Blur Image Detection and Classification using Edge Detection Techniques
Breast Cancer Detection using Machine Learning and Image Processing
Sensor Based Sign Language Recognition System
Contour Based X-Ray Image Classification System for Detection of Covid-19
A Survey on Deep Learning Techniques in Real-Time Applications
An Efficient Foot Ulcer Determination System for Diabetic Patients
Landslide Susceptibility Mapping through Weightages Derived from Statistical Information Value Model
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
Character recognition techniques, associate a symbolic identity with the image of the character, is an important area in pattern recognition and image processing. The principal idea is to convert raw images (scanned from document, typed, pictured, et cetera) into editable text like html, doc, txt or other formats. There is a very limited number of Bangla Character recognition system, if available they can't recognize the whole alphabet set. This paper demonstrates a Character Recognition system from printed Bangla characters using MATLAB. It can also compare the character in one image file to another one. Processing steps here involved binarization, noise removal and segmentation in various levels, features extraction and recognition .
Diabetes Mellitus (DM) is a metabolic disorder gets great high impact on human life in recent years. The WHO has estimated that the number of diabetes in the world by 2025 may reach up to 60 million and India's contribution would be 30 million. Recent report says that one fifth of Asian countries, most lives are lost due to non-communicable diseases like cardiovascular disease, Cancer and Diabetes. Preventing the disease of diabetes is an ongoing area of interest to the healthcare community. Long term complications of DM patients' includes; Retinopathy (Disease affecting human eye/ retina), Neuropathy (Neuro-deficit problems/Nerve Damages), Nephropathy (Chronic kidney failure), Gastropathy, Cardiovascular disease (Heart attack), Cerebrovascular disease (Parkinson's disease, Stroke), Foot ulcers and Premature death. Diabetes Retinopathy (DR) is retinopathy, it only affects people who have had diabetes for a long time period and can result in blindness/loss of vision. The sight-threatening stages of DR can be broadly classified as Non-Proliferative Diabetic Retinopathy (NPDR), and Proliferative Diabetic Retinopathy (PDR). This study presents the DR patients' prevention and detecting the retinal images in early stages, can be treated more easily and clinically. A study by Indian Council for Medical Research (ICMR's) INDIAB (India Diabetes) confirmed that one out of 10 people in Tamilnadu are diabetic, and every two adults in a group of 25 are in the pre-diabetic stage. From this study, it is mandatory on clinical research to screen the diabetes patients in the line of retinopathy. MATLAB – classifier predicts the high accuracy level of retinal images classification out of three stages in non-proliferative retinopathy.
The image enhancement techniques have become an important pre-processing tool for digital vision processing applications, where one of the most common degradations in images is their poor contrast quality. This suggests the use of contrast enhancement methods as an attempt to modify the intensity distribution of the image. The main objective of this paper is the analysis of mathematical morphological approach with comparison to various other state-of-art techniques for addressing the problems of low contrast in images. A technique used for contrast enhancement is the combined use of the top-hat bottom-hat transforms and logical operations. Histogram Equalization (HE) is one of the common methods used for improving contrast in digital images. This method is simple and effective for global contrast enhancement of images, but it suffers from some drawbacks. Contrast Limited Adaptive Histogram Equalization (CLAHE) enhances the local contrast of the images without the amplification of the noise. The proposed technique is best while compared qualitatively and quantitatively with existing technique.
Just like the uniqueness of physiological features of various biometric modalities, Tongue Print of a person possesses uniqueness in itself. Tongue print of every Human is different and unique. Besides being unique it the least manipulated organ that it enhances security for identification and authentication of a person, avoiding most of the forgery cases. This paper introduces the feature extraction and the methodology for the study of person identification using tongue print. Speeded Up Robust Factor (SURF) is used, which is a local invariant interest point detector and descriptor.
Image processing is the emerging field where it is possible to increase or decrease the quality of image without affecting the required features or objects in the image for particular applications. It includes the fields of medical, astronomy, satellites, etc., particularly in the digital medical imaging technologies such as Computerized Tomography (CT), Positron Emission Tomography (PET), Magnetic Resonance Imaging (MRI), as well as combined modalities, e.g. SPECT/CT has revolutionized modern medicine. Resolutions of MRI medical images are very much intended for diagnosis and curing of physical and metabolic activities of the volume below the skin. Many image processing techniques for increasing the MRI image resolution are proposed which are not suitable for noise corrupted images. To provide solution for this, Super- Resolution techniques are introduced, all the Super-Resolution techniques are based on multiple images. After that, sparse representation techniques are proposed which are the single image super resolution methods, but it is based on the constructed databases, which require large computational tasks, and also it is a time consuming process. The proposed method contains only a single image which can be super resolved and denoised by the different techniques with increased PSNR (Peak Signal to Noise Ratio) and also compromising SSIM (Structural Similarity) with better visibility objects in the MRI image.