• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.6
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• pp.81-89
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• 2004

In this paper, we compare face recognition rate by PCA algorithm using distance change and embedded data being input left side and right side image in stereo images. The proposed method detects face region from RGB color space to YCbCr color space. Also, The extracted face image's scale up/down according to distance change and extracts more robust face region. The proposed method through an experiment could establish standard distance (100cm) in distance about 30∼200cm, and get 99.05% (100cm) as an average recognition result by scale change. The definition of super state is specification region in normalized size (92${\times}$112), and the embedded data extracts the inner factor of defined super state, achieved face recognition through PCA algorithm. The orignal images can receive specification data in limited image's size (92${\times}$112) because embedded data to do learning not that do all learning, in image of 92${\times}$112 size averagely 99.05%, shows face recognition rate of test 1 99.05%, test 2 98.93%, test 3 98.54%, test 4 97.85%. Therefore, the proposed method through an experiment showed that if apply distance change rate could get high recognition rate, and the processing speed improved as well as reduce face information.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.19 no.5
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• pp.9-16
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• 2005

The purpose of this research is to acquire emotional data on luminous source by measuring and evaluating human emotional response to the change of the optical feature of luminous environment Luminous sources used in actual architectural space were selected with the optical feature of luminous soured then to measure and analysis human emotional response on Luminous Source. As a result of that 1) In the result of performance measurement by the item of the clear vision of an optic function the fluorescent lamp of daylight indicated the most excellent Performance. 2) In the item of fatigue and stress, the metal halide lamp and mercury lamp showed the most 3) In $\ulcorner$ suitable in light$\lrcorner$, $\ulcorner$a similar with daylight$\lrcorner$ adjective of the amenity item the fluorescent lamp of daylight which color temperature was high turned up to be high also, in $\ulcorner$brilliant$\lrcorner$, adjective, the metal halide lamp and mercury lamp turned up to be low. 4) In the result of factor analysis, three factors $\ulcorner$activity$\lrcorner$, $\ulcorner$potency$\lrcorner$, $\ulcorner$evaluation$\lrcorner$ were abstracted and $\ulcorner$activity$\lrcorner$ factor has the most influential on evaluating the mood of interior space. 5) For the affection in the mood evaluation by each luminous sources, $\ulcorner$activity$\lrcorner$ factor was the most influential by metal halide lamp and fluorescent lamp of daylight, $\ulcorner$potency$\lrcorner$ factor was most influential by kind of incandescent lamp, $\ulcorner$evaluation$\lrcorner$ factor was most influential by fluorescent lamp of low color temperature.

• Journal of the Korea Society of Computer and Information
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• v.18 no.6
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• pp.21-28
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• 2013

This paper proposes an effective fire detection approach that includes the following multiple heterogeneous algorithms: moving region detection using grey level histograms, color segmentation using fuzzy c-means clustering (FCM), feature extraction using a grey level co-occurrence matrix (GLCM), and fire classification using support vector machine (SVM). The proposed approach determines the optimal threshold values based on grey level histograms in order to detect moving regions, and then performs color segmentation in the CIE LAB color space by applying the FCM. These steps help to specify candidate regions of fire. We then extract features of fire using the GLCM and these features are used as inputs of SVM to classify fire or non-fire. We evaluate the proposed approach by comparing it with two state-of-the-art fire detection algorithms in terms of the fire detection rate (or percentages of true positive, PTP) and the false fire detection rate (or percentages of true negative, PTN). Experimental results indicated that the proposed approach outperformed conventional fire detection algorithms by yielding 97.94% for PTP and 4.63% for PTN, respectively.

• Cartoon and Animation Studies
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• s.32
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• pp.1-21
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• 2013

Silhouette Animation has been recognized as a genre of animation since the very beginning of the animation history, and also Its segmental movement and Aesthetic expression has led a variety of semantic interpretations. Especially the French animation director, Michel Ocelot, recently integrated 3-dimensional digital to the silhouettes animation, and it extended the possibility of the silhouettes animation in many aspects. In his latest animation feature, , he showed how he made changes in 3-dimension by creating and evolving his own way and style of silhouette animation. Although mainstream digital animations preferably to show realistic images and motion, Michel Ocelot used very selective movement, subjective digital colors and extended space which couldn't be expressed in the way of creating traditional style of silhouette animation. This alternative slow movement and the unique aesthetics in 3-dimension emphasize the unconscious elements of color, composition, patterns, and it provides digitally enhanced images and pictorial impression. In addition, the acquisition of digital three-dimensional use of space made possible to provides the wider formative imagination to the audience. In this paper, we analyzed aesthetic characteristics of the digital silhouette animation, (2011), specially focusing on the aspects of Movement, Image, Space, which could not be found in the traditional silhouette animation. It is significant to obtain diversity of the future digital animation and its positive development. In addition, this provides opportunity to explore Michel Ocelot's new experiments and animation philosophy.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.5
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• pp.1011-1019
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• 2015

CAPTCHA(Completely Automated Public Turing test to tell Computers and Humans Apart) is a test used in computing to distinguish whether or not the user is computer or human. Many web sites mostly use the character-based CAPTCHA consisting of digits and characters. Recently, with the development of OCR technology, simple character-based CAPTCHA are broken quite easily. As an alternative, many web sites add noise to make it harder for recognition. In this paper, we analyzed the most recent CAPTCHA, which incorporates the addition of the natural images to obfuscate the characters. We proposed an efficient method using support vector machine to separate the characters from the background image and use convolutional neural network to recognize each characters. As a result, 368 out of 1000 CAPTCHAs were correctly identified, it was demonstrated that the current CAPTCHA is not safe.

