Landslide Susceptibility Mapping through Weightages Derived from Statistical Information Value Model
An Efficient Foot Ulcer Determination System for Diabetic Patients
Statistical Wavelet based Adaptive Noise Filtering Technique for MRI Modality
Real Time Sign Language: A Review
Remote Sensing Schemes Mingled with Information and Communication Technologies (ICTS) for Flood Disaster Management
FPGA Implementation of Shearlet Transform Based Invisible Image Watermarking Algorithm
A Comprehensive Study on Different Pattern Recognition Techniques
User Authentication and Identification Using NeuralNetwork
Flexible Generalized Mixture Model Cluster Analysis withElliptically-Contoured Distributions
Efficient Detection of Suspected areas in Mammographic Breast Cancer Images
The paper presents an article on image encryption scheme. The major approach to protect an image is encryption. There are many image encryption techniques. Image encryption schemes have been increasingly studied to meet the demand for real-time secure image transmission over the Internet and through wireless networks. Traditional image encryption algorithm such as data encryption standard (DES), has the weakness of low-level efficiency when the image is large. The chaos-based encryption has suggested a new and efficient way to deal with the intractable problem of fast and highly secure image encryption. One-dimensional chaotic system with the advantages of high-level efficiency and simplicity, such as Logistic map, has been widely used now developed. The encryption use the Arnold cat map to scramble the image. Arnold's cat map's feature is that image being apparently randomized by the transformation but returning to its original state after a number of steps. The overall objective of this paper is to propose an integrated technique for image encryption.
The traditional mixture model assumes that, a dataset is composed of several clusters of Gaussian distributions. In real life, however, data often do not fit the restrictions of normality very well. It is likely that data from a single cluster exhibiting non- Gaussian shape characteristics could be erroneously modeled as multiple clusters, resulting in suboptimal inference and decision making. More flexibility is given to the mixture model by generalizing the Gaussian mixture model to use Elliptically-Contoured Distributions. While still symmetric, multivariate Elliptically-Contoured Distributions encompass a wide range of peak and tail characteristics. Distributions that can be generated as special cases include the Power Exponential, Gaussian, Laplace, Student’s T, Cauchy, and Uniform. This generalization makes mixture modeling more robust against nonnormality. Two computational algorithms, GARM and GEM,were adapted to optimize the Elliptically Contoured Mixture model and use results from robust estimation theory in order to data-adaptively regularize both. Finally, the Fisher information matrix and information criterion ICOMP are extended to score the new mixture model. These tools are used simultaneously to select the best mixture model and classify all observations without making any subjective decisions. The performance of the proposed mixture model is first demonstrated on a simulated dataset with extreme overlap. Secondly, the Elliptically-Contoured Mixture model is used on a medical dataset in which, the clinicallydetermined cluster structure are recovered For both datasets, the proposed mixture model substantially improves classification rates over the Gaussian mixture model.
The tongue has many relationships and acquaintances in the body, both to the peaks and the internal organs. It can present strong visual pointers of a person's overall agreement or disagreement. Tongue diagnosis has played such a prominent role in the diagnosis. The tongue is a muscular organ in the oral cavity that is associated with the function of deglutition, taste and speech. It acts as an easily accessible mirror of the health of a person, and indicates the state of hydration of the body. Some characteristic changes occur in the tongue in some particular diseases. In this paper, the tongue image of the patient is captured by digital camera and then the disease affected part of the tongue is segmented by threshold based algorithm which separate tongue from all other features like teeth, skin etc. The image will be classified based on the features extracted from the segmented part exhausting Neural Networks.
Texture analysis is one of the important and most useful tasks in image processing applications. Several texture models have been developed over the past few years and Local Binary Patterns (LBP) are one of the simple and efficient approaches among them. The main disadvantage of “LBP” method is the complex computation of vector generation. Here an innovative classifier, called Extended Rule Based Local Binary Pattern (ERLBP) is given, which is an efficient model and its performance has been compared with other widely used texture models to show the computational superiority, robustness to gray scale variations and improved discriminating capability.
Wireless Capsule Endoscopy (WCE) is a transitional endoscopy, diagnosing disease in gross area of the Gastrointestinal (GI) tract ahead the reach of other endoscopy. Cancer is a leading cause of death. As per WHO report 13 million people were affected by cancer disease every year. Bowel cancer is a third most cancer occurring in the GI system. Recent works addresses various screening methods, and adaptive controls to improve the analysis completion. This paper describes a robust method of segmenting the bowel images and to discriminate the normal and affected location using Lab VIEW. The system shows, that the threshold adjusted (segregation) capsule endoscopic images are emphatic, and sophisticate classification by adequate software used to afford an image as clarion.
