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
The emergence of satellite remote sensing technology has provided people with various appropriate, more accurate and easy to use tools for monitoring environmental conditions like the health of vegetation. Using the red and infrared band reflectances, for instance, enables the derivation of a vegetation index called Normalized Difference Vegetation Index (NDVI) in spatial and temporal domains. This index is vital to assess the evolution of drought as well as predict crop yield.
The aim of this study is to analyze a series of deviation of NDVI images, extract virtual drought objects from the series, and investigate for drought patterns from historical images for the growing season after appropriate preprocessing and segmentation of the images.
In this study, the virtual drought objects extracted from images over the growing season (June -September) were found to exhibit a given (similar) pattern for the historical drought years, taken in Ethiopia. The graphical pattern exhibited by historical occurrences of drought for specific areas on the ground, demonstrated nearly a similar time series except the fact that the intensities vary. This variance is an indicative of the difference in the severity level of the droughts at each specific area. Hence, given the implementation of the appropriate prediction tool, this similarity in the time series analysis of the historical data over a drought will give new views for ways in drought prediction for early warning and crop condition monitoring at near real-time.
Signature Identification and Verification (SIV) system is one of the oldest behavioral biometrics, which is being more widely used for the identification and verification applications by a person. Handwritten signature written with a skew is a hurdle to any SIV system. If one has to achieve the accurate results in identification and verification process using signature as a biometric trait, we need to remove the skew of the signatures which are scanned from the documents, and in order to estimate the skew angle and correct the skewness of the signature, skew detection stage is the most important step to be taken care off. In this paper the authors present a Gaussian Mixture Model to estimate the skew angle of a signature. Experimentation is carried out on the Kannada signature database of 30 users.
This paper addresses the problem of designing an autonomous robot for the purpose of navigating in the sensitive areas, keeping focus on localization of the robot. If a robot doesn't know its current location, it is very difficult to determine its further activities. Thus, localization plays a vital role in building an efficient mobile robot. This paper mainly focuses on design and implementation of OI-ROBOT (Object Identification Robot) which mainly comprises of fish eye lens camera to obtain Omni-directional vision, Sensors, to identify the position of the robot and an embedded micro controller that takes charge in target recognition and distortion rectification. The experimental results demonstrate the navigation and selflocalization of the mobile robot. This Robot also helps in fire detection and can be easily available through an Andriod phone or Internet.
Products and services available in the market in the form of brands that make trade practices are very important. Nowadays in the market, brand name is becoming very important. Every organization must have it's unique trademark or logo for uniqueness. Therefore designing an efficient trademark retrieval system and its evaluation for distinctiveness is thus becoming a very tedious job nowadays. Trademark Image Registration is one of the important application area of Content Based Image Retrieval (CBIR). Trademark image registration, where a new candidate mark is compared with the existing marks to ensure that there is no risk of confusion, has long been recognized as a prime application area of CBIR . In the proposed work, a CBIR system is designed for trademark image retrieval based on 3D color histogram (HSV values) technique. The color histogram has the advantages of rotation and translation invariance and it has the disadvantages of lack of spatial information. The experiments were conducted on a database of few trademark images. The performance of the system was evaluated using standard evaluation parameters precision and recall.
Digital image processing plays an important role in the analysis and interpretation of satellite image data. One of the most common degradations in satellite images is their poor contrast quality. Image enhancement technique help in improving the visibility of the image. This suggests the use of contrast enhancement methods as an attempt to modify the intensity distribution of the image. The main aim of this paper is to contrast and edge enhancements for digital satellite images using Discrete Wavelet Transform based Singular Value Decomposition and Morphological Gradient. The objective of the proposed method is that the input image is decomposed into different sub bands through DWT, estimating the singular value matrix of the low–low sub band image, and then, reconstructing the enhanced image by applying inverse DWT. To achieve a sharper color image, an intermediate stage for estimating the high-frequency sub bands is required. This is done by the success of threshold decomposition, gradient based operators are used to detect the locations of the edges, sharpen these detected edges. