• Journal of the Semiconductor & Display Technology
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• v.4 no.1 s.10
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• pp.61-64
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• 2005

This study presents a new algorithm to detect the facial feature in a color image entered from the mobile device with complex backgrounds and undefined distance between camera's location and the face. Since skin color model with Hough transformation spent approximately 90$\%$ of running time to extract the fitting ellipse for detection of the facial feature, we have changed the approach to the simple geometric vector operation, called a TS(Triangle-Square) transformation. As the experimental results, this gives benefit of reduced run time. We have similar ratio of face detection to other methods with fast speed enough to be used on real-time identification system in mobile environments.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.2
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• pp.69-78
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• 2002

This paper addresses a method to automatically detect out a person's face from a given image that consists of a hair and face view of the person and a complex background scene. Out method involves an effective detection algorithm that exploits the spatial distribution characteristics of human skin color via an adaptive fuzzy color classifier (AFCC), The universal skin-color map is derived on the chrominance component of human skin color in Cb, Cr and their corresponding luminance. The desired fuzzy system is applied to decide the skin color regions and those that are not. We use RGB model for extracting the hair color regions because the hair regions often show low brightness and chromaticity estimation of low brightness color is not stable. After some preprocessing, we apply convex-hull to each region. Consequent face detection is made from the relationship between a face's convex-hull and a head's convex-hull. The algorithm using the convex-hull shows better performance than the algorithm using pattern method. The performance of the proposed algorithm is shown by experiment. Experimental results show that the proposed algorithm successfully and efficiently detects the faces without constrained input conditions in color images.

• Journal of The Korea Institute of Healthcare Architecture
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• v.13 no.2
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• pp.27-36
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• 2007

Recently, hospital patients experience anxiety, confusion, and stress about wayfinding as the spacial layout and treatment circulatory system of hospitals have become complicated due to their oversized and complex structure. As part of finding a solution to the problem, this study seeks to examine what are the essential elements of the wayfinding planning of O.P.D. in general hospitals, to develop the model of wayfinding, and to suggest the methods of improving the wayfinding system. The research methods of this study adopted were literature review in wayfinding cognition, plan analysis of ten general hospitals, space analysis of these hospitals through space syntax, analysis of the system of visual-perceptual information through a field study, and analysis of surveys and follow-up surveys conducted to support the results. Based on these results, the proposals for finding decision points, providing the information, and developing a model planning are listed as follows. 1) The comprehensive understanding of O.P.D. spacial layout and the visual-perceptual information system is necessary to find the essential elements of wayfinding. 2) The decision points are found through the full understanding of spacial functions, circulation systems, and facility configuration, considering the spacial layout, the bound of the visual-perceptual information system, and the circulatory system. Furthermore, the information decision points could be confined by space syntax. 3) The checklist and color compound & color codes, developed through the planning of signage system and color system could be applied to the methods of providing the information. 4) The planning of wayfinding system according to the whole process of practices for outpatients was mentioned above. The system of visual-perceptual information developed through the process of this study should be integrated in the spacial layout of the whole O.P.D.

• Journal of the East Asian Society of Dietary Life
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• v.17 no.5
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• pp.653-660
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• 2007

The purpose of this study was to measure the causal relationships among such variables as moral concerns for animals, meat texture, meat color, satiety from meat, disgust with meat and pork consumption. A total of 250 questionnaires were completed. Structural equation models were used to measure the causal effects of the constructs. The study outcomes demonstrated that the structural analysis results of the data were an excellent model fit. The effects of moral concerns for animals, meat texture and satiety from meat on the disgust with meat were statistically significant. As expected, disgust with meat had a significant effect on pork consumption. Moreover, moral concerns for animals and satiety from meat had a significant indirect effect on pork consumption through disgust with meat. Also, satiety from meat alone had a significant indirect effect on pork consumption through disgust with meat. By developing and testing conceptual models that integrated the relationships among ideational variables, sensory affective variables, anticipated consequences variables, emotional variables, and behavioral variables, this study may approach a deeper understanding of the complex relationships among pork consumption-related variables. A greater understanding of these complex relationships can improve the managerial diagnosis of problems as well as opportunities for different marketing strategies, including pork production and pork product development, and marketing communications.

