• The KIPS Transactions:PartB
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• v.8B no.6
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• pp.705-717
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• 2001

In this paper, we propose a rapid lane detection method to extract a preceding vehicle from sequential images captured by a single monocular CCD camera. We detect positions of lanes for an individual image within the limited area that would not be hidden and thereby compute the slopes of the detected lanes. Then we find a search area where vehicles would exist and extract the position of the preceding vehicle within the area with edge component by applying a structured method. To verify the effects of the proposed method, we capture the road images with a notebook PC and a CCD camera for PC and present the results such as processing time for lane detection, accuracy and vehicles detection against the images.

• Proceedings of the Korea Information Processing Society Conference
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• 2002.04a
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• pp.647-650
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• 2002

The human faces do not have distinct features unlike other general objects. In general the features of eyes, nose and mouth which are first recognized when human being see the face are defined. These features have different characteristics depending on different human face. In this paper, We propose a face recognition algorithm using the hidden Markov model(HMM). In the preprocessing stage, we find edges of a face using the locally adaptive threshold scheme and extract features based on generic knowledge of a face, then construct a database with extracted features. In training stage, we generate HMM parameters for each person by using the forward-backward algorithm. In the recognition stage, we apply probability values calculated by the HMM to input data. Then the input face is recognized by the euclidean distance of face feature vector and the cross-correlation between the input image and the database image. Computer simulation shows that the proposed HMM algorithm gives higher recognition rate compared with conventional face recognition algorithms.

• Journal of IKEEE
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• v.19 no.2
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• pp.160-167
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• 2015

We present an experimental implementation of the inexpensive microwave security sensor that can detect both static and slowly moving objects in cluttered environment. The prototype consists of a frequency-modulated continuous wave radar sensor, control board or computer and software. The prototype was tested in a cluttered indoor environment. In case of intrusion or change of environment the sensor will give an alarm, determine the location of new object, change in its location and can detect a slowly moving target. To make a low-cost unit we use commercially available automotive radar and own signal processing techniques for object detection and tracking. The intruder detection is based on a comparison between current 'image' in memory and 'no-intrusion' reference image. The main challenge is to develop a reliable technique for detection of a relatively low-magnitude object signals hidden in multipath clutter echo signals. Various experimental measurements and computations have shown the feasibility and performance of the system.

• Journal of the Institute of Convergence Signal Processing
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• v.17 no.2
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• pp.67-70
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• 2016

This paper discusses a graphic user interface(GUI) for the concealed target segmentation. The human subject hiding a metal gun is captured by the passive millimeter wave(MMW) imaging system. The imaging system operates on the regime of 8 mm wavelength. The MMW image is analyzed by the multi-level segmentation to segment and identify a concealed weapon under clothing. The histogram of the passive MMW image is modeled with the Gaussian mixture distribution. LBG vector quantization(VQ) and expectation and maximization(EM) algorithms are sequentially applied to segment the body and the object area. In the experiment, the GUI is implemented by the MFC(Microsoft Foundation Class) and the OpenCV(Computer Vision) libraries and tested in real-time showing the efficiency of the system.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.5
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• pp.755-765
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• 2015

This paper is concerned with Interval Type-2 Radial Basis Function Neural Network classifier realized with the aid of Census Transform(CT) and (2D)2LDA methods. CT is considered to improve performance of face recognition in a variety of illumination variations. (2D)2LDA is applied to transform high dimensional image into low-dimensional image which is used as input data to the proposed pattern classifier. Receptive fields in hidden layer are formed as interval type-2 membership function. We use the coefficients of linear polynomial function as the connection weights of the proposed networks, and the coefficients and their ensuing spreads are learned through Conjugate Gradient Method(CGM). Moreover, the parameters such as fuzzification coefficient and the number of input variables are optimized by Artificial Bee Colony(ABC). In order to evaluate the performance of the proposed classifier, Yale B dataset which consists of images obtained under diverse state of illumination environment is applied. We show that the results of the proposed model have much more superb performance and robust characteristic than those reported in the previous studies.

• Journal of Electrical Engineering and Technology
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• v.11 no.4
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• pp.974-986
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• 2016

Reversible image watermarking, a type of digital data hiding, is capable of recovering the original image and extracting the hidden message with precision. A number of reversible algorithms have been proposed to achieve a high embedding capacity and a low distortion. While numerous algorithms for the achievement of a favorable performance regarding a small embedding capacity exist, the main goal of this paper is the achievement of a more favorable performance regarding a larger embedding capacity and a lower distortion. This paper therefore proposes a reversible data hiding algorithm for which a novel piecewise 2D auto-regression (P2AR) predictor that is based on a rhombus-embedding scheme is used. In addition, a minimum description length (MDL) approach is applied to remove the outlier pixels from a training set so that the effect of a multiple linear regression can be maximized. The experiment results demonstrate that the performance of the proposed method is superior to those of previous methods.

• Journal of the Korea Society of Computer and Information
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• v.14 no.7
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• pp.41-47
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• 2009

A solution to the problems of recognizing as one spot or detection failures for complex regions, in which many spots representing proteins are overlapped and saturated, is suggested. The accumulated gradients of each point in complex regions are calculated, and the resulting accumulated gradient image segmented using watershed technique. The suggested solution show better and efficient result than existing method for spot separation, detects more protein spots hidden in the image of 2-dimensional electrophoresis, and expands the scope of prediction.

• Journal of the Korean Society of Clothing and Textiles
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• v.31 no.6 s.165
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• pp.911-922
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• 2007

In this study, for the purpose of correct viewing on the image of Africa and understanding of modem fashion, African image and art, the general characteristics of African costume, the background of fashion subjecting African image, and the trend according to ages were examined based on theoretical background. Then the researcher drew African image by analyzing the works in four 2005 S/S major fashion collections to designers and design factors. The ten voted designers' and brands' works in 2005 S/S collections had four concepts of African image; 'Wild Erotic', 'Abstract Primitive', 'Natural Elegant' and 'Sporty Romantic'. The viewpoint of modem fashion on African image from the aspect of design, designer and fashion trend can be examined as below. First, African costume, which was religious and ceremonial, appeared to emphasize its esthetic side with decorative details in modem fashion design and designers competed to choose a method to harmonize tradition and modem style and by adopting these from occult to decorative meaning, Second, fashion designers presented city unpolished beauty of modem women to a special style and made african image to be recognized as a code of fashion culture by integrating it with modem people's mind to go back to the past and admiration for the purity of nature. Third, thanks to the instinctive vitality hidden in the primitive life, inspiration for creative design that is found in the esthetic mind of the Indians, foreign taste emphasizing ethnic trend, and admiration to naturalism due to the increase of concern over ecology, 'African image' led the beginning of 21C trend by being settled as a in fashion trend.

• Journal of the Korean Society of Costume
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• v.60 no.7
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• pp.14-30
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• 2010

The purpose of this study is to come up with a perspective that can enhance the understanding about the mechanism of fashion change. The starting idea of this study is that fashion has changed according to the ideal body image of the specific society or individual. The theoretical framework about the ideal body image has been studied by the literatures on the subject and it is verified by the analysis of John Galliano & Martin Margiela's fashion works. The results are as follows. First, Galliano exposes the woman's body as a sexual symbol which is articulated by men's eyes. Margiela describes the woman's body as human being which doesn't highlight any sexual characteristics. Second, Galliano emphasizes the body conscious silhouette whereas Margiela perceives the body as a whole, rather than looks into each body part. Third, Galliano uses lots of decoration to make display luxurious. Margiela restrains himself from using decorations and tries to create images by interaction between the clothes and bodies. Forth, Galliano expresses the eroticism by accentuating eyes and lips with strong color cosmetics. Margiela's fashion is not dazzling with makeup. He even covers the face with fabrics. Aesthetics in all societies is articulated by their hidden social power groups, and then it has influence on taking shape of the ideal body image and the mainstream of fashion. But the innate characters of individuals offer challenges to the fashion majority. The tension between the social power and individual character makes and changes the fashion.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.5
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• pp.697-704
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• 2018

Since the face in image content corresponds to individual information that can distinguish a specific person from other people, it is important to accurately detect faces not hidden in an image. In this paper, we propose a method to accurately detect a face from input images using a deep learning algorithm, which is one of the machine learning methods. In the proposed method, image input via the red-green-blue (RGB) color model is first changed to the luminance-chroma: blue-chroma: red-chroma ($YC_bC_r$) color model; then, other regions are removed using the learned skin color model, and only the skin regions are segmented. A CNN model-based deep learning algorithm is then applied to robustly detect only the face region from the input image. Experimental results show that the proposed method more efficiently segments facial regions from input images. The proposed face area-detection method is expected to be useful in practical applications related to multimedia and shape recognition.