A Robust Skin Colour Segmentation Using Bivariate Pearson Type IIαα (Bivariate Beta) Mixture Model

Jagadesh, B. N.; Rao, K. Srinivasa; Satyanarayana, Ch.

doi:10.5815/ijigsp.2012.11.01

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…Zou et al [164] also applied 3 Gaussian clusters to model different ethnicities. BPMM has been also addressed for the task of skin segmentation [165,166]. Here, BPMM type IIaα (Bivariate Beta) is used to model skin color in HS color space.…”

Section: ) Parametric Modelsmentioning

confidence: 99%

A Comprehensive Survey on Human Skin Detection

Mahmoodi

Sayedi

2016

IJIGSP

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Human Skin detection is one of the most widely used algorithms in vision literature which has been numerously exploited both directly and indirectly in multifarious applications. This scope has received a great deal of attention specifically in face analysis and human detection/tracking/recognition systems. As regards, there are several challenges mainly emanating from nonlinear illumination, camera characteristics, imaging conditions, and intra-personal features. During last twenty years, researchers have been struggling to overcome these challenges resulting in publishing hundreds of papers. The aim of this paper is to survey applications, color spaces, methods and their performances, compensation techniques and benchmarking datasets on human skin detection topic, covering the related researches within more than last two decades. In this paper, different difficulties and challenges involved in the task of finding skin pixels are discussed. Skin segmentation algorithms are mainly based on color information; an in-depth discussion on effectiveness of disparate color spaces is elucidated. In addition, using standard evaluation metrics and datasets make the comparison of methods both possible and reasonable. These databases and metrics are investigated and suggested for future studies. Reviewing most existing techniques not only will ease future studies, but it will also result in developing better methods. These methods are classified and illustrated in detail. Variety of applications in which skin detection has been either fully or partially used is also provided.

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Section: ) Parametric Modelsmentioning

confidence: 99%

A Comprehensive Survey on Human Skin Detection

Mahmoodi

Sayedi

2016

IJIGSP

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“…In some cases, we need to remove the rest part of the arm, so a skin color segmentation technique is needed. Modeling the human skin color is a very challenging field [19]. We use the 3D tracking data to generate an extended mask around the green arm to restrict the area of working and apply the Mahalanobis distance [20] to remove the skin color of the rest of the arm that gives high rates of skin detection process [21].…”

Section: F Compostingmentioning

confidence: 99%

Cut off Your Arm: A Medium-Cost System for Integrating a 3D Object with a Real Actor

Afifi¹,

Korashy²,

William³

et al. 2014

IJIGSP

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In the film industry, many tricks have been employed using the integration of a 3D object with a real actor. Usually, attaching a 3D object with a real actor is a costly process because of the usage of an expensive motion capture system. This paper presents a system using a medium-cost motion capture system and a chroma-keying technique for generating a video footage of an actor with an integrated 3D object (e.g. amputated arm). The result of the proposed system shows the attaching process of different 3D objects with a real actor who is combined with a new background scene in the same viewpoint.

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“…Very little work has been reported in skin colour segmentation methods using bivariate Pearsonian mixture models except the works of Jagadesh et al (2012aJagadesh et al ( , 2012b and Srinivasa Rao et al (2012). In these papers, the authors have considered that the image consisting of either African/Asian/European only but not a mixture of all these three races.…”

Section: Introductionmentioning

confidence: 99%

A unified approach for skin colour segmentation using generic bivariate Pearson mixture model

Jagadesh

Rao

Satyanarayana³

2020

IJAIP

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Skin colour segmentation is rapidly growing area of research in computer science for identification and authentication of persons. In this paper, a novel generic bivariate Pearsonian mixture model for skin colour segmentation is proposed. It is observed that the hue and saturation of the colour image better characterise the features of the individual human races. In general, the human race can be characterised in to three categories namely Asian, African and European. The feature of the skin colour of these races are modelled by three different bivariate Pearsonian distributions. The combination of all these three races of people in an image can be characterised by a three component mixture model. Deriving the updated equations of the EM-algorithm, the generic bivariate Pearson mixture model parameters are estimated. The initialisation of the model parameters are done through moment method of estimation and K-means algorithm. The segmentation algorithm is developed using component maximum likelihood under Bayesian frame. The performance of the proposed algorithm is carried by experimentation with random sample of five images and computing the segmentation performance metrics such as PRI, GCE and VOI. The efficiency of the proposed model with that of bivariate GMM is carried through confusion matrix and ROC curves. It is observed that the proposed algorithm outperforms the existing algorithms.

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A Robust Skin Colour Segmentation Using Bivariate Pearson Type IIαα (Bivariate Beta) Mixture Model

Cited by 6 publications

References 10 publications

A Comprehensive Survey on Human Skin Detection

A Comprehensive Survey on Human Skin Detection

Cut off Your Arm: A Medium-Cost System for Integrating a 3D Object with a Real Actor

A unified approach for skin colour segmentation using generic bivariate Pearson mixture model

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