• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.304-306
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• 2007

Methods of Object recognition from camera image are to compare features of color. edge or pattern with model in a general way. SIFT(scale-invariant feature transform) has good performance but that has high complexity of computation. Using simple color histogram has low complexity. but low performance. In this paper we represent a model as a color cooccurrence histogram. and we improve performance using pyramid matching. The color cooccurrence histogram keeps track of the number of pairs of certain colored pixels that occur at certain separation distances in image space. The color cooccurrence histogram adds geometric information to the normal color histogram. We suggest object recognition by pyramid matching of color cooccurrence histogram.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.5
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• pp.160-167
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• 2014

This paper presents a spliced color image detection method using Benford' Law and color difference. For a suspicious image, after color conversion, the discrete wavelet transform and the discrete cosine transform are performed. We extract the difference between the ideal Benford distribution and the empirical Benford distribution of the suspicious image as features. The difference between Benford distributions for each color component were also used as features. Our method shows superior splicing detection performance using only 13 features. After training the extracted feature vector using SVM classifier, we determine whether the presence of the image splicing forgery. Experimental results show that the proposed method outperforms the existing methods with smaller number of features in terms of splicing detection accuracy.

• The Korean Journal of Malacology
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• v.25 no.3
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• pp.203-210
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• 2009

Optical properties and color analysis of nacreous layers were performed using various pearl shells. The cross section and microstructure of the surface in each nacreous layers were observed through a SEM (Scanning Electron Microscope), and the diffraction pattern on SEM images was analyzed using FFT (Fast Fourier Transform). Through these analysises, it was verified that the color and optical characteristics are closely related to the structure of nacreous layers. Incident angle-dependent reflection spectrum was used to examine the phenomena of color differenceas the direction of observation. Quantified values on the color change were obtained by CIE $L^*a^*b^*$ color scale. Using this research, database for the characteristics of natural pearl shells can be established, and the precise analytic method for observation of pearl shells was suggested.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.60
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• pp.47-55
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• 2000

Two experiments were conducted to examine the relationships between the color structure and the user preference of a CNS (Car Navigation System) digital map in terms of HSE (Human Sensibility Ergonomics). In the first experiment, the user's preference of color structures were investigated from the subjects' self-designed digital maps using a CNS digital map UIMS (User Interface Management System): in the second, statistical relation models between the user's color structure satisfaction level and the color components of CIE (Commission Internationale de ι'Eclairage) of the real products were suggested. For each experiment, CIE L*u*v* and CIE LCH color space were adapted, respectively, because they have their own characteristics of perceptual uniformity which enables the color components to transform a linear function.

• International Journal of Computer Science & Network Security
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• v.21 no.12spc
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• pp.556-564
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• 2021

Numerous spatial and transform-domain-based conventional denoising algorithms struggle to keep critical and minute structural features of the image, especially at high noise levels. Although neural network approaches are effective, they are not always reliable since they demand a large quantity of training data, are computationally complicated, and take a long time to construct the model. A new framework of enhanced hybrid filtering is developed for denoising color images tainted by additive white Gaussian Noise with the goal of reducing algorithmic complexity and improving performance. In the first stage of the proposed approach, the noisy image is refined using a high-dimensional non-local means filter based on Principal Component Analysis, followed by the extraction of the method noise. The wavelet transform and SURE Shrink techniques are used to further culture this method noise. The final denoised image is created by combining the results of these two steps. Experiments were carried out on a set of standard color images corrupted by Gaussian noise with multiple standard deviations. Comparative analysis of empirical outcome indicates that the proposed method outperforms leading-edge denoising strategies in terms of consistency and performance while maintaining the visual quality. This algorithm ensures homogeneous noise reduction, which is almost independent of noise variations. The power of both the spatial and transform domains is harnessed in this multi realm consolidation technique. Rather than processing individual colors, it works directly on the multispectral image. Uses minimal resources and produces superior quality output in the optimal execution time.

