• Title/Summary/Keyword: Skin Detection

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A Study on the Performance of Human Hand Region Detection in Images According to Color Spaces (컬러공간에 따른 영상내 사람 손 영역의 검출 성능연구)

  • Kim, Jun-Yup;Do, Yong-Tae
    • Proceedings of the KIEE Conference
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    • 2005.10b
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    • pp.186-188
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    • 2005
  • Hand region detection in images is an important process in many computer vision applications. It is a process that usually starts at a pixel-level, and that involves a pre-process of color space transformation followed by a classification process. A color space transformation is assumed to increase separability between skin classes for hands and non-skin classes for other parts, to increase similarity among different skin tones, and to bring a robust performance under varying illumination conditions, without any sound reasonings. In this work, we examine if the color space transformation does bring those benefits to the problem of hand region detection on a dataset of images with different hand postures, backgrounds, people, and illuminations. Results indicate that best of the color space is the normalized RGB.

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Face Detection by Eye Detection with Progressive Thresholding

  • Jung, Ji-Moon;Kim, Tae-Chul;Wie, Eun-Young;Nam, Ki-Gon
    • 제어로봇시스템학회:학술대회논문집
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    • 2005.06a
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    • pp.1689-1694
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    • 2005
  • Face detection plays an important role in face recognition, video surveillance, and human computer interface. In this paper, we present a face detection system using eye detection with progressive thresholding from a digital camera. The face candidate is detected by using skin color segmentation in the YCbCr color space. The face candidates are verified by detecting the eyes that is located by iterative thresholding and correlation coefficients. Preprocessing includes histogram equalization, log transformation, and gray-scale morphology for the emphasized eyes image. The distance of the eye candidate points generated by the progressive increasing threshold value is employed to extract the facial region. The process of the face detection is repeated by using the increasing threshold value. Experimental results show that more enhanced face detection in real time.

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Facial region Extraction using Skin-color reference map and Motion Information (칼라 참조 맵과 움직임 정보를 이용한 얼굴영역 추출)

  • 이병석;이동규;이두수
    • Proceedings of the IEEK Conference
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    • 2001.09a
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    • pp.139-142
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    • 2001
  • This paper presents a highly fast and accurate facial region extraction method by using the skin-color-reference map and motion information. First, we construct the robust skin-color-reference map and eliminate the background in image by this map. Additionally, we use the motion information for accurate and fast detection of facial region in image sequences. Then we further apply region growing in the remaining areas with the aid of proposed criteria. The simulation results show the improvement in execution time and accurate detection.

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Human Face Detection from Still Image using Neural Networks and Adaptive Skin Color Model (신경망과 적응적 스킨 칼라 모델을 이용한 얼굴 영역 검출 기법)

  • 손정덕;고한석
    • Proceedings of the IEEK Conference
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    • 1999.06a
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    • pp.579-582
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    • 1999
  • In this paper, we propose a human face detection algorithm using adaptive skin color model and neural networks. To attain robustness in the changes of illumination and variability of human skin color, we perform a color segmentation of input image by thresholding adaptively in modified hue-saturation color space (TSV). In order to distinguish faces from other segmented objects, we calculate invariant moments for each face candidate and use the multilayer perceptron neural network of backpropagation algorithm. The simulation results show superior performance for a variety of poses and relatively complex backgrounds, when compared to other existing algorithm.

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2-Stage Adaptive Skin Color Model for Effective Skin Color Segmentation in a Single Image (단일 영상에서 효과적인 피부색 검출을 위한 2단계 적응적 피부색 모델)

  • Do, Jun-Hyeong;Kim, Keun-Ho;Kim, Jong-Yeol
    • 한국HCI학회:학술대회논문집
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    • 2009.02a
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    • pp.193-196
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    • 2009
  • Most of studies adopt a fixed skin color model to segment skin color region in a single image. The methods, however, result in low detection rates or high false positive error rates since the distribution of skin color is varies depending on the characteristics of input image. For the effective skin color segmentation, therefore, we need a adaptive skin color model which changes the model depending on the color distribution of input image. In this paper, we propose a novel adaptive skin color segmentation algorithm consisting of 2 stages which results in both high detection rate and low false positive error rate.

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Deep Learning-based Automatic Wrinkles Segmentation on Microscope Skin Images for Skin Diagnosis (피부진단을 위한 딥러닝 기반 피부 영상에서의 자동 주름 추출)

  • Choi, Hyeon-yeong;Ko, Jae-pil
    • 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.

