• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.2
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• pp.126-136
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• 1998

One of the central problems in sereo matching is the selectionof the optimal window sizes for comparing image regions. The window size must be large enough to include enough variation for reliable matching, but small enough to avoid the effect of projection distortion. This paper discusses these problems with some novel algorithm based on iterativediffusion process at different disparity hypotheses. Also this paper proposes four kinds of diffusion algorithms to preseve discontinuity in stereo matching. We present and discuss extensive empirical results of algorithms based on various sets of synthetic and real image.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.5
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• pp.1109-1122
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• 1996

Generally, moving images contain the various components in motions, which reange from a static object and background to a fast moving object. To extract the accurate motion parameters, we must consider the various motions. That requires a wide search egion in motion estimation. The wide search, however, causes a high computational complexity. If we have a few knowledge about the motion direction and magnitude before motion estimation, we can determine the search location and search window size using the already-known information about the motion. In this paper, we present a local adaptive motion estimation approach that predicts a block motion based on spatio-temporal neighborhood blocks and adaptively defines the search location and search window size. This paper presents a technique for reducing computational complexity, while having high accuracy in motion estimation. The proposed algorithm is introduced the forward and backward projection techniques. The search windeo size for a block is adaptively determined by previous motion vectors and prediction errors. Simulations show significant improvements in the qualities of the motion compensated images and in the reduction of the computational complexity.

• Journal of the Korean Institute of Telematics and Electronics
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• v.16 no.6
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• pp.16-21
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• 1979

Two dimensional kernels which reconstruct a tomographic image from a blurred one formed by simple back-projection are investigated in the frequency domain and their performances are compared. The kernels are derived from a point spread function of the tomographic system and have the form of a ramp filter modified by several window functions to suppress ringings or artifacts in the reconstruction. Computer simulation using computer-generated phantom image data with different filter functions has been carried out. In this simulation, it is found that the computation time for 2-D reconstruction is much less than that of 1-D convolution method by a factor of ten or more whereas the reconstructed image quality of the former is far poorer than the latter. In 2-D reconstruction heavy windowing results in less noisy reconstruction but details smear out in this case. The trade-offs between these points are considered.

• Journal of Broadcast Engineering
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• v.22 no.6
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• pp.850-853
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• 2017

In this paper, we propose an image analysis system based on omnidirectional image and object tracking image display using super wide angle camera. In order to generate spherical images, the projection process of converting from two wide-angle images to the equirectangular panoramic image was performed and the spherical image was expressed by converting rectangular to spherical coordinate system. Object tracking was performed by selecting the desired object initially, and KCF(Kernelized Correlation Filter) algorithm was used so that robust object tracking can be performed even when the object's shape is changed. In the initial dialog, the file and mode are selected, and then the result is displayed in the new dialog. If the object tracking mode is selected, the ROI is set by dragging the desired area in the new window.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.10C
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• pp.975-981
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• 2007

In this paper, we propose a spatially adaptive image restoration algorithm using local and statistics and estimated noise. The ratio of local mean, variance, and maximum values with different window size is used to constrain the solution space, and these parameters are computed at each iteration step using partially restored image. In addition, the additive noise estimated from partially restored image and the local constraints are used to determine a parameter for controlling the degree of local smoothness on the solution. The resulting iterative algorithm exhibits increased convergence speed when compared to the non-adaptive algorithm. In addition, a smooth solution with a controlled degree of smoothness is obtained without a prior knowledge about the noise. Experimental results demonstrate that the proposed algorithm requires the similar iteration number to converge, but there is the improvement of SNR more than 0.2 dB comparing to the previous approach.

