• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.55.2-55
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• 2001

The statistical analysis of the feature extraction and the neural networks are proposed to recognize a human face. In the preprocessing step, the normalized skin color map with Gaussian functions is employed to extract the region efface candidate. The feature information in the region of face candidate is used to detect a face region. In the recognition step, as a tested, the 360 images of 30 persons are trained by the backpropagation algorithm. The images of each person are obtained from the various direction, pose, and facial expression, Input variables of the neural networks are the feature information that comes from the eigenface spaces. The simulation results of 30 persons show that the proposed method yields high recognition rates.

• International Journal of Computer Science & Network Security
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• v.21 no.12spc
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• pp.648-656
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• 2021

Color digital images are used in many multimedia applications and in many vital applications. Some of these applications require excellent protection for these images because they are confidential or may contain confidential data. In this paper, a new method of data cryptography is introduced, tested, and implemented. It will be shown how this method will increase the security level and the throughput of the data cryptography process. The proposed method will use a secret image_key to generate necessary private keys for each byte of the data block. The proposed method will be compared with other standard methods of data cryptography to show how it will meet the requirements of excellent cryptography, by achieving the objectives: Confidentiality, Integrity, Non-repudiation, and Authentication.

• Journal of IKEEE
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• v.22 no.4
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• pp.1188-1194
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• 2018

This paper proposes an image enhancement-based license plate detection algorithm to improve the overall performance of system. Non-uniform illumination conditions have huge impact on overall plate detection system accuracy. In this paper, we propose an algorithm for color image enhancement-based license plate detection for improving accuracy of images degraded by excessively strong and low sunlight. Firstly, the image is enhanced by Multi-Scale Retinex algorithm. Secondly, a plate detection method is employed to take advantage of geometric properties of connected components, which can significantly reduce the undesired plate regions. Finally, intersection over union method is applied for detecting the accurate location of number plate. Experimental results show that the proposed method significantly improves the accuracy of plate detection system.

• Journal of Information Processing Systems
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• v.17 no.6
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• pp.1138-1156
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• 2021

Emotional semantics are the highest level of semantics that can be extracted from an image. Constructing a system that can automatically recognize the emotional semantics from images will be significant for marketing, smart healthcare, and deep human-computer interaction. To understand the direction of image emotion recognition as well as the general research methods, we summarize the current development trends and shed light on potential future research. The primary contributions of this paper are as follows. We investigate the color, texture, shape and contour features used for emotional semantics extraction. We establish two models that map images into emotional space and introduce in detail the various processes in the image emotional semantic recognition framework. We also discuss important datasets and useful applications in the field such as garment image and image retrieval. We conclude with a brief discussion about future research trends.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.156-165
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• 2021

In this paper, we propose an histogram weighted centroid based Siamese region proposal network for object tracking. The original Siamese region proposal network uses two identical artificial neural networks which take two different images as the inputs and decide whether the same object exist in both input images based on a similarity measure. However, as the Siamese network is pre-trained offline, it experiences many difficulties in the adaptation to various online environments. Therefore, in this paper we propose to incorporate the histogram weighted centroid feature into the Siamese network method to enhance the accuracy of the object tracking. The proposed method uses both the histogram information and the weighted centroid location of the top 10 color regions to decide which of the proposed region should become the next predicted object region.

• The Bulletin of The Korean Astronomical Society
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• v.45 no.1
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• pp.62.2-62.2
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• 2020

Globular clusters(GCs) are found not only around galaxies (galaxy GCs), but also between galaxies in galaxy clusters (intracluster GCs; ICGCs). The ICGCs, which are not bound to any of cluster member galaxies, are governed by the galaxy clutster potential. ICGCs have been detected in the wide field of Virgo and Fornax galaxy clusters. However, previous surveys covered only a small fraction of Coma and Perseus. In this study we present a wide field survey of these two galaxy clusters, using Subaru Hyper Suprime-Cam(HSC) archival images, covering a circular field with diameter of ~1.8 deg. We select ICGC candidates, by masking the images of bright galaxies and choosing point sources in the remaining area. We find thousands of ICGCs in each galaxy cluster. These ICGCs show a bimodal color distribution, which is dominated by blue GCs. We investigate spatial distributions and radial number density profiles of the blue and red ICGCs in each galaxy cluster. Implications of the results will be discussed.

