• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.1763-1766
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• 2003

This paper proposes a non-contact color inspection system for plasma display panel (PDP). The red, green, and blue test pattern images are acquired by using the area color CCD camera at the various distance from the PDP. The RGB values are obtained from the region of interest (ROI) which are extracted by applying the image processing to the test pattern image. Finally, the CIE xy and u'v' chromaticity coordinates of the test pattern images according to the distance are acquired from the RGB color coordinates.

• Korean Institute of Interior Design Journal
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• v.15 no.4 s.57
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• pp.123-128
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• 2006

The purpose of the research is to estimate the amount of a color image reaction variation by changing areas in order to design the method to reduce an error about the color sample when it is applied in the real situation. The summary of the results acquired in this research is as followed. (1) With fixed observing distance of 1m, we observed that the value and chroma of each color object became higher as sight angle was increased as $2^{\circ},\;10^{\circ},\;and\;30^{\circ}$, even though the variation ratio was different. (2) With fixed sight angle of 10, we observed that the value and chroma of each color object becane higher as observing distance was changed from 1m to 3.3m, even though the variation ratio was different. (3) With same area, we observed that the values and chromas of each color object in the conditions of $1m-30^{\circ}\;and\;3.3m-10^{\circ}$ were almost same. (4) When the area became larger, the subjects tended to feel that colors were bright and clear with the increase of tone. In all the colors, the variation of a color reaction in chroma is higher than those in value. In future, we can observe the limit in applying to colors in the architecture by identifying the tendency of the color change according to the area change qualitatively.

• Journal of Korea Multimedia Society
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• v.11 no.6
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• pp.764-776
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• 2008

Image identification is the process of checking whether the query image is the transformed version of the specific original image or not. In this paper, image identification method based on feature composition is proposed. Used features include color distance, texture information and average pixel intensity. We extract color characteristics using color distance and texture information by Modified Generalized Symmetry Transform as well as average intensity of each pixel as features. Individual feature is quantized adaptively to be used as bins of histogram. The histogram is normalized according to data type and it is used as the signature in comparing the query image with database images. In matching part, Manhattan distance is used for measuring distance between two signatures. To evaluate the performance of the proposed method, independent test and accuracy test are achieved. In independent test, 60,433 images are used to evaluate the ability of discrimination between different images. And 4,002 original images and its 29 transformed versions are used in accuracy test, which evaluate the ability that the proposed algorithm can find the original image correctly when some transforms was applied in original image. Experiment results show that the proposed identification method has good performance in accuracy test. And the proposed method is very useful in real environment because of its high accuracy and fast matching capacity.

• ETRI Journal
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• v.38 no.1
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• pp.164-173
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• 2016

Demosaicing, or color filter array (CFA) interpolation, estimates missing color channels of raw mosaiced images from a CFA to reproduce full-color images. It is an essential process for single-sensor digital cameras with CFAs. In this paper, a new demosaicing method for digital cameras with Bayer-like W-RGB CFAs is proposed. To preserve the edge structure when reproducing full-color images, we propose an edge direction-adaptive method using color difference estimation between different channels, which can be applied to practical digital camera use. To evaluate the performance of the proposed method in terms of CPSNR, FSIM, and S-CIELAB color distance measures, we perform simulations on sets of mosaiced images captured by an actual prototype digital camera with a Bayer-like W-RGB CFA. The simulation results show that the proposed method demosaics better than a conventional one by approximately +22.4% CPSNR, +0.9% FSIM, and +36.7% S-CIELAB distance.

• 한국해양학회지
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• v.3 no.2
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• pp.55-62
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• 1968

