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
Human age prediction is useful for many applications. The age information could be used as a kind of semantic knowledge for analysis and understanding of various application domains. The facial image analysis for classifying human age has a vital role in image processing, pattern recognition, computer vision, cognitive science, and Forensic science. Age Specific Human Computer Interaction (ASHCI) has enormous prospects in daily life applications. However, more research has to be done on age estimation techniques. One of the main reasons is that, aging effects on human faces present several unique characteristics which make age estimation a challenging task. In this paper, we give a thorough analysis on the problem of facial aging in context to various approaches and summary of contributions in age estimation. We offer a comparative analysis of various approaches that have been proposed for facial feature extraction and facial aging.
The design of Speech Recognition system has careful attention in the following issues: classification of various types of speech classes, speech representation, and feature extraction techniques. The purpose of this paper is to summarize and compare some of the well known methods used in different stages of Speech Recognition system and identify research topic and applications which are at the front position of this stimulating and challenging field. Speech is the most famous and primary mode of communication among human beings. The communication among human and computer is called as human computer interface. Speech has been the important mode of interaction with computer . This paper gives an overview of major technological viewpoint and approval of the primary progress of speech recognition, and also gives an overview of the techniques developed in each stage of speech recognition.
With the increase in technology threats to personal data, security has been increased. There was a need to introduce a technology that secures our data more efficiently from other unlawful intervention. Automatic Fingerprint Recognition Systems are more powerful and widely used for criminal identification and authentication. Automatic fingerprint recognition is an extremely critical process, especially in low quality and rotation invariant inked and scanned fingerprints because of the quality of ink and the way of finger impression on the surface of the paper or scanner. In this paper, an efficient real time fingerprint identification system for inked fingerprints has been addressed. This method ensures the quality of the fingerprint with greater matching reliability and efficiency.
In clinical diagnosis, the retinal images perform a very important role. Especially, Arteriolar to Venular Ratio (AVR) value is more necessary for the early detection of cardio vascular diseases like hypertension, coronary disease and few retinopathy Diseases. For the diagnosis of these diseases, the AVR must be calculated very accurately and it requires the correct method of vessel identification and also an accurate diameter measurement. In this paper, 'Graph Tracer Algorithm' is implemented for identifying the retinal blood vessel very precisely. The Naive Bayes classifier is used classifying these identified vessels into artery and vein. Finally, the AVR is calculated for these true vessels and the values are compared with the Gold standard AVR values.
Digital Imaging plays an essential role throughout the major regions of life such as clinical diagnosis. However, it faces the trouble of Gaussian noise. Noise corrupts both images and videos equally. The aim of Denoising algorithm must be to remove these kinds of noise. Image denoising should be used because a new noisy image is just not pleasant to watch. Inclusion of, some fine details in the image could possibly be confused with the noise or perhaps vice-versa. Image denoising is a critical task throughout Medical applications, where the complexity regarding noise is predominant, as well as, the contrast regarding medical images tend to be moreover low caused by various photograph acquisition. In the past two decades, denoising is completed by this multi-resolution change like Wavelet change. Denoising pictures using Curvelet change approach has been widely utilized in many fields with its ability to have highly excellent images. Curvelet change is better than wavelet in the expression regarding image advantage, because such geometry attribute of challenge, has obtained achievements now in photograph denoising in the medical field. Here, medical images are denoised with various noise estimators using Curvelet transform. These different noise estimators are compared and measured using image quality metrics such as Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR). The experimental result proves to be a significant one.
