• KIPS Transactions on Software and Data Engineering
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• v.5 no.1
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• pp.13-20
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• 2016

In this paper, we propose an object recognition system that can effectively find out its category, its instance name, and several attributes from the color and depth images of an object with hierarchical feature learning. In the preprocessing stage, our system transforms the depth images of the object into the surface normal vectors, which can represent the shape information of the object more precisely. In the feature learning stage, it extracts a set of patch features and image features from a pair of the color image and the surface normal vector through two-layered learning. And then the system trains a set of independent classification models with a set of labeled feature vectors and the SVM learning algorithm. Through experiments with UW RGB-D Object Dataset, we verify the performance of the proposed object recognition system.

• The Journal of the Korea Contents Association
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• v.6 no.7
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• pp.90-98
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• 2006

Digital watermarking is a technique embedding hidden information into multimedia data imperceptibly such as images and sounds. Generally an original image is transformed and coded watermark data is embedded in frequency domain watermarking models. In this paper, We propose a new color image watermarking technique using Fresnel transform. A watermark image is Fresnel - transformed and the intensity of transformed pattern is embedded into color image. In our watermarking model, an original image is converted from RGB components into YCrCb components and then the values of real number and imaginary number of a Fresnel-transformed pattern of a watermark image are embedded into Y component. The watermarking experiments were conducted to show the validity of the proposed method using PSNR value, and the results show that our method has the robustness against lossy compression like JPEG.

• Journal of Service Research and Studies
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• v.5 no.2
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• pp.103-110
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• 2015

Recently, Classification of characters in a 3D FPS game has emerged as a very significant issue. In this study, We propose the game character Classification method using Image Region Segmentation of the extracting meaningful object in a simple operation. In this method, first used a non-linear RGB color model and octree color quantization scheme. The input image represented a less than 20 quantized color and uses a small number of meaningful color histogram. And then, the image divided into small blocks, calculate the degree of similarity between the color histogram intersection and adjacent block in block units. Because, except for the block boundary according to the texture and to extract only the boundaries of the object block. Set a region by these boundary blocks as a game object and can be used for FPS game play. Through experiment, we obtain accuracy of more than 80% for Classification method using each feature. Thus, using this property, characters could be classified effectively and it draws the game more speed and strategic actions as a result.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.7
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• pp.972-980
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• 2022

The butterfly species recognition technology based on machine learning using images has the effect of reducing a lot of time and cost of those involved in the related field to understand the diversity, number, and habitat distribution of butterfly species. In order to improve the accuracy and time efficiency of butterfly species classification, various features used as the inputs of machine learning models have been studied. Among them, branch length similarity(BLS) entropy or color intensity entropy methods using the concept of entropy showed higher accuracy and shorter learning time than other features such as Fourier transform or wavelet. This paper proposes a feature extraction algorithm using RGB color intensity entropy for butterfly color images. In addition, we develop butterfly recognition systems that combines the proposed feature extraction method with representative ensemble models and evaluate their performance.

• 전자공학회논문지 IE
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• v.45 no.4
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• pp.60-67
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• 2008

Skin color is a very important information for an automatic face recognition. In this paper, we proposed a skin region extraction method using color information and skin color model. We use the adaptive lighting compensation technique for improved performance of skin region extraction. Also, using an preprocessing filter, normally large areas of easily distinct non skin pixels, are eliminated from further processing. And we use the modified ST color space, where undesired effects are reduced and the skin color distribution fits better than others color space. Experimental results show that the proposed method has better performance than the conventional methods, and reduces processing time by $35{\sim}40%$ on average.

• Food Science and Preservation
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• v.20 no.6
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• pp.760-765
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• 2013

The image analysis as a tool for evaluation of browning degree on fresh-cut lotus root was studied. The fresh-cut lotus root treated as 4 groups (Cont-without any treatment, DB-blanching at $50^{\circ}C$ for 5 min in distilled water, AB-blanching at $45^{\circ}C$ for 5 min in 1% ascorbic acid, CB-blanching at $45^{\circ}C$ for 5 min in 1% citric acid). The samples treated with each methods were packaged with 0.04 mm polyethylene bag ($25cm{\times}30cm$) and stored at $4^{\circ}C$ for 9 days. On the RGB color space, the AB and CB group showed high R, G, B value. On the HSV and CIE $L^*a^*b^*$ color space, the AB and CB group showed low browning area, $a^*$, $b^*$ value and high $L^*$ value. Polyphenol oxidase activity was low in the AB and CB groups in all storage period. This result means that the AB and CB groups were inhibited the development of tissue browning. The result of sensory evaluation also supported this opinion. And the correlation coefficient between sensory evaluation with all color values was over 0.84. Especially, the $L^*$ value showed the highest correlation coefficient (0.93). In conclusion, the image analysis is suitable for analysis of browning degree on fresh-cut lotus root by analyzing diverse color value.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2004.11a
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• pp.279-286
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• 2004