• Journal of the Korean Society of Costume
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• v.59 no.2
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• pp.143-153
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• 2009

The purpose of this study is to provide color information in order to planning and merchandising in china fashion through the color of Beijing opera. In objective study, we collect total 302 Beijing opera images. The collecting method of source data is to extract digital color data by color picker. We transform RGB color data to H V/C, CIE L*a*b and analyze the attributes of color and tone, three-dimensional analysis. The results of this study are as follows : 1. The color distrbution of Beijing opera is R(26.9%)>YR(18.2%)>PB(11.6%)>Y(9.6%). Traditional preference color, red is most popular color as 26.7%:, the practice of vivid tone red is numerous. 2. The tone distribution of Beijing opera costume is P(16%)>It(13.9%)>d(11%)>5(9.6%)>4kg (8.2%)>b(7.1%:). The value o# Beijing opera costume distribute medium and medium-high and the chroma of those distributes low. 3. High chroma yellow is restrictive color as the symbol of emperor in china but medium-low chroma yellow is very frequently used. 4. Blue is often used in china costume. Especially in Beijing opera costume blue is symbol of bravery, dignity, cruel character 5. White in Beijing opera costume is much used for symbol of righteous loyalist. Black is less used than white in Beijing opera costume and black is authority color for symbol of the prime minister.

• The KIPS Transactions:PartB
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• v.17B no.3
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• pp.197-206
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• 2010

High-quality and low-price digital printing devices are nowadays abused to print or forge official documents and bills. Identifying color laser printers will be a step for media forensics. This paper presents a new method to identify color laser printers with printed color images. Since different printer companies use different manufactural systems, printed documents from different printers have little difference in visual. Analyzing this artifact, we can identify the color laser printers. First, high-frequency components of images are extracted from original images with discrete wavelet transform. After calculating the gray-level co-occurrence matrix of the components, we extract some statistical features. Then, these features are applied to train and classify the support vector machine for identifying the color laser printer. In the experiment, total 2,597 images of 7 printers (HP, Canon, Xerox DCC400, Xerox DCC450, Xerox DCC5560, Xerox DCC6540, Konica), are tested to classify the color laser printer. The results prove that the presented identification method performs well with 96.9% accuracy.

• Journal of KIISE:Databases
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• v.30 no.2
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• pp.125-133
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• 2003

In this paper we propose an efficient content-based image retrieval method using the color and wavelet based texture features. The color features are obtained from soft-color histograms of the global image and the wavelet-based texture features are obtained from the invariant moments of the high-pass sub-band through the spatial-frequency analysis of the wavelet transform. The proposed system, called a color and texture based two-step retrieval(CTBTR), is composed of two-step query operations for an efficient image retrieval. In the first-step matching operation, the color histogram features are used to filter out the dissimilar images quickly from a large image database. The second-step matching operation applies the wavelet based texture features to the retained set of images to retrieve all relevant images successfully. The experimental results show that the proposed algorithm yields more improved retrieval accuracy with computationally efficiency than the previous methods.

• The Journal of the Korea Contents Association
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• v.17 no.5
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• pp.624-632
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• 2017

Automatic butterfly identification using images is one of the interesting research fields because it helps the related researchers studying species diversity and evolutionary and development process a lot in this field. The performance of the butterfly species identification system is dependent heavily on the quality of selected features. In this paper, we propose color intensity (CI) entropy by using the distribution of color intensities in a butterfly image. We show color intensity entropy can increase the recognition rate by 10% if it is used together with previously suggested branch length similarity entropy. In addition, the performance comparison with other features such as Eigenface, 2D Fourier transform, and 2D wavelet transform is conducted against several well known machine learning methods.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.1
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• pp.47-55
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• 2000

In this paper, a vision-based roadway detection algorithm for an automated vehicle control system, based on roadway sign information on roads, is proposed. First, in order to detect roadway signs, the color scene image is enhanced under hue-invariance. Fuzzy logic is employed to simplify the enhanced color image into a binary image and the binary image is morphologically filtered. Then, an effective algorithm of locating signs based on binary rank order transform (BROT) is utilized to extract signs from the image. This algorithm performs better than those previously presented. Finally, the inner shapes of roadway signs with curving roadway direction information are recognized by neural networks. Experimental results show that the new detection algorithm is simple and robust, and performs well on real sign detection. The results also show that the neural networks used can exactly recognize the inner shapes of signs even for very noisy shapes.