• Journal of the Korea Fashion and Costume Design Association
• /
• v.6 no.2
• /
• pp.111-121
• /
• 2004

The Mevelana sect is a spiritual Islamic group experiencing spiritual transaction with their god through a peculiar dancing as a form of religious ritual. The Mevelana, a sect of Islamic Sufism, has their head mosque in Konya in Turkey. Although Mevelana sect is regarded as heretic of Islam, it has exerted considerable influence on the Islamic religion through its peculiar religious worshiping form constituted in dancing and reciting poems. Nowadays, Turkey recognizes the Mevelana dancing as their precious cultural legacy of a long history, exerting public information efforts to give it for wider publicity of Turkey to the world. The Mevelana dress with ornament attired for the ritual dancing performance is regarded to symbolize a spiritual medium, which leads to conciliation with the eternity. The straight lines and curved line characteristic of the Mevelana dancer's trousers, skirt, jacket and such mirrors the image of the Orientals, which is in peculiar contrast with the white and gray colors of the dress with ornament. The impression of the spiritual Mevelana dressing in harmony with the dynamic dancing motion goes beyond mere a religious dressing. It is expressive of a graceful and sophisticated modern formative art, of which the mystic design gives an inspiration to the modern fashion. After Poiret, Islamic factors have emerged in the modern fashion. For instance, a hat with Arabic fashion lapel, a Fez hat of Turkish style, harem pants and such are still popular in the modern fashion. It seems probable that the Iraq War would far more activate the influence of Arabic culture to the modern fashion. By making an analysis on the religious background and formative characteristics of the Mevelana dressing, and by giving design examples on how the Mevelanan dressing has been applied to the modern fashion, this research suggests working out new designs by making a renewed application of their characteristics to the modern fashion.

• Journal of the Korean Society of Clothing and Textiles
• /
• v.34 no.8
• /
• pp.1253-1264
• /
• 2010

This research is an in-depth study on the differences of cosmetics advertising and brand representation between Korean companies and Chinese companies. In addition, it studies the preferences of cosmetics consumption in Korea and China. To study these topics, two major methods are applied to magazine advertising analysis and consumer research. Analysis objects are the magazine advertisements of the Korean brands Mamond and Laneige, which entered the China market more than 5 years ago; the 64 advertisements are evenly split between Koreans and Chinese. The objects of the survey are 470 females between the ages 20 and 30 (237 from Korea and 233 from China). The results were as follows. First, Chinese advertisements use intense appeal in which the types of advertisement appeal are highly preferred. Second, ordinary models are highly preferred. Second, (on the nationality of the models) Chinese and Korean models are preferred in comparison to western models. Third, (as shown in the survey) Koreans and Chinese preferred magazine advertisements with headlines and copies. Four, blue colors are commonly used in the advertisements; however, the survey shows that the Chinese consumers prefer gray colors. Furthermore, from this study, there is a significant dynamic between the brand image and consumer satisfaction as well as the re-purchase intention.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.19 no.12
• /
• pp.2785-2791
• /
• 2015

In this paper, neddle detection algorithm for the removal of needle stuck into skin in oriental clinic is presented. First, in the proposed method, potential candidate areas of each needle are selected by using the morphological open operation in a gray image, and the false candidates are removed by considering their area size. Next, edge points are extracted using canny edge detector in selected candidate areas, line segments are approximated using the edge points. Based on the direction of line segment and the distance between two line segments, two main line segments of the needle are extracted. The final verification of needle is accomplished by using the morphological analysis of these two line segments. In the experiments, the detection rate of proposed method reaches to 97.5% for the 16 images containing 119 needles.