본 논문에서는 출입국자 관리의 효율성과 체계적인 출입국 관리를 위하여 위조 여권을 판별할 수 있는 지능형 여권 얼굴 인증 시스템을 제안한다. 제안하는 지능형 여권 얼굴 인증 시스템은 여권 이미지에서 여권 코드 문자열을 인식하여 여권 사용자의 사진 및 관련 정보를 여권 데이터베이스에서 추출한다. 추출된 출입국자의 사진 및 얼굴과 여권에 부착된 사진 및 얼굴과의 유사도 측정을 통하여 여권 사진의 위조 여부을 판단한다. 이때, 이미지의 유사도 측정을 위해서 다양한 실험을 통한 결과를 종합 분석해 본 결과 사진 영역의 인증에는 Luminance, Edge, RGB 특징이, 얼굴 영역의 인증을 위해서는 Hue, YIQ-I, YCbCr-Cb 특징이 효과적인 것으로 나타났으며 사진 영역의 유사도와 얼굴영역의 유사도가 모두 0.8이상인 경우 정상적인 여권으로 판정하고 그렇지 않은 경우 위조가 되었을 가능성이 있는 여권으로 판정하는 방법을 사용하여 FAR 3.1%, FRR 2.7%의 우수한 결과를 나타내었다.

• Journal of Advanced Navigation Technology
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• v.15 no.4
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• pp.672-679
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• 2011

In this paper, detection algorithm of face region based on skin color of in the TV images is proposed. In the first, reference image is set to the sampled skin color, and then the extracted of face region is candidated using the Euclidean distance between the pixels of TV image. The eye image is detected by using the mean value and standard deviation of the component forming color difference between Y and C through the conversion of RGB color into CMY color model. Detecting the lips image is calculated by utilizing Q component through the conversion of RGB color model into YIQ color space. The detection of the face region is extracted using basis of knowledge by doing logical calculation of the eye image and lips image. To testify the proposed method, some experiments are performed using front color image down loaded from TV color image. Experimental results showed that face region can be detected in both case of the irrespective location & size of the human face.

• Proceedings of the Korea Information Processing Society Conference
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• 2006.11a
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• pp.261-264
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• 2006

본 논문은 조명의 변화가 심한 연속영상에서 동적객체를 안정적으로 추출하기 위하여 색상강도 및 기울기 기반 배경모델을 구축하고 이를 이용하여 입력영상으로부터 동적 객체의 윤곽선을 안정적으로 추출하는 기법을 제시한다. 제안기법에서는 우선, 동적객체가 포함되지 않은 배경 연속영상의 HSI 컬러공간에서 색상(Hue) 강도와 색상 기울기에 대한 배경모델을 생성한다. 실시간으로 입력되는 동적 객체를 포함한 연속영상에 대하여 각 화소에 대한 색상(Hue)성분을 추출하고 이웃 화소와의 색상성분에 대한 기울기 크기를 계산한다. 이를 기구축된 배경모델과 비교하여 그 차분값이 일정 임계값을 초과하는 경우 동적객체의 윤곽선으로 판별한다. 제안 기법은 극심한 조명 변화에 강건하게 동적 객체의 윤곽정보를 실시간 추출하였다. 본 논문에서는 기존 RGB 기반 배경 모델링 기법을 적용한 경우와의 비교 실험을 통하여 제안 기법의 안정성을 보였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2011.01a
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• pp.173-174
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• 2011

본 논문에서는 마우스 제어를 위한 손 인식 시스템을 제안한다. 이를 위하여 배경영상과 입력영상의 차영상을 이용하여 움직임 영역을 구하고, RGB 컬러모델을 HSV 컬러모델로 변환하여 피부색상과 유사한 영역을 얻는다. 이 둘 사이의 교집합을 통하여 손 후보 영역을 추출하고 모폴로지 연산을 통해 잡음을 제거한 후 손 영상을 추출한다. 추출한 손 영상을 모폴로지 연산을 이용하여 손바닥 영역과 손가락 영역으로 분리한 다음 손바닥 영역의 위치정보를 마우스의 좌표로, 손가락의 개수를 마우스 이벤트로 정의하여 마우스를 제어한다. 실험 결과는 제안된 시스템이 마우스 제어에 효과적으로 사용될 수 있음을 보이고 있다.