Ch. Satyanarayana

Bio: Ch. Satyanarayana is an academic researcher. The author has contributed to research in topics: Image segmentation & Fault detection and isolation. The author has an hindex of 2, co-authored 6 publications receiving 11 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

Sentiment Analysis using Neural Network and LSTM

Abstract: People put their opinions or views on various events happening in the society or world. Twitter is one of the best social networking sites where a huge amount of data generates on the daily basis. These data can be used to classify their tweets based on various sentiments attached to them. Numerous technologies are applied to analyse the sentiments of users. Sentiment analysis needs a very efficient method to manage long arrangement data and their drawn-out dependencies. In this paper, we have applied a deep learning technique to perform Twitter sentiment analysis. Simple Neural Network, Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN) methods are applied for the sentiment analysis and their performances are evaluated. The LSTM is the best among all proposed techniques with the highest accuracy of 87%. We have collected a Twitter dataset from Kaggle to perform our experiment. The future improvement of the proposed research should include REST APIs and web crawling-based solutions to get live tweets to perform real-time analytics. We have analysed 1.6 million tweets in our research work.

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.

Design of Generalized Weighted Laplacian Based Quality Assessment for Software Reliability Growth Models

Abstract: The reliability of a software depends on the quality. So, the software growth models require efficient quality assessment procedure. It can be estimated by various parameters. The current paper proposes a novel approach for assessment of quality based on the Generalized Weighted Laplacian (GWL) method. The proposed method evaluates various parameters for detection and removal time. The Mean Value Function (MVF) is then calculated and the quality of the software is estimated, based on the detection of failures. The proposed method is evaluated on process CMMI level 5 project data and the experimental results shows the efficiency of the proposed method.

Residual Fault Detection and Performance Analysis of G-O Software Growth Model

Abstract: Quality assessment for the software growth models (SRGMs) is crucial is deciding the growth model for the application. The main goal of the growth model is to provide higher efficiency with optimal reliability. So there should be an analytical approach for the measurement of these factors. Here in this paper we propose one such exponential approach for the assessment of G-O (Goel, Okumoto) growth model under certain residual faults based on NHPP.

Software reliability modeling : fundamentals and applications

Abstract: Chapter 1 Introduction to Software Reliability Modeling and Its Applications.- 1. Introduction.- 2. Definitions and Software Reliability Model.- 3. Software Reliability Growth Modeling.- 4. Imperfect Debugging Modeling.- 4.1 Imperfect debugging model with perfect correction rate.- 4.2 Imperfect debugging model with introduced faults.- 5. Software Availability Modeling.- 5.1 Model description.- 5.2 Software availability measures.- 6. Application of Software Reliability Assessment.- 6.1 Optimal software release problem.- 6.2 Statistical software testing-progress control.- 6.3 Optimal testing-effort allocation problem. Chapter 2 Recent Developments in Software Reliability Modeling.- 1. Introduction.- 2. Human Factor Analysis.- 3. Stochastic Differential Equation Modeling.- 4. Discrete NHPP Modeling.- 5. Quality-Oriented Software Management Analysis.

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.