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Rao K. Srinivasa

Bio: Rao K. Srinivasa is an academic researcher. The author has contributed to research in topics: Image segmentation & Queue. The author has an hindex of 2, co-authored 2 publications receiving 9 citations.

Papers
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Journal ArticleDOI
TL;DR: A new Image Segmentation method based on Finite Doubly Truncated Bivariate Gaussian Mixture Model that outperforms the existing model based image segmentation methods.
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.

6 citations

Journal Article
TL;DR: The sensitivity analysis of the model reveals that the dependency parameter and state dependent service rates can reduce congestion in queues and average waiting time of the customer.
Abstract: In this paper, we develop and analyze an interdependent forked queueing model with state dependent service times. Here, it is assumed that the arrival and service processes are correlated and follows a multivariate poisson process. Using the differencedifferential equations, the joint probability generating function of the number of customers in each queue is derived. The system performance like the average number of customers in each queue, the average waiting time of a customer, the throughput of each service station, the idleness of the servers are derived explicitly. The sensitivity analysis of the model reveals that the dependency parameter and state dependent service rates can reduce congestion in queues and average waiting time of the customer. This model also includes some of the earlier models as particular cases for specific values of the parameters. The forked queueing models are much useful for analyzing and monitoring several communication networks and production processes.

3 citations


Cited by
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Journal ArticleDOI
TL;DR: By using interdependent controlled arrival and service rates and reneging parameters along with time-sharing concept, the queueing model is developed for more versatile situations encountered in production/ manufacturing industries.
Abstract: The present investigation deals with the multi component machine repair problem by including the concepts of reneging and interdependent controlled rates. We consider a machining system consisting of M operating units, S 1 and S 2 warm spares of two kinds and a repair facility having R permanent repairmen and r additional removable repairmen. When all the spares are used and the operating units fail, the system works in degraded mode. By using interdependent controlled arrival and service rates and reneging parameters along with time-sharing concept, the queueing model is developed for more versatile situations encountered in production/ manufacturing industries. The solution is obtained by using recursive technique. The expressions for the queue size distribution and expected queue length are established. Some more performance indices such as expected number of failed units waiting for the service, expected number of busy repairmen, expected number of both types of spare parts functioning as standbys, etc., are obtained with the help of recursive technique. By taking numerical illustrations, the sensitivity analysis is provided to validate the analytical results.

7 citations

Journal ArticleDOI
TL;DR: The application of multivariate generalized Gaussian mixture probability model for segmenting the texture of an image integrating with hierarchical clustering, developed using component maximum likelihood under Bayesian frame is addressed.
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.

5 citations

01 Jan 2012
TL;DR: In this paper, a novel and new skin color segmentation algorithm is proposed based on bivariate Pearson type II a for human computer interaction, which is one of the most important segmentation algorithms.
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

5 citations

01 Jan 2014
TL;DR: The survey of the skin pixel segmentation using the learning algorithms is presented and it is shown that skin classifier identifies the boundary of theskin image in a skin color model based on the training dataset.
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.

3 citations

Journal ArticleDOI
TL;DR: The sensitivity analysis of the model revealed that the time and load dependent service rates have significant influence on congestion of queues and waiting time and can predict the performance measures more accurately for small period of time.
Abstract: This paper develops and analyzes a two node tandem queueing model with phase type service having time and state dependent service rates. Here, it is assumed that the service processes of the two service stations follow non-homogenous Poisson processes and service rates are dependent on the number of customers in the queue connected to it. Using the difference-differential equations, the joint probability generating function of the queue size distribution is derived. The system performance measures such as average number of customers in the queue, throughput of the service stations, and average waiting time of customers in the queue and in the system and the variance of the number of customers in each queue are derived. A numerical illustration is presented. The sensitivity analysis of the model revealed that the time and load dependent service rates have significant influence on congestion of queues and waiting time. The transient analysis can predict the performance measures more accurately for small period of time. This model can also include some of the early models as particular cases.

2 citations