• Journal of Digital Contents Society
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• v.19 no.9
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• pp.1787-1793
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• 2018

Shadow is a common phenomenon observed in natural scenes, but it has a negative influence on image analysis such as object recognition, feature detection and scene analysis. Therefore, the process of detecting and removing shadows included in digital images must be considered as a pre-processing process of image analysis. In this paper, the existing methods for acquiring 1D invariant images, one of the feature elements for detecting and removing shadows contained in a single natural image, are described, and a method for obtaining 1D invariant images based on linear regression has been proposed. The proposed method calculates the log of the band-ratio between each channel of the RGB color image, and obtains the grayscale image line by linear regression. The final 1D invariant images were obtained by projecting the log image of the band-ratio onto the estimated grayscale image line. Experimental results show that the proposed method has lower computational complexity than the existing projection method using entropy minimization, and shadow detection and removal based on 1D invariant images are performed effectively.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.05a
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• pp.251-252
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• 2012

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.10
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• pp.874-880
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• 2002

A number of techniques have been proposed for 3D display using view-difference of two eyes. These methods do not express enough reality like real world. The display images have to change according to the position of a viewer to improve reality. In this paper, we present an approach for generating intermediate image between two different view images by applying new image interpolation algorithm The interpolation algorithm is designed to cope with complex shapes. The proposed image interpolation algorithm generates rotated image about vertical axes by any angle from base images. Each base image that was obtained from CCD camera has an view-angle difference of $3^{\circ}C$, $5.5^{\circ}C$, $^{\circ}C$, $22^{\circ}C$, and $45^{\circ}C$. The proposed into mediate image generation method uses the geometric analysis of image and depth information through the block-based matching method.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.724-726
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• 2003

Currently available image fusion methods are not efficient for fusing the Landsat 7 images. Significant color distortion is one of the major problems. In this paper, using the well-known wavelet based method for data fusion between high-resolution panchromatic and low-resolution multispectral satellite images, we performed Landsat 7 image fusion. Based on the experimental results obtained from this study, we analyzed some reasons for color distortion. A new approach using the biorthogonal wavelets based method for data fusion is presented. This new method has reached an optimum fusion result - with the same spectral resolution as the multispectral image and the same spatial resolution as the panchromatic image with minimum artifacts.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.6
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• pp.2317-2337
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• 2015

Cryptography provides an effective solution to secure the communication over public networks. The communication over public networks that includes electronic commerce, business and military services, necessitates the requirement of simple and robust encryption techniques. In this paper, a novel image encryption method which employs calligraphy based hybrid scan and random number is presented. The original image is scrambled by pixel position permutation with calligraphy based diagonal and novel calligraphy based scan patterns. The cipher image is obtained by XORing the scrambled image with random numbers. The suggested method resists statistical, differential, entropy, and noise attacks which have been demonstrated with a set of standard images.

• Korean Journal of Remote Sensing
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• v.27 no.3
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• pp.379-388
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• 2011

At present, GEOBIA (Geographic Object-based Image Analysis), heir of OBIA (Object-based Image Analysis), is regarded as an important methodology by object-oriented paradigm for remote sensing, dealing with geo-objects related to image segmentation and classification in the different view point of pixel-based processing. This also helps to directly link to GIS applications. Thus, GEOBIA software is on the booming. The main theme of this study is to look into the applicability of geo-spatial processing open source to GEOBIA. However, there is no few fully featured open source for GEOBIA which needs complicated schemes and algorithms, till It was carried out to implement a preliminary system for GEOBIA running an integrated and user-oriented environment. This work was performed by using various open sources such as OTB or PostgreSQL/PostGIS. Some points are different from the widely-used proprietary GEOBIA software. In this system, geo-objects are not file-based ones, but tightly linked with GIS layers in spatial database management system. The mean shift algorithm with parameters associated with spatial similarities or homogeneities is used for image segmentation. For classification process in this work, tree-based model of hierarchical network composing parent and child nodes is implemented by attribute join in the semi-automatic mode, unlike traditional image-based classification. Of course, this integrated GEOBIA system is on the progressing stage, and further works are necessary. It is expected that this approach helps to develop and to extend new applications such as urban mapping or change detection linked to GIS data sets using GEOBIA.