The distributions of average water color and the transparency in the seas around Korea show two patterns: the one is the East Sea and the South Ses, the other is the Yellow Sea. In the East Sea and the South Sea, the water colors C$\sub$E/ in Forel scales change from green to greenish blue with distance x in miles from the seashore, an average color is bluish green, 3.7 in Forel scales, and the relationship is given by C$\sub$E/ = 5e$\^$-0.056.root.x; an average transparency is 15m and the transparency T$\sub$E/ shows following formula with distance x, E$\sub$E/=0.9.root.x+10. In the Yellow Sea, the water color C$\sub$Y/ changes from green yellow to bluish green with distance, an average color is light green, 5.6 in Forel scales, and the relationship is given by C$\sub$Y/= 8.5e$\^$-0.086.root.x; an average transparency is 7m, the farther it is from the seashore, the deeper transparency T$\sub$Y/ is as following, T$\sub$Y/=1.2 .root.x+1. Along the seashore, the transparency T$\sub$Y/ is only 10% that of the East Sea and the South Sea. The distributions of the water color and the transparency by depth change in values within the continental shelf. The water color in Forel scales decreases with the distance from the seashore and depth; the transparency increases with the distance and depth. They are caused by suspended particles, especially suspended clay, and it is the major factor in the change in color and transparency, particularly in the Yellow Sea. In September, the sea water is the clearest in the seas around Korea, transparency shows the maximum and water color the minimum in forel scales. The water color shows green yellow when transparency is 1m, green at 10m, and greenish blue at 20m. the relationship between the water color and the transparency shows an exponential distribution as following, C=9e$\^$-kT/, k=0.0625m$\^$-1/. This formula agrees with calculated formulas between the water color and the transparency from the emprircal formulas C$\sub$E/ and T$\sub$E/, C$\sub$Y/ and T$\sub$Y.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.540-543
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• 1999

We propose a face tracking algorithm using skin-color based segmentation and a robust Hausdorff distance. First, we present L＊a＊b＊ color model and face segmentation algorithm. A face is segmented from the first frame of input video sequences using skin-color map. Then, we obtain an initial face model with Laplacian operator. For tracking, a robust Hausdorff distance is computed and the best possible displacement t. is selected. Finally, the previous face model is updated using the displacement t. It is robust to some noises and outliers. We provide an example to illustrate the proposed tracking algorithm in video sequences obtained from CCD camera.

• Journal of the Institute of Convergence Signal Processing
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• v.7 no.1
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• pp.11-16
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• 2006

To manipulate large video databases, effective video indexing and retrieval are required. A large number of video indexing and retrieval algorithms have been presented for frame-w]so user query or video content query whereas a relatively few video sequence matching algorithms have been proposed for video sequence query. In this paper, we propose an efficient algorithm to extract key frames using color histograms and to match the video sequences using edge features. To effectively match video sequences with low computational load, we make use of the key frames extracted by the cumulative measure and the distance between key frames, and compare two sets of key frames using the modified Hausdorff distance. Experimental results with several real sequences show that the proposed video retrieval algorithm using color and edge features yields the higher accuracy and performance than conventional methods such as histogram difference, Euclidean metric, Battachaya distance, and directed divergence methods.

• Journal of Internet Computing and Services
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• v.13 no.4
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• pp.53-62
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• 2012

Object segmentation is a demanding research area and not a trivial problem of image processing and computer vision. Tremendous segmentation algorithms were addressed on gray-scale (or biomedical) images that rely on numerous image features as well as their strategies. These works in practice cannot apply to natural color images because of their negative effects to color values due to the use of gray-scale gradient information. In this paper, we proposed a new approach for color object segmentation by modifying a geometric active contour model named distance regularized level set evolution (DRLSE). Its speed function will be designed to exploit as much as possible color gradient information of images. Finally, we provide experiments to show performance of our method with respect to its accuracy and time efficiency using various color images.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.695-698
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• 2009

Three-dimensional (3D) shape recognition and distance recognition methods utilizing monocular camera systems have been required for field of virtual-reality, computer graphics, measurement technology and robot technology. There have been many studies regarding 3D shape and distance recognition based on geometric and optical information, and it is now possible to accurately measure the geometric information of an object at short range distances. However, these methods cannot currently be applied to long range objects. In the field of virtual-reality, all visual objects must be presented at widely varying ranges, even though some objects will be hazed over. In order to achieve distance recognition from a landscape image, we focused on the use of aerial perspective to simulate a type of depth perception and investigated the relationship between distance and color perception. The applicability of our proposed method was demonstrated in experimental results.

• The KIPS Transactions:PartD
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• v.18D no.5
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• pp.323-328
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• 2011

For content-based image retrieval, the earth mover's distance and the optimal color composition distance are proposed to measure the dissimilarity. Although providing good retrieval results, both methods are too time-consuming to be used in a large image database. To solve the problem, we propose a new distance function that calculates an approximate earth mover's distance in linear time. To calculate the dissimilarity in linear time, the proposed approach employs the space-filling curve. We have performed extensive experiments to show the effectiveness and efficiency of the proposed approach. The results reveal that our approach achieves almost the same results with the EMD in linear time.