• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.11C
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• pp.1040-1048
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• 2009

This paper proposes an auto flesh tone balance algorithm for the picture that is taken for people. General white balance algorithms bring neutral region into focus. But, other objects can be basis if its spectral reflectance is known. In this paper the basis for white balance is human face. For experiment, first, transfer characteristic of image sensor is analyzed and camera output RGB on average face chromaticity under standard illumination is calculated. Second, Output rate for the image is adjusted to make RGB rate for the face photo area taken under unknown illumination RGB rate that is already calculated. Input tri-stimulus XYZ can be calculated from camera output RGB by camera transfer matrix. And input tri-stimulus XYZ is transformed to standard color space (sRGB) using sRGB transfer matrix. For display, RGB data is encoded as eight-bit data after gamma correction. Algorithm is applied to average face color that is light skin color of Macbeth color chart and average color of various face colors that are actually measured.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.11
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• pp.457-464
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• 2021

The advent of the big data era has enabled the rapid development of deep learning that learns rules by itself from data. In particular, the performance of CNN algorithms has reached the level of self-adjusting the source data itself. However, the existing image processing method only deals with the image data itself, and does not sufficiently consider the heterogeneous environment in which the image is generated. Images generated in a heterogeneous environment may have the same information, but their features may be expressed differently depending on the photographing environment. This means that not only the different environmental information of each image but also the same information are represented by different features, which may degrade the performance of the image analysis model. Therefore, in this paper, we propose a method to improve the performance of the image color constancy model based on Adversarial Learning that uses image data generated in a heterogeneous environment simultaneously. Specifically, the proposed methodology operates with the interaction of the 'Domain Discriminator' that predicts the environment in which the image was taken and the 'Illumination Estimator' that predicts the lighting value. As a result of conducting an experiment on 7,022 images taken in heterogeneous environments to evaluate the performance of the proposed methodology, the proposed methodology showed superior performance in terms of Angular Error compared to the existing methods.

• Korean Journal of Cognitive Science
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• v.25 no.1
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• pp.51-71
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• 2014

This paper presents an image enhancement method in real road traffic scenes. The images captured by the camera on the car cannot keep the color constancy as illumination or weather changes. In the real environment, these problems are more worse at back light conditions and at night that make more difficult to the applications of the vision-based intelligent vehicles. Using the existing image enhancement methods without considering the position and intensity of the light source and their geometric relations the image quality can even be deteriorated. Thus, this paper presents a fast and effective method for image enhancement resembling human cognitive system which consists of 1) image preprocessing, 2) color-contrast evaluation, 3) alpha blending of over/under estimated image and preprocessed image. An input image is first preprocessed by gamma correction, and then enhanced by an Automatic Color Enhancement(ACE) method. Finally, the preprocessed image and the ACE image are blended to improve image visibility. The proposed method shows drastically enhanced results visually, and improves the performance in traffic sign detection of the vision based intelligent vehicle applications.

• Journal of the Korea Society of Computer and Information
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• v.10 no.3 s.35
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• pp.143-150
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• 2005

Camera image is very sensitive to illumination that result in difficulties for recognizing character. Also Camera captured document images have not only skew but also vignetting effect and geometric distortion. Vignetting effect make it difficult to separate characters from the document images. Geometric distortion, occurred by the mismatch of angle and center position between the document image and the camera, make the shape of characters to be distorted, so that the character recognition is more difficult than the case of using scanner. In this paper, we propose a method that can increase the performance of character recognition by correcting the geometric distortion of document images using a linear approximation which changes the quadrilateral region to the rectangle one. The proposed method also determine the quadrilateral transform region automatically, using the alignment of character lines and the skewed angles of characters located in the edges of each character line. Proposed method, therefore, can correct the geometric distortion without getting positional information from camera.

• Journal of Korean Academy of Oral and Maxillofacial Radiology
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• v.27 no.2
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• pp.83-90
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• 1997

This study was performed to demonstrate the effect of linear or nonlinear contrast enhancement on subtraction images. Three different textures were radiograped on dental film. The first radiograph was taken without the presence of an object. the second, which showed trabucular bone, was taken of the molar area of a human. the third radiograph was taken of the coronal part of molars. Each film was digitized into a 1312 x 1024 pixel x 8 bit depth matrix by means of a Nikon 35 mm film scanner(LS-3510AF, Japan) with fixed gain and internal dark current correction to maintain constant illumination. The scanner was interfaced to a Macintosh Le ill computer(Apple Computer, Charlotte, N.C) This resulted in three pairs of images, including different textures-plain, bone and enamel. Digital regular, linearly and nonlinearly enhanced subtraction was performed. Computer software was ,used to simulate lesions in the shape of a 2D-Gaussian curve on each of a pair of images. The each subtraction images were presented in a random sequence to two groups of 10 observers(students and dentists). ROC analysis was used to compare observer performance. The following results were obtained ; 1. All of LCE subtraction, equalized subtraction and regular subtraction images of plain texture were diagnosed the best by far. 2. The data revealed a siginificant LCE effect in both the student group and the expert group. 3. Clinical expertise was a helphul factor for the observers in this study.