• Journal of the Korean Society of Visualization
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• v.11 no.2
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• pp.18-26
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• 2013

An accurate and reliable performance evaluation technique is indispensable for the development of marine antifouling coatings. The existing standard practice is however, based on the visual observation of biofouling settlement area, which is prone to the subjective judgment of the inspector. In spite of the above mentioned importance, a systematic and objective fouling evaluation technique has not yet been introduced. In this study, a novel quantitative antifouling performance evaluation method for marine antifouling paints is devised based on the image analysis of panel immersion test results. The present image analysis method is to quantify settlement area for each fouling category by distinctive color. The fouling categories are set as unfouled, biofilm, green algae, brown algae, calcareous animal and spongy animal with specific HSL (Hue, Saturation, Lightness) color ranges. In order to assess the effectiveness of the proposed method, static immersion tests for three antifouling coatings were undertaken for two years.

• Journal of the Korean Society of Clothing and Textiles
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• v.25 no.2
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• pp.303-314
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• 2001

The purposes of this study were 1) to identify different sub-segments of tourism market in Cheju island based on fashion image of clothing souvenir and 2) to provide manufactures with useful information for developing cultural fashion goods related to Cheju island. The data were collected via a self-administered questionnaire from 192 female tourist(20-59 years old) in Cheju and analyzed by factor analysis, cluster analysis, one-way ANOVA and $\chi$$^2$-test. Six factors of fashion image were found image were found and labeled as: Sexy/feminine, wild/mannish, easy/simple, Cheju, ecology, natural image. Four factors of benefit sought were also identified: Practicality/economic-value, fashionability/preference of design, reputation and souvenir-value. Three groups were identified based on fashion image sought: Image of primitive nature(G1: 37.7%), image of nature in the city(G2: 20.1%), sexy/feminine image(G3: 42.2%). There were significant differences among sub-groups in age, fashion innovation, benefit sought and intention of buying Gal-ot. Younger females(20-30 ages) were included more in group 1 than group 2 while older females(40-50 ages) were included more in group 2. Group 1 had a higher score of fashion innovation comparing group 3. Group 1 had the highest scores on practicality/economic-value and souvenir-value. However, group 2 placed the highest importance on reputation as well as practicality/economic-value and group 3 had the lowest scores on all types of benefit. Group 1 and group 2 had intention of buying Gal-ot more than group 1. Based on the results of this study, manufactures may implement target marketing strategies on group 1 which sought the image of primitive nature and group 2 which sought the image of nature in the city.

• Journal of Information Processing Systems
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• v.1 no.1 s.1
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• pp.32-35
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• 2005

Image denoising is basic work for image processing, analysis and computer vision. This paper proposes a novel algorithm based on wavelet threshold for image denoising, which is combined with the linear CLS (Constrained Least Squares) filtering and thresholding methods in the transform domain. We demonstrated through simulations with images contaminated by white Gaussian noise that our scheme exhibits better performance in both PSNR (Peak Signal-to-Noise Ratio) and visual effect.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.1
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• pp.31-40
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• 2024

Recently, static analysis-based signature and pattern detection technologies have limitations due to the advanced IT technologies. Moreover, It is a compatibility problem of multiple architectures and an inherent problem of signature and pattern detection. Malicious codes use obfuscation and packing techniques to hide their identity, and they also avoid existing static analysis-based signature and pattern detection techniques such as code rearrangement, register modification, and branching statement addition. In this paper, We propose an LLVM IR image-based automated static analysis of malicious code technology using machine learning to solve the problems mentioned above. Whether binary is obfuscated or packed, it's decompiled into LLVM IR, which is an intermediate representation dedicated to static analysis and optimization. "Therefore, the LLVM IR code is converted into an image before being fed to the CNN-based transfer learning algorithm ResNet50v2 supported by Keras". As a result, we present a model for image-based detection of malicious code.