A new color based method for skin detection using RGB vector space

Aznaveh, Mohsen Mahmoudi; Mirzaei, Hamidreza; Roshan, Emitis; Saraee, Mohamad

doi:10.1109/hsi.2008.4581568

Cited by 13 publications

(6 citation statements)

References 10 publications

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“…A part of the process of the segmentation is to construct a rule of decision that discriminates or differs between the pixels of an image that corresponds to skin color and non-skin color. This can be done by nonparametric modeling as Bayesian classifier and histogram-based thresholding or parametric modeling such as Gaussian model (GM) and Gaussian mixture model (GMM) [10]. This paper describes the technique of histogram threshold to image segmentation of skin color.…”

Section: Skin Color Segmentationmentioning

confidence: 99%

Detection and Tracking of the Regions of Skin Using the Technique HS-ab

Alejo¹,

Gallegos‐Funes²

2018

Biomimetic Prosthetics

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The content of this work is the proposal of the technical HS-ab for the detection and tracking of the regions of skin in real time. First, two proposed techniques are analyzed for the modeling of skin color in images using a combination of color spaces HSV with YCbCr and HSV with CIELab. In the process of definition of the intervals of pixels is taken into account the following: non-skin color uses the components H and S, whereas skin color uses the components C b , C r , a, and b. The results showed that the HS-ab technique is better than the HS-CbCr technique because of the precision in detecting skin color according to the percentages C (34.8%) and CDR (67%). After, the morphologic operations are applied to debug the images of the previous segmentation and detect regions of skin using methods such as blob extraction and contour detection. Subsequently, the tracking of skin color consists of calculating the moments and positions of each frame to know the trajectory of the regions of skin. The purpose of the work is to design an easy-to-use computer vision system that will facilitate the early rehabilitation of patients before they are clinically ready to be fitted with a prosthesis.

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Section: Skin Color Segmentationmentioning

confidence: 99%

Detection and Tracking of the Regions of Skin Using the Technique HS-ab

Alejo¹,

Gallegos‐Funes²

2018

Biomimetic Prosthetics

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“…The support signal represents the matching error produced by the pixels of the circle candidate (action). (15) where a function verifies the pixel existence in and is the no. of pixels lying on the corresponding to .…”

Section: E Circle Detection Using Lamentioning

confidence: 99%

“…In reference, [15] the RGB vector space is used with the neighborhood vector to determine the skin color of a human. To detect the human by using skin color is not an easy task.…”

Section: ) Rgb Vector Spacementioning

confidence: 99%

SURF and LA with RGB Vector Space Based Detection and Monitoring of Manholes with an Application to Tri-Rotor UAS Images

Ali¹,

Nanjing²,

Wang³

et al. 2017

IJET

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The detection of manhole's in a road by using Unmanned Aerial System or Vehicle (UAS) offers significant precision, however detection of manholes in above ground level images is a difficult task, with the help of Digital Single Lens Reflex (DSLR) camera and by using an image processing technique it is quite easy to detect small objects from the aerial view. Previously many methods is used to detect the manhole's covers, and many of them is quite accurate about 90% accuracy rate. But all the method applied for the detection of a covered manhole, but no one detects if there is no manhole cover lie in a manhole frame. This manuscript we present Speeded-Up Robust Feature's (SURF) algorithm for the key point detection of the manhole cover is present or not, and by marking many key points detects the manhole. After that cross checking our algorithm accuracy by using another method Learning Automata (L.A) with color gradient vector Red, Green, Blue (RGB) for the detection of a circular shape of manhole cover and color recognition for the cover is present or not.Index Terms-Manhole detection, speeded-up robust feature, Learning automata, RGB vector space.

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“…En el segundo punto, el modelado del color de la piel es construir una regla de decisión que discrimine o diferencie entre los píxeles de una imagen que corresponden al color de la piel y aquéllos que no. Esto puede llevarse a cabo por definiciones explícitas de regiones de color, modelado no paramétrico de la distribución de piel o modelado paramétrico de la distribución de piel [6].…”

Section: Introductionunclassified

Comparación de dos técnicas propuestas HS-CbCr y HS-ab para el modelado de color de piel en imágenes

Alejo¹,

Gallegos-Funes²

2016

RCS

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Resumen. El contenido de este trabajo es la propuesta de dos técnicas para el modelado de color de piel en imágenes usando una combinación de espacios de colores HSV con YCbCr y HSV con CIELab. Se determina los intervalos de pixeles color no piel usando los componentes H y S, y los intervalos de pixeles color piel de los componentes Cb, Cr, a y b. Se compara los resultados de la técnica HS-CbCr que es la combinación de los componentes H y S (pixeles color no piel) con los componentes Cb y Cr (pixeles color piel), contra la técnica HSab que de la misma forma es la combinación de H y S pero con los componentes a y b (pixeles color piel). Se analiza la eficiencia de cada técnica para la segmentación de piel en base al porcentaje de detección de piel y no piel. Los resultados experimentales mostraron que la técnica HS-ab es mejor que la técnica HS-CbCr por la precisión además se descarta los obstáculos de luminancia pues en ambas técnicas se elimina la componente de luminancia.Abstract. The content of this paper is the proposal of two techniques for the modeling of skin color in images using a combination of spaces of colors HSV with YCbCr and HSV with CIELab. To determine the intervals of pixels nonskin color uses the components H and S, on the other hand, the intervals of pixels skin color uses the components Cb, Cr, a and b. There are compared the results of the technique HS-CbCr which is the combination of the components H and S (pixels non-skin color) with the components Cb and Cr (pixels skin color), against the technique HS-ab that of the same form is the combination of H and S but with the components a and b (pixels skin color). It analyzes the efficiency of each technique for the segmentation of skin based on the percentage of detection of skin and non-skin. The experimental results showed that technique HS-ab is better than the technique HS-CbCr because of the precision in addition the obstacles of luminance are discarded because in both techniques the luminance component is eliminated.

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A new color based method for skin detection using RGB vector space

Cited by 13 publications

References 10 publications

Detection and Tracking of the Regions of Skin Using the Technique HS-ab

Detection and Tracking of the Regions of Skin Using the Technique HS-ab

SURF and LA with RGB Vector Space Based Detection and Monitoring of Manholes with an Application to Tri-Rotor UAS Images

Comparación de dos técnicas propuestas HS-CbCr y HS-ab para el modelado de color de piel en imágenes

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