• Journal of Information Processing Systems
• /
• v.9 no.1
• /
• pp.117-140
• /
• 2013

Hindi is the most widely spoken language in India, with more than 300 million speakers. As there is no separation between the characters of texts written in Hindi as there is in English, the Optical Character Recognition (OCR) systems developed for the Hindi language carry a very poor recognition rate. In this paper we propose an OCR for printed Hindi text in Devanagari script, using Artificial Neural Network (ANN), which improves its efficiency. One of the major reasons for the poor recognition rate is error in character segmentation. The presence of touching characters in the scanned documents further complicates the segmentation process, creating a major problem when designing an effective character segmentation technique. Preprocessing, character segmentation, feature extraction, and finally, classification and recognition are the major steps which are followed by a general OCR. The preprocessing tasks considered in the paper are conversion of gray scaled images to binary images, image rectification, and segmentation of the document's textual contents into paragraphs, lines, words, and then at the level of basic symbols. The basic symbols, obtained as the fundamental unit from the segmentation process, are recognized by the neural classifier. In this work, three feature extraction techniques-: histogram of projection based on mean distance, histogram of projection based on pixel value, and vertical zero crossing, have been used to improve the rate of recognition. These feature extraction techniques are powerful enough to extract features of even distorted characters/symbols. For development of the neural classifier, a back-propagation neural network with two hidden layers is used. The classifier is trained and tested for printed Hindi texts. A performance of approximately 90% correct recognition rate is achieved.

• Journal of KIISE:Software and Applications
• /
• v.30 no.7_8
• /
• pp.773-779
• /
• 2003

Locating exact position of facial components is a key preprocessing for realizing highly accurate and reliable face recognition schemes. In this paper, we propose a simple but powerful method for detecting isolated facial components such as eyebrows, eyes, and a mouth, which are horizontally oriented and have relatively dark gray levels. The method is based on the shape-resolving locally optimum thresholding that may guarantee isolated detection of each component. We show that pure facial regions can be determined by grouping facial features satisfying simple geometric constraints on unique facial structure. In the test for over 1000 images in the AR -face database, pure facial regions were detected correctly for each face image without wearing glasses. Very few errors occurred in the face images wearing glasses with a thick frame because of the occluded eyebrow -pairs. The proposed scheme may be best suited for the later stage of classification using either the mappings or a template matching, because of its capability of handling rotational and translational variations.

• KIPS Transactions on Software and Data Engineering
• /
• v.4 no.4
• /
• pp.201-208
• /
• 2015

This paper proposes a method for detecting the direction indicator shown in the road surface efficiently from the black box system installed on the vehicle. In the proposed method, the direction indicators are detected by inverse perspective mapping(IPM) and bag of visual features(BOF)-based NN classifier. In order to apply the proposed method to real-time environments, the candidated regions of direction indicator in an image only performs IPM, and BOF-based NN is used for the classification of feature information from direction indicators. The results of applying the proposed method to the road surface direction indicators detection and recognition, the detection accuracy was presented at least about 89%, and the method presents a relatively high detection rate in the various road conditions. Thus it can be seen that the proposed method is applied to safe driving support systems available.

• Journal of the Korea Society of Computer and Information
• /
• v.19 no.4
• /
• pp.55-62
• /
• 2014

This paper proposes a fault detection method utilizing converted images of acoustic emission signals and texture analysis for identifying bearing's multi-faults which frequently occur in an induction motor. The proposed method analyzes three texture features from the converted images of multi-faults: multi-faults image's entropy, homogeneity, and energy. These extracted features are then used as inputs of a fuzzy-ARTMAP to identify each multi-fault including outer-inner, inner-roller, and outer-roller. The experimental results using ten times trials indicate that the proposed method achieves 100% accuracy in the fault classification.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.4
• /
• pp.2124-2148
• /
• 2017