Effect of 8% lidocaine spray on the sensory and pain thresholds of the skin of the face and hands evaluated by quantitative sensory testing

  • Okayasu, Ichiro;Komiyama, Osamu;Ayuse, Takao;De Laat, Antoon
    • Journal of Dental Anesthesia and Pain Medicine
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    • v.18 no.6
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    • pp.361-365
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    • 2018
  • Background: Recently, we examined the effects of 2% lidocaine gel on the tactile sensory and pain thresholds of the face, tongue and hands of symptom-free individuals using quantitative sensory testing (QST); its effect was less on the skin of the face and hands than on the tongue. Consequently, instead of 2% lidocaine gel, we examined the effect of 8% lidocaine spray on the tactile sensory and pain thresholds of the skin of the face and hands of healthy volunteers. Methods: Using Semmes-Weinstein monofilaments, QST of the skin of the cheek and palm (thenar skin) was performed in 20 healthy volunteers. In each participant, two topical sprays were applied. On one side, 0.2 mL of 8% lidocaine pump spray was applied, and on the other side, 0.2 mL of saline pump spray was applied as control. In each participant, QST was performed before and 15 min after each application. Pain intensity was measured using a numeric rating scale (NRS). Results: Both the tactile detection threshold and filament-prick pain detection threshold of the cheek and thenar skin increased significantly after lidocaine application. A significant difference between the effect of lidocaine and saline applications was found on the filament-prick pain detection threshold only. NRS of the cheek skin and thenar skin decreased after application of lidocaine, and not after application of saline. Conclusion: The significant effect of applying an 8% lidocaine spray on the sensory and pain thresholds of the skin of the face and hands can be objectively scored using QST.

Hyperspectral Image Recognition for Tumor Detection (하이퍼스펙트럴 영상 인식을 통한 종양 검출)

  • 김한열;김인택
    • Proceedings of the IEEK Conference
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    • 2003.07d
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    • pp.1545-1548
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    • 2003
  • This paper presents a method for detecting skin tumors on chicken carcasses using hyperspectral images. It utilizes both fluorescence and reflectance image information in hyperspectral images. A detection system that is built on this concept can increase detection rate and reduce processing time. Chicken carcasses are examined first using band ratio FCM information of fluorescence image and it results in candidate regions for skin tumor. Next classifier selects the real tumor spots using PCA components information of reflectance image from the candidate regions.

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The Importance of Food Open Challenge Test in Atopic Dermatitis: The Comparison of Allergy History, Skin-Prick Test, and Specific IgE Detection

  • Noh, Geun-Woong;Ji, Eun-Jung;Park, Jung-Nan;Kim, Kyung-Hee;Do, Min-Hee;Lee, Eung-Kyung;Lee, Sang-Sun
    • Nutritional Sciences
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    • v.2 no.2
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    • pp.119-124
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    • 1999
  • Food allergies play an important role in Atopic Dermatitis (AD). Dietary manipulation is essential in the management of AD. However, there has been a paucity of data reporting the prevalence of food allergies in AD patients in Korea. In this study, the Food Open Challenge Tests (FOCT) were conducted to investigate food allergies in AD patients. The skin-prick test and the detection of specific IgE, as well as allergy history of patient were used to detect food allergies in all AD patients. Elimination diet was conducted for two weeks prior to FOCTs. The prevalence of food allergies by FOCT is as follows: milk (67.3%); chicken (64.2%); pork (62.8%); eggs (61.0%); beef(55.4%) ; wheat (52.0%) and soybean (45.2%). Allergenic food items in Korean AD patients were different from people in other foreign countries. The seven major foods those tested positively by FOCTs were completely eliminated from the replaced diets for two weeks, and were subsequently reintroduced one at a time. Results from FOCTs were not comparable with allergy history or skin-prick tests or specific IgE detection. The sensitivity and specificity of skin-prick tests and specific IgE detection were lower than FOCTs. Allergy history, skin-prick tests, and specific IgE detection are useful for the identification of food allergen but its clinical significance differed according to food items. Therefore, we conclude that even though a 10-day delay was necessary after food challenge, FOCT is a useful and valid method to confirm food allergies and may be essential for the effective control of food allergies for treatment of AD.

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Driver face localization using morphological analysis and multi-layer preceptron as a skin-color model (형태분석과 피부색모델을 다층 퍼셉트론으로 사용한 운전자 얼굴추출 기법)

  • Lee, Jong-Soo
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.6 no.4
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    • pp.249-254
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    • 2013
  • In the area of computer vision, face recognition is being intensively researched. It is generally known that before a face is recognized it must be localized. Skin-color information is an important feature to segment skin-color regions. To extract skin-color regions the skin-color model based on multi-layer perceptron has been proposed. Extracted regions are analyzed to emphasize ellipsoidal regions. The results from this study show good accuracy for our vehicle driver face detection system.