Brain Tumour Detection using Deep Learning Technique
AI Driven Detection and Remediation of Diabetic Foot Ulcer(DFU)
Advancements in Image Processing: Towards Near-Reversible Data Hiding and Enhanced Dehazing Using Deep Learning
State-of-the-Art Deep Learning Techniques for Object Identification in Practical Applications
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
3D Face recognition has been an area of interest among researchers for the past few decades especially in pattern recognition. The main advantage of 3D Face recognition is the availability of geometrical information of the face structure which is more or less unique for a subject. This paper focuses on the problems of person recognition using 3D Face data. Use of unregistered 3D Face data drastically increases the operational speed of the system with huge database enrolment. In this effort, unregistered 3D Face data is fed to a classifier in multiple spectral representations of the same data. Discrete Fourier Transform (DFT) and Discrete Cosine Transform (DCT) are used for the spectral representations. The face recognition accuracy obtained when the feature extractors are used individually is evaluated. Fusion of the matching scores proves that the recognition accuracy can be improved significantly by fusion of scores of multiple representations. FRAV 3D database is used for testing the algorithm.
In today’s internet era, fortification of digital gratified during communication is penurious. Watermarking affords the security for digital content. Robustness of such watermarking procedure is quite low. For increasing the robustness an approach is introduced which is Neuro fuzzy based watermarking embedding process. Conventional methods have information loss during recovery as, it will be easily hacked, due to lower embedding capacity and, requires more memory and power consumption. The proposed scheme binary image is embedded over color image which uses Shearlet and Inverse Shearlet algorithm for preprocessing of an image and Neuro fuzzy algorithm to embed the bits in green plane of an image. Requirement of Lower Memory and, speed of encryption are improved by Neuro fuzzy algorithm.
Making face recognition more reliable under uncontrolled lighting conditions is one of the most important challenges for practical face recognition systems. Data preprocessing thus becomes an important and emerging topic in many data-driven applications such as image processing and bioinformatics. Dimensionality reduction provides an efficient way for data abstraction and representation as well as feature extraction. It aims to detect intrinsic structures of data and to extract a reduced number of variables (dimensions) that capture and retain the main features of the high-dimensional data. For instance, images contain a large number of pixel values and are presented as high-dimensional arrays. The computationally efficient combination of the most successful local appearance descriptors, like Local Binary Pattern (LBP) with its extension Local Ternary Patterns (LTP) for facial appearance and Gabor filter to encode facial shape over a range of coarser scales are implemented. Here, a data mining approach for dimensionality reduction provides an efficient way for data abstraction and representation as well as feature extraction. It aims to detect intrinsic structures of data and to extract a reduced number of variables (dimensions) that capture and retain the main features of the highdimensional data. The resulting method provides state-of-the-art performance on different data sets that are widely used for testing recognition under difficult illumination conditions: Ex-tended Yale-B, CAS-PEAL-R1. Further experiments show that our preprocessing method outperforms several existing preprocessors for a range of feature sets, data sets and lighting conditions by comparing with previously published methods, achieving a face verification rate of 89.1% at 0.2% false accept rate.
Now-a-days people are heavily dependent on computers to store and process important information. User authentication and identification has become one of the most important and challenging issue in order to secure them from intruders. As traditional user ID and password scheme have failed to provide information security, keystroke dynamics authentication systems can be used to strengthen the existing security techniques. Keystroke dynamic authentication systems are transparent, low cost, and non-invasive for the user, but it has lower accuracy and lower performance compared to other biometric authentication systems. The aim of this paper is to depict a detailed survey of the researches on keystroke dynamic authentication that have used neural networks for classification described in the last two decades. The summary, accuracy of each experiment, and shortcomings of those researches have been presented in this study. Finally, the paper addresses some challenges in keystroke dynamic authentication systems using neural networks that need to be resolve in order to get better performance.
Microarray image enhancement and segmentation procedure are the rudimentary processing steps to obtain high quality image data that would truly reflect the underlying biology in the samples. Robust enhancement and segmentation has been the subject of research for many years, and it is important to understand these crucial steps. Reducing noise from the original image is still a challenging problem. It is important to preserve features like edges and sharp structures for better visualization and further analysis. After denoising, segmentation process is a vital step in microarray image. Selection of suitable algorithm for image segmentation is a very difficult task for a particular type of image although extensive work has been proposed. In this paper, the authors outline fundamental concepts of various existing algorithms, its advantages and limitations for microarray images. These algorithms are classified into various categories and a brief description and analysis of these algorithms is presented. This is an initiative to study, analyze and to provide future direction for research in the areas of microarray enhancement and segmentation techniques.