Accurate facial expression recognition (FER) requires reliable signal filtering and the effective feature extraction. Considering these requirements, this paper presents a novel approach for FER which is robust to noise. The main contributions of this work are: First, to preserve texture details in facial expression images and remove image noise, we improved the anisotropic diffusion filter by adjusting the diffusion coefficient according to two factors, namely, the gray value difference between the object and the background and the gradient magnitude of object. The improved filter can effectively distinguish facial muscle deformation and facial noise in face images. Second, to further improve robustness, we propose a new feature descriptor based on a combination of the Histogram of Oriented Gradients with the Canny operator (Canny-HOG) which can represent the precise deformation of eyes, eyebrows and lips for FER. Third, Canny-HOG's block and cell sizes are adjusted to reduce feature dimensionality and make the classifier less prone to overfitting. Our method was tested on images from the JAFFE and CK databases. Experimental results in L-O-Sam-O and L-O-Sub-O modes demonstrated the effectiveness of the proposed method. Meanwhile, the recognition rate of this method is not significantly affected in the presence of Gaussian noise and salt-and-pepper noise conditions.

• Journal of information and communication convergence engineering
• /
• v.8 no.5
• /
• pp.575-580
• /
• 2010

Eye gaze as a form of input was primarily developed for users who are unable to use usual interaction devices such as keyboard and the mouse; however, with the increasing accuracy in eye gaze detection with decreasing cost of development, it tends to be a practical interaction method for able-bodied users in soon future as well. This paper explores a low-cost, robust, rotation and illumination independent eye gaze system for gaze enhanced user interfaces. We introduce two brand-new algorithms for fast and sub-pixel precise pupil center detection and 2D Eye Gaze estimation by means of deformable template matching methodology. In this paper, we propose a new algorithm based on the deformable angular integral search algorithm based on minimum intensity value to localize eyeball (iris outer boundary) in gray scale eye region images. Basically, it finds the center of the pupil in order to use it in our second proposed algorithm which is about 2D eye gaze tracking. First, we detect the eye regions by means of Intel OpenCV AdaBoost Haar cascade classifiers and assign the approximate size of eyeball depending on the eye region size. Secondly, using DAISMI (Deformable Angular Integral Search by Minimum Intensity) algorithm, pupil center is detected. Then, by using the percentage of black pixels over eyeball circle area, we convert the image into binary (Black and white color) for being used in the next part: DTBGE (Deformable Template based 2D Gaze Estimation) algorithm. Finally, using DTBGE algorithm, initial pupil center coordinates are assigned and DTBGE creates new pupil center coordinates and estimates the final gaze directions and eyeball size. We have performed extensive experiments and achieved very encouraging results. Finally, we discuss the effectiveness of the proposed method through several experimental results.

• Investigative Magnetic Resonance Imaging
• /
• v.21 no.4
• /
• pp.210-222
• /
• 2017

Purpose: To develop a novel combination of controlled aliasing in parallel imaging results in higher acceleration (CAIPIRINHA) with integrated SSFP (CAIPI-iSSFP) for accelerated SSFP imaging without banding artifacts at 3T. Materials and Methods: CAIPI-iSSFP was developed by adding a dephasing gradient to the balanced SSFP (bSSFP) pulse sequence with a gradient area that results in $2{\pi}$ dephasing across a single pixel. Extended phase graph (EPG) simulations were performed to show the signal behaviors of iSSFP, bSSFP, and RF-spoiled gradient echo (SPGR) sequences. In vivo experiments were performed for brain and abdominal imaging at 3T with simultaneous multi-slice (SMS) acceleration factors of 2, 3 and 4 with CAIPI-iSSFP and CAIPI-bSSFP. The image quality was evaluated by measuring the relative contrast-to-noise ratio (CNR) and by qualitatively assessing banding artifact removal in the brain. Results: Banding artifacts were removed using CAIPI-iSSFP compared to CAIPI-bSSFP up to an SMS factor of 4 and 3 on brain and liver imaging, respectively. The relative CNRs between gray and white matter were on average 18% lower in CAIPI-iSSFP compared to that of CAIPI-bSSFP. Conclusion: This study demonstrated that CAIPI-iSSFP provides up to a factor of four acceleration, while minimizing the banding artifacts with up to a 20% decrease in the relative CNR.