• Journal of Advanced Navigation Technology
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• v.24 no.2
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• pp.148-154
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• 2020

Wrinkles are one of the main features of skin aging. Conventional image processing-based wrinkle detection is difficult to effectively cope with various skin images. In particular, Wrinkle extraction performance is significantly decreased when the wrinkles are not strong and similar to the surrounding skin. In this paper, deep learning is applied to extract wrinkles from microscopic skin images. In general, the microscope image is equipped with a wide-angle lens, so the brightness at the boundary area of the image is dark. In this paper, to solve this problem, the brightness of the skin image is estimated and corrected. In addition, We apply the structure of semantic segmentation network suitable for wrinkle extraction. The proposed method obtained an accuracy of 99.6% in test experiments on skin images collected in our laboratory.

• Journal of Korean Academy of Oral and Maxillofacial Radiology
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• v.27 no.1
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• pp.17-24
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• 1997

The purpose of the present study was to use digital profile image features and digital image analysis of fixed-dimension bone regions, extracted from standardized periapical radiographs of the maxilla, to determine whether differences exist in alveolar bone of younger women(mean age: 59.23±7.34 years) and just menopaused women(mean age: 59.23±7.34). Periapical films were used from two groups of 20 randomly selected women. None of the subjects had a remarkable medical history. To simplify protocol, we chose one interproximal bone area between the maxillary right canine and lateral incisor for study. Ech film was digitized into a 1312 x 1024 pixel x 8 bit depth matrix by means of a Nikon 35 mm film scanner(LS-35lOAF, Japan) with fixed gain and internal dark current correction to maintain constant illumination. The scanner was interfaced to a Macintosh LC III computer(Apple Computer, Charlotte, N.C.). Area and profile orientation were selected with a NIMH Image 1.37(NIH Research Services Branch, Bethesda, Md.). Histogram features were extracted from each profile and area. The results of this study indicate that mean pixel intensities didn't differ significantly between two groups and there was a high correlarion-coefficient between digitized radiographic profile features and area features.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.156-158
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• 2007

The radiance observed from the ocean depends on the illumination and viewing geometry along with the water properties, and this variation is called the bidirectional effect which is important to be considered in ocean color remote sensing. In the present study, as a preliminary step, the spectral-angular radiances in coastal water were investigated with experiments for a range of viewing geometric conditions $(0-70^{\circ})$. Over a phytoplankton-dominated water surface the upward radiance for visible and near-infrared wavelengths (example, SeaWiFS and GOCI) increased at nadir and decreased toward the near-horizon, becoming dependent of viewing angles (with higher radiance at nadir view angle and lower radiance at near-horizon viewing angle). This variations were better expressed by the Q-factor, which relates upwelling radiance to the upwelling irradiance (i.e., $Q=E_u/L_u$, also dependent on Sun's position). The Q-factor for this case was more non-uniform with the considered wavelengths and was dependent on viewing geometric conditions. These experimental results confirm the previous similar findings in other coastal waters.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11b
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• pp.913-915
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• 2005

임의의 영상에서 얼굴을 검출하는 것은 얼굴을 인식하는데 있어서 선행되어야 할 필수과정이다. 본 논문은 조명의 변화가 심한 컬러영상에서 얼굴을 검출하는 것을 목적으로 한다. 본 논문은 기존의 기하학적 밝기분포 마스크만을 사용한 방법이 조명 변화에 취약한 단점을 보완하는데 중점을 두었다. 히스토그램 평활화(Histogram Equalization : HE)와 감마 크기 보정 (Gamma Intensity Correction : GIC) 방법을 이용해서 조명에 대한 간섭을 줄인 후, 영상 전체에서 피부 영역을 추출하고 이어서 눈 후보들을 검출한다. 검출된 눈 후보들로부터 기하학적 밝기분포 마스크를 적용하여 효과적으로 얼굴 후보들을 찾을 수 있고, 이렇게 찾아진 얼굴 후보들은 주성분분석법(Principal Component Analysis : PCA)를 이용해서 얼굴인지 여부를 판별하게 된다. 본 알고리즘은 조명 밝기 등으로 인해 검출률이 떨어졌던 단점을 보완할 수 있었고, 향후 얼굴 검출 분야에 있어서도 활용 가치가 있을 것으로 생각된다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.12
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• pp.2398-2405
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• 2007

This paper proposes a contrast enhancement technique which stretches the intensity surfaces of image to improve the quality of the digital photos. The proposed method enhances the contrast of image by stretching the intensity surface of the original image to the maximum range of the output image in proportion to the distances between the original intensity surface and upper, lower intensity surface, respectively. The upper and lower intensity surfaces are generated from the original intensity surface by gaussian smoothing. In the experiments, digital color images in a variety of illumination conditions were used and the proposed method was compared with other several existed image enhancement algorithms, which are histogram stretching, surface stretching, histogram equalization, gamma correction and retinex. It was proved that the experimental results were more natural visually without deterioration of gradation.