• Korean Journal of Remote Sensing
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• v.35 no.6_4
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• pp.1319-1326
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• 2019

The KARI(Korea Aerospace Research Institute) operates the government satellite information application consultation to cope with ever-increasing demand for satellite images in the public sector, and carries out various support projects including the generation and provision of mosaic images on the Korean Peninsula every year to enhance user convenience and promote the use of satellite images. In particular, the government has wanted to increase the utilization of mosaic images on the Korean Peninsula and seek to classify and update mosaic images so that users can use them in their businesses easily. However, it is necessary to test and verify whether the classification results of the mosaic images can be utilized in the field since the original spectral information is distorted during pan-sharpening and color balancing, and there is a limitation that only R, G, and B bands are provided. Therefore, in this study, the reliability of the classification result of the mosaic image was compared to the result of KOMPSAT-3 image. The study found that the accuracy of the classification result of KOMPSAT-3 image was between 81~86% (overall accuracy is about 85%), while the accuracy of the classification result of mosaic image was between 69~72% (overall accuracy is about 72%). This phenomenon is interpreted not only because of the distortion of the original spectral information through pan-sharpening and mosaic processes, but also because NDVI and NDWI information were extracted from KOMPSAT-3 image rather than from the mosaic image, as only three color bands(R, G, B) were provided. Although it is deemed inadequate to distribute classification results extracted from mosaic images at present, it is believed that it will be necessary to explore ways to minimize the distortion of spectral information when making mosaic images and to develop classification techniques suitable for mosaic images as well as the provision of NIR band information. In addition, it is expected that the utilization of images with limited spectral information could be increased in the future if related research continues, such as the comparative analysis of classification results by geomorphological characteristics and the development of machine learning methods for image classification by objects of interest.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.4
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• pp.461-468
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• 2003

In a sequence of images obtained by surveillance cameras, facial regions appear very small and their colors change abruptly by lighting condition. This paper proposes a new face detection scheme, robust on complex background, small size, and lighting conditions. The proposed method is consisted of three processes. In the first step, the candidates for the face regions are selected using face color distribution and motion information. In the second stage, the non-face regions are removed using face color ratio, boundary ratio, and average of column-wise intensity variation in the candidates. The face regions containing eyes and mouth are segmented and classified, and then they are scored using their topological relations in the last step. To speed up and improve a performance the above process, a block based image segmentation technique is used. The experiments have shown that the proposed algorithm detects faced regions with more than 91% of accuracy and less than 4.3% of false alarm rate.

• Journal of the Korean Society of Fashion and Beauty
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• v.1 no.1 s.1
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• pp.95-104
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• 2003

Just as costumes reflect the spirit of the time, hair styles echo the social changes and even facilitate them, being used as a means of communication. In short, hair styles reflect the cultural life of the time dynamically. In our modern times, fashion is moving very fast, and such a phenomenon is more conspicuous in hair styles. While individuals are eager to pursue their own individuality, hair styles play a leading role in fashion, excelling the costumes. In this sense, we need to note that hair styles may be related with individual, social and psychological factors. As people are more interested in hair colors, the scope of hair color selection becomes wider. People visit beauty shops to have their hair colors changes rather than have their hairs cut. Selection of a hair color seems to be deeply related with individuals' psychological states. Since hair colors have much effects on their facial images, hair designers need to have an empathy with their customers. Each person has his or her own unique image, and his/her selection of hair colors is affected much by external environment as well as his/her traits. With such basic assumptions in mind, this study was aimed at analyzing the preferences of hair colors by those in their 20's, 30's and 40's who are more interested in their hair colors. To this end, their preferences of or tendencies for hair colors were surveyed by sex, age group and job.

• Journal of Information Processing Systems
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• v.9 no.1
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• pp.141-156
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• 2013

In this paper we propose a method to detect human faces in color images. Many existing systems use a window-based classifier that scans the entire image for the presence of the human face and such systems suffers from scale variation, pose variation, illumination changes, etc. Here, we propose a lighting insensitive face detection method based upon the edge and skin tone information of the input color image. First, image enhancement is performed, especially if the image is acquired from an unconstrained illumination condition. Next, skin segmentation in YCbCr and RGB space is conducted. The result of skin segmentation is refined using the skin tone percentage index method. The edges of the input image are combined with the skin tone image to separate all non-face regions from candidate faces. Candidate verification using primitive shape features of the face is applied to decide which of the candidate regions corresponds to a face. The advantage of the proposed method is that it can detect faces that are of different sizes, in different poses, and that are making different expressions under unconstrained illumination conditions.