skin tone image to separate all non-face regions from candidate faces. Candidate verification using primitive shape features of the face is applied to decide which of the candidate regions corresponds to a face. The advantage of the proposed method is that it can detect faces that are of different sizes, in different poses, and that are making different expressions under unconstrained illumination conditions.

• Journal of Korea Multimedia Society
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• v.17 no.12
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• pp.1393-1401
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• 2014

This paper presents an adaptive image interpolation method using a pixel-based neighbor embedding which is modified from the patch-based neighbor embedding of contemporary super resolution algorithms. Conventional interpolation methods for high resolution detect at least 16-directional edges in order to remove zig-zaging effects and selectively choose the interpolation strategy according to the direction and value of edge. Thus, they require much computation and high complexity. In order to develop a simple interpolation method preserving edge's directional shape, the proposed algorithm adopts the simplest Haar wavelet and suggests a new pixel-based embedding scheme. First, the low-quality image but high resolution, magnified into 1 octave above, is acquired using an adaptive 8-directional interpolation based on the high frequency coefficients of the wavelet transform. Thereafter, the pixel embedding process updates a high resolution pixel of the magnified image with the weighted sum of the best matched pixel value, which is searched at its low resolution image. As the results, the proposed scheme is simple and removes zig-zaging effects without any additional process.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.3
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• pp.698-704
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• 2013

The image process for image enhancement applies differently the same algorithm for each application. So, the optimized value for each application is required. In this paper, the X-ray medical image using a high-pass filter was improved edges. The result image was improved edge and the contrast of flat area using a constant multiplier and offset. Therefore, the high-frequency emphasis filter optimized for medical image is required. These optimized values are the gaussian high-pass filter, the distance of cutoff frequency=0.05 and offset=0.5. From the result of optimaized simulation, The proposed method has enhanced contrast and edge of the image in the contrast of existing mothods.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2004.03a
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• pp.273-278
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• 2004

Feature identification is one of the largest issue in high spatial resolution satellite imagery. A popular method associated with this feature identification is image segmentation to produce image segments that are more likely to features interested. Here, it is, proposed that combination of edge extraction and region growing methods for image segments were used to improve the result of image segmentation. At the intial step, an image was segmented by edge detection method. The segments were assigned IDs, and polygon topology of segments were built. Based on the topology, the segments were tested their similarities with adjacent segments using multivariate analysis. The segments that have similar spectral characteristics were merged into a region. The test application shows that the segments composed of individual large, spectrally homogeneous structures, such as buildings and roads, were merged into more similar shape of structures.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.7
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• pp.1393-1398
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• 2004

Image is degraded by several causes such as the process of acquisition, storage and transmission. To restore those images, many researches have been continued. Centrally methods to restore degraded image by AWGN(additive white gaussian noise) a.e mean filter and wiener filter. Especially, mean filter is superior in noise reduction of area that is a small change of luminosity. But mean filter brings about the effect smoothing edge components of the image, because it does'nt consider characteristics of the image. So in this paper we propose an image restoration method compounding respective images adding established weights, after filtering with mean filter and powerful wiener filter in both improvement of contrast and preservation of edge components.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2006.05a
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• pp.825-828
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• 2006

The most commonly used techniques for image magnification are interpolation based. However, the magnified images produced by this technique often appear blocking and blurring phenomenon when the image is enlarged. In this paper, we enhanced image magnification algorithm using edge information. The proposed algorithm not used interpolation based but by using sub-band of input image in extrapolation. According to mapping relationship in pyramid, we calculated up-band information to magnify. In experiments, the proposed model shows solved the problem of image loss like the blocking and blurring phenomenon. As the result, it is faster and higher resolution than traditional magnification algorithms.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.359-360
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• 2021

The pixel-based processing of an image refers to a process of converting a value of one pixel only depending on the value of the current pixel, regardless of the value of another pixel. Pixel-based processing is used as the most basic operation in many fields such as image conversion, image enhancement, and image synthesis. There are processing methods such as arithmetic operation, histogram smoothing, and contrast stretching. In this paper, in order to clearly distinguish the tidal flat region from the tidal flat image of the west coast taken with a drone, we seek a method to find an efficient outline using pixel-based processing in the boundary detection part of the pre-processing process.

• Journal of the Korea Society of Computer and Information
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• v.16 no.7
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• pp.149-156
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• 2011

This paper proposes the effective edge selection algorithm for the rapid processing time and low memory usage of efficient graph-based image segmentation on mobile device. The graph-based image segmentation algorithm is to extract objects from a single image. The objects are consisting of graph edges, which are created by information of each image's pixel. The edge of graph is created by the difference of color intensity between the pixel and neighborhood pixels. The object regions are found by connecting the edges, based on color intensity and threshold value. Therefore, the number of edges decides on the processing time and amount of memory usage of graph-based image segmentation. Comparing to personal computer, the mobile device has many limitations such as processor speed and amount of memory. Additionally, the response time of application is an issue of mobile device programming. The image processing on mobile device should offer the reasonable response time, so that, the image segmentation processing on mobile should provide with the rapid processing time and low memory usage. In this paper, we demonstrate the performance of the effective edge selection algorithm, which effectively controls the edges of graph for the rapid processing time and low memory usage of graph-based image segmentation on mobile device.