• Proceedings of the KIEE Conference
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• 1979.08a
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• pp.164-165
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• 1979

Two dimensional kernels which reconstruct the tomographic image from the blurred one formed by simple back-projection are investigated and their performances are compared. These kernels are derived from tile point spread function of the tomographic system and have the form of a ramp filter modified by several window functions to suppress ringing in the reconstruction. Computer simulation using a computer generated phantom image data with different correction functions(kernels) has been carried out. In this simulation, filtering in frequency domain by 2-D FFT technique or in space domain by 2-D direct convolution is considered. It is found that the-computation time required for real space convolution technique is much larger than that of Fourier 2-D filtering technique in the pratical situation.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.35 no.2
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• pp.83-92
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• 2019

This study set out to analyze James Turrell's Skyspace works by applying the object relations theory, compare them with several modern architectural works with a sky window based on the analysis results, and check common and differentiating features between them. The findings were as follows: first, Turrell's Skyspace works shed new light on the images of the sky by projecting inner light to the outside; second, sky windows in architecture make the self-representation of inner space clear through the introjection of light; third, 'Hill of the Buddha' reflects the most general aspect of the object relations theory as the nature of projection found in Turrell's Skyspace is added to it; and finally, Turrell installed event-like spaces and steps in 'Roden Crater' to apply the introjection of others that was deficient in Skyspace.

• Journal of Broadcast Engineering
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• v.19 no.5
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• pp.578-589
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• 2014

Window image is displayed through a monitor screen when we execute the application programs on the computer. This includes webpage, video player and a number of applications. The webpage delivers a variety of information by various types in comparison with other application. Unlike a natural image captured from a camera, the window image like a webpage includes diverse components such as text, logo, icon, subimage and so on. Each component delivers various types of information to users. However, the components with different characteristic need to be divided locally, because text and image are served by various type. In this paper, we divide window images into many sub blocks, and classify each divided region into background, text and subimage. The detected subimages can be applied into 2D-to-3D conversion, image retrieval, image browsing and so forth. There are many subimage classification methods. In this paper, we utilize AdaBoost for verifying that the machine learning-based algorithm can be efficient for subimage detection. In the experiment, we showed that the subimage detection ratio is 93.4 % and false alarm is 13 %.

• Journal of Korean Society of Transportation
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• v.17 no.1
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• pp.105-112
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• 1999

Image Processing Technique has been used as an efficient method to collect traffic information on the road such as vehicle counts, speed, queues, congestion and incidents. Most of the current methods which have been used to detect vehicles by the image processing are based on point processing, dealing with the local gray level of each pixel in the small window. However, these methods have some drawbacks. Firstly, detection is restricted by image quality. Secondly, they can not deal with occlusion and perspective projection problems, In this research, a new method which possibly deals with occlusion and perspective problems will be proposed. It extracts spatial information such as the position, the relationship of vehicles in 3-dimensional space, as well as vehicle detection in the image. The main algorithm used in this research is based on an extension of the Hough Transform. The Hough Transform which is proposed to estimates parameters of vertices and directed edges analytically on the Hough Space, is a valuable method for the 3-dimensional analysis of static scenes, motion detection and the estimation of viewing parameters.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.10
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• pp.162-170
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• 2013

In recent years, the interests and needs of the Pedestrian Protection System (PPS), which is mounted on the vehicle for the purpose of traffic safety improvement is increasing. In this paper, we propose a pedestrian candidate window extraction and unit cell histogram based HOG descriptor calculation methods. At pedestrian detection candidate windows extraction stage, the bright ratio of pedestrian and its circumference region, vertical edge projection, edge factor, and PCA reconstruction image are used. Dalal's HOG requires pixel based histogram calculation by Gaussian weights and trilinear interpolation on overlapping blocks, But our method performs Gaussian down-weight and computes histogram on a per-cell basis, and then the histogram is combined with the adjacent cell, so our method can be calculated faster than Dalal's method. Our PCA reconstruction error based pedestrian detection candidate window extraction method efficiently classifies background based on the difference between pedestrian's head and shoulder area. The proposed method improves detection speed compared to the conventional HOG just using image without any prior information from camera calibration or depth map obtained from stereo cameras.