B. N. Jagadesh

Bio: B. N. Jagadesh is an academic researcher. The author has contributed to research in topics: Image segmentation & Segmentation. The author has an hindex of 2, co-authored 3 publications receiving 7 citations.

A Robust Skin Colour Segmentation Using Bivariate Pearson Type II

Abstract: Probability distributions formulate the basic framework for developing several segmentation algorithms. Among the various segmentation algorithms, skin colour segmentation is one of the most important algorithms for human computer interaction. Due to various random factors influencing the colour space, there does not exist a unique algorithm which serve the purpose of all images. In this paper a novel and new skin colour segmentation algorithms is proposed based on bivariate Pearson type II a

Skin Colour Segmentation using Fintte Bivariate Pearsonian Type-IV a Mixture Model

Abstract: The human computer interaction with respect to skin colour is an important area of research due to its ready applications in several areas like face recognition, surveillance, image retrievals, identification, gesture analysis, human tracking etc. For efficient skin colour segmentation statistical modeling is a prime desiderata. In general skin colour segment is done based on Gaussian mixture model. Due to the limitations on GMM like symmetric and mesokurtic nature the accuracy of the skin colour segmentation is affected. To improve the accuracy of the skin colour segmentation system, In this paper the skin colour is modeled by a finite bivariate Pearsonian type-IVa mixture distribution under HSI colour space of the image. The model parameters are estimated by EM algorithm. Using the Bayesian frame the segmentation algorithm is proposed. Through experimentation it is observed that the proposed skin colour segmentation algorithm perform better with respect to the segmentation quality metrics like PRI, GCE and VOI. The ROC curves plotted for the system also revealed that the developed algorithm segment pixels in the image more efficiently. Keywords : Skin colour segmentation, HSI colour space, Bivariate Pearson type IVa mixture model, Image segmentation metrics.

Analysis of Shape Signature in First and Second Derivatives by Using Wavelet Transformation

Abstract: The object recognition techniques are popular in computer vision and pattern recognition research field. The present paper focuses on the design of a novel shape signature based on angular information. The Wavelet coefficients are also used to formulate the shape signature. Further, the angular information is captured at two different derivatives of the input image. The angular information is used to estimate the tangential measure for each of the representative point of the input image. The represented shape signature is described with the Fourier transformation. The Fourier descriptors are used for the classification stage. The classification stage uses Euclidean distance measure for the classification. The proposed approach is evaluated on the standard database. The estimated performance measures show the efficiency of the proposed approach.

A Comprehensive Survey on Human Skin Detection

Abstract: 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.

New face features to detect multiple faces in complex background

Abstract: We propose in this paper a novel method for fast detecting faces even in the presence of constraints such as variation in illumination, human skin tone and facial expression, pose, and background (indoor or outdoor). Our system processes color images in a manner that would decrease the area of a face that must be scanned and for this, a parametric model based on Gaussian mixture models (GMM) applied to segmented regions of skin color. To select, relevant and minimum features from the faces candidates firstly, the variance based Haar-like features are extracted than merged with local binary patterns (LBP) features previously extracted. The resulting fused vectors construct Support Vector Machine database training to achieve a high detection rate. To verify the effectiveness of the proposed method, we carried out a serial of detailed experiments on three difficult face detection datasets (Caltech, BAO and UCD) which contain images featuring both single and multiple faces, presented in a variety of positions and featuring complex backgrounds, both indoor and outdoor. Experimental results have shown that our approach gives better results (91.04%) than those obtained by systems based on primitive Haar-like features and AdaBoost, providing a higher detection rate of 16.51%. Furthermore, the shorter detection time of our method is guaranteed by reducing the dimension of feature vectors and by limited search of faces on only the skin-detected regions and not on the entire image.

Contribution des familles exponentielles en traitement des images

Abstract: Cette these est consacree a l'evaluation des familles exponentielles pour les problemes de la modelisation des bruits et de la segmentation des images couleurs. Dans un premier temps, nous avons developpe une nouvelle caracterisation des familles exponentielles naturelles infiniment divisible basee sur la fonction trace de la matrice de variance covariance associee. Au niveau application, cette nouvelle caracterisation a permis de detecter la nature de la loi d'un bruit additif associe a un signal ou a une image couleur. Dans un deuxieme temps, nous avons propose un nouveau modele statistique parametrique mulltivarie base sur la loi de Riesz. La loi de ce nouveau modele est appelee loi de la diagonale modifiee de Riesz. Ensuite, nous avons generalise ce modele au cas de melange fini de lois. Enfin, nous avons introduit un algorithme de segmentation statistique d'image ouleur, a travers l'integration de la methode des centres mobiles (K-means) au niveau de l'initialisation pour une meilleure definition des classes de l'image et l'algorithme EM pour l'estimation des differents parametres de chaque classe qui suit la loi de la diagonale modifiee de la loi de Riesz.

A Robust Skin Colour Segmentation Using Bivariate Pearson Type II

Abstract: Probability distributions formulate the basic framework for developing several segmentation algorithms. Among the various segmentation algorithms, skin colour segmentation is one of the most important algorithms for human computer interaction. Due to various random factors influencing the colour space, there does not exist a unique algorithm which serve the purpose of all images. In this paper a novel and new skin colour segmentation algorithms is proposed based on bivariate Pearson type II a

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

Abstract: 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.