• Archives of design research
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• v.19 no.5 s.67
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• pp.151-160
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• 2006

When it comes to the paintings in Joseon Dynasty, landscape and a genre paintings were popular but it was Minhwa that people of every dass loved. Minhwa contained not only true meaning of a picture, but also it was simple and straighforwardly depicted. Among them, paintings in books belonging to the Joseon Dynasty (hereinafter called Checkgeori paintings) describe a small universe in which people live harmoniously in nature on the basis of a Confucian society. In addition, it shows modern philosophy in paintings and makes a feature of human life. In this paper, 'Ssangpok Checkgeori 'in Checkgeori paintings illustrates the relationships between things such as color, arrangement, place and direction. They have outstanding features such as the direction of things in composition, multiple vanishing points, a folding screen constituent and movement of viewpoints. Therefore, we come to know the painting method of 'Ssangpok Checkgeori' can be applied to modern paintings.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.11
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• pp.1029-1035
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• 2013

This paper proposes a real time lane detection algorithm using LRF (Laser Range Finder) for autonomous navigation of a mobile robot. There are many technologies for safety of the vehicles such as airbags, ABS, EPS etc. The real time lane detection is a fundamental requirement for an automobile system that utilizes outside information of automobiles. Representative methods of lane recognition are vision-based and LRF-based systems. By the vision-based system, recognition of environment for three dimensional space becomes excellent only in good conditions for capturing images. However there are so many unexpected barriers such as bad illumination, occlusions, and vibrations that the vision cannot be used for satisfying the fundamental requirement. In this paper, we introduce a three dimensional lane detection algorithm using LRF, which is very robust against the illumination. For the three dimensional lane detections, the laser reflection difference between the asphalt and lane according to the color and distance has been utilized with the extraction of feature points. Also a stable tracking algorithm is introduced empirically in this research. The performance of the proposed algorithm of lane detection and tracking has been verified through the real experiments.

• Journal of the Korean Association of Geographic Information Studies
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• v.10 no.4
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• pp.1-13
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• 2007

We make image that accumulate two principal component after change picture to use GLCM(Gray Level Co-Occurrence Matrix)'s texture feature information. And then these images use preprocess to achieved corner detection and area detection. Experiment results, two principle component conversion accumulation images had most informations about six kind textures by Eigen value 94.6%. When compared with red tide area that uses sea color and red tide area of image that have all principle component, displayed the most superior result. Also, we creates Euclidian space using Euclidian distance measurement about red tide area and clear sea. We identify of red tide area by red tide area and clear sea about random sea area through Euclidian distance and spatial distribution.

• International Journal of Knowledge Content Development & Technology
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• v.7 no.1
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• pp.57-78
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• 2017

There are several statistical classification algorithms available for land use/land cover classification. However, each has a certain bias or compromise. Some methods like the parallel piped approach in supervised classification, cannot classify continuous regions within a feature. On the other hand, while unsupervised classification method takes maximum advantage of spectral variability in an image, the maximally separable clusters in spectral space may not do much for our perception of important classes in a given study area. In this research, the output of an ANN algorithm was compared with the Possibilistic c-Means an improvement of the fuzzy c-Means on both moderate resolutions Landsat8 and a high resolution Formosat 2 images. The Formosat 2 image comes with an 8m spectral resolution on the multispectral data. This multispectral image data was resampled to 10m in order to maintain a uniform ratio of 1:3 against Landsat 8 image. Six classes were chosen for analysis including: Dense forest, eucalyptus, water, grassland, wheat and riverine sand. Using a standard false color composite (FCC), the six features reflected differently in the infrared region with wheat producing the brightest pixel values. Signature collection per class was therefore easily obtained for all classifications. The output of both ANN and FCM, were analyzed separately for accuracy and an error matrix generated to assess the quality and accuracy of the classification algorithms. When you compare the results of the two methods on a per-class-basis, ANN had a crisper output compared to PCM which yielded clusters with pixels especially on the moderate resolution Landsat 8 imagery.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.12
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• pp.2547-2554
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• 2009

In this paper, we proposed a face detection algorithm and a hardware implementation method for ROI(Region Of Interest) of AF(Auto Focusing). We used face features in skin regions of YCbCr color space for face detection. The face features are the number of skin pixels in face regions, edge pixels in eye regions, and shadow pixels in lip regions. The each feature was statistically selected by 2,000 sample pictures of face. The proposed algorithm detects two faces that are closer center of the image for considering the effectiveness of hardware resource. The detected faces are displayed by rectangle for ROI of AF, and the rectangles are represented by positions in the image about starting point and ending point of the rectangles. The proposed face detection method was verified by using FPGA boards and mobile phone camera sensor.