Rao P. Srinivasa

Bio: Rao P. Srinivasa is an academic researcher. The author has contributed to research in topics: Image segmentation & Gaussian. The author has an hindex of 1, co-authored 1 publications receiving 6 citations.

Image Segmentation and Retrievals based on Finite Doubly Truncated Bivariate Gaussian Mixture Model and KMeans

Abstract: A new Image Segmentation method based on Finite Doubly Truncated Bivariate Gaussian Mixture Model is proposed in this paper. The Truncated Bivariate Gaussian Distribution includes several of the skewed and asymmetric distributions as particular cases with finite range. This distribution also includes the Gaussian distribution as a limiting case. We use Expectation maximization (EM) algorithm to estimate the model parameters of the image data and the number of mixture components is estimated by using K-means Clustering algorithm. The K-means clustering algorithm is also utilized for developing the initial estimates of the EM algorithm. The segmentation is carried out by clustering of feature vector into appropriate component according to the Maximum Likelihood Estimation criteria. The advantage of our method lies its efficiency on initialization of the model parameters and segmenting the images in a totally unsupervised manner. The performance of the proposed algorithm is studied by computing the segmentation performance measures like, PRI, GCE and VOI. The ability of this method for image retrieval is demonstrated by computing the image quality metrics for six images namely OSTRICH, POT, TOWER, BEARS, DEER and BIRD. The experimental results show that this method outperforms the existing model based image segmentation methods.

Studies on Texture Segmentation Using D- Dimensional Generalized Gaussian Distribution integrated with Hierarchical Clustering

Abstract: Texture deals with the visual properties of an image. Texture analysis plays a dominant role for image segmentation. In texture segmentation, model based methods are superior to model free methods with respect to segmentation methods. This paper addresses the application of multivariate generalized Gaussian mixture probability model for segmenting the texture of an image integrating with hierarchical clustering. Here the feature vector associated with the texture is derived through DCT coefficients of the image blocks. The model parameters are estimated using EM algorithm. The initialization of model parameters is done through hierarchical clustering algorithm and moment method of estimation. The texture segmentation algorithm is developed using component maximum likelihood under Bayesian frame. The performance of the proposed algorithm is carried through experimentation on five image textures selected randomly from the Brodatz texture database. The texture segmentation performance measures such as GCE, PRI and VOI have revealed that this method outperform over the existing methods of texture segmentation using Gaussian mixture model. This is also supported by computing confusion matrix, accuracy, specificity, sensitivity and F-measure.

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

Data Mining Based Skin Pixel Detection Applied On Human Images: A Study Paper

Abstract: Skin segmentation is the process of the identifying the skin pixels in a image in a particular color model and dividing the images into skin and non-skin pixels. It is the process of find the particular skin of the image or video in a color model. Finding the regions of the images in human images to say these pixel regions are part of the image or videos is typically a preprocessing step in skin detection in computer vision, face detection or multiview face detection. Skin pixel detection model converts the images into appropriate format in a color space and then classification process is being used for labeling of the skin and non-skin pixels. A skin classifier identifies the boundary of the skin image in a skin color model based on the training dataset. Here in this paper, we present the survey of the skin pixel segmentation using the learning algorithms.

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.