• Genomics & Informatics
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• v.20 no.4
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• pp.40.1-40.8
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• 2022

The concepts of phylogeny and floral genetics play a crucial role in understanding the origin and diversification of flowers in angiosperms. Angiosperms evolved a great diversity of ways to display their flowers for reproductive success with variations in floral color, size, shape, scent, arrangements, and flowering time. The various innovations in floral forms and the aggregation of flowers into different kinds of inflorescences have driven new ecological adaptations, speciation, and angiosperm diversification. Evolutionary developmental biology seeks to uncover the developmental and genetic basis underlying morphological diversification. Advances in the developmental genetics of floral display have provided a foundation for insights into the genetic basis of floral and inflorescence evolution. A number of regulatory genes controlling floral and inflorescence development have been identified in model plants such as Arabidopsis thaliana and Antirrhinum majus using forward genetics, and conserved functions of many of these genes across diverse non-model species have been revealed by reverse genetics. Transcription factors are vital elements in systems that play crucial roles in linked gene expression in the evolution and development of flowers. Therefore, we review the sex-linked genes, mostly transcription factors, associated with the complex and dynamic event of floral development and briefly discuss the sex-linked genes that have been characterized through next-generation sequencing.

• Smart Media Journal
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• v.2 no.2
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• pp.14-19
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• 2013

Recently, due to the increase of the use of scanned images, the text segmentation techniques, which play critical role to optimize the quality of the scanned images, are required to be updated and advanced. In this study, an algorithm has been developed based on the modification of Gaussian mixture model (GMM) by integrating the calculation of Gaussian detection gradient and the estimation of the number clusters. The experimental results show an efficient method for text segmentation in natural scenes such as storefronts, street signs, scanned journals and newspapers at different size, shape or color of texts in condition of lighting changes and complex background. These indicate that our model algorithm and research approach can address various issues, which are still limitations of other senior algorithms and methods.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2088-2091
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• 2003

We presents a new algorithm for tracking person in video sequence that integrates the meanshift iteration procedure into the particle filtering. Utilizing the nice property of convergence to the modes in the meanshift iteration we show that only a few sample points are sufficient, while in general the particle filtering requires a large number of sample points. Multi-parts of a person is tracked independently of each other based on the color Then, the similarity against the reference model color and the geometric constraints between multi-parts are reflected as the sample weights. Also presented is the computer simulation results, which show successful tracking even for complex background clutter.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.11
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• pp.4534-4555
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• 2015

We propose a robust object cataloging method using multiple locally distinct heterogeneous features for aiding image retrieval. Due to challenges such as variations in object size, orientation, illumination etc. object recognition is extraordinarily challenging problem. In these circumstances, we adapt local interest point detection method which locates prototypical local components in object imageries. In each local component, we exploit heterogeneous features such as gradient-weighted orientation histogram, sum of wavelet responses, histograms using different color spaces etc. and combine these features together to describe each component divergently. A global signature is formed by adapting the concept of bag of feature model which counts frequencies of its local components with respect to words in a dictionary. The proposed method demonstrates its excellence in classifying objects in various complex backgrounds. Our proposed local feature shows classification accuracy of 98% while SURF,SIFT, BRISK and FREAK get 81%, 88%, 84% and 87% respectively.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.251-252
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• 2007

The thesis proposed the Text Extraction Algorithm which is a text extraction algorithm which uses the Coiflet Wavelet, YCbCr Color model and the close curve edge feature of adaptive LoG Operator in order to complement the demerit of the existing research which is weak in complexity of background, variety of light and disordered line and similarity of text and background color. This thesis is simulated with natural images which include naturally text area regardless of size, resolution and slant and so on of image. And the proposed algorithm is confirmed to an excellent by compared with an existing extraction algorithm in same image.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.1
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• pp.47-55
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• 2024

We spend a lot of time in indoor space, and the space has a huge impact on our lives. Interior design plays a significant role to make an indoor space attractive and functional. However, it should consider a lot of complex elements such as color, pattern, and material etc. With the increasing demand for interior design, there is a growing need for technologies that analyze these design elements accurately and efficiently. To address this need, this study suggests a deep learning-based design analysis system. The proposed system consists of a semantic segmentation model that classifies spatial components and an image classification model that classifies attributes such as color, pattern, and material from the segmented components. Semantic segmentation model was trained using a dataset of 30000 personal indoor interior images collected for research, and during inference, the model separate the input image pixel into 34 categories. And experiments were conducted with various backbones in order to obtain the optimal performance of the deep learning model for the collected interior dataset. Finally, the model achieved good performance of 89.05% and 0.5768 in terms of accuracy and mean intersection over union (mIoU). In classification part convolutional neural network (CNN) model which has recorded high performance in other image recognition tasks was used. To improve the performance of the classification model we suggests an approach that how to handle data that has data imbalance and vulnerable to light intensity. Using our methods, we achieve satisfactory results in classifying interior design component attributes. In this paper, we propose indoor space design analysis system that automatically analyzes and classifies the attributes of indoor images using a deep learning-based model. This analysis system, used as a core module in the A.I interior recommendation service, can help users pursuing self-interior design to complete their designs more easily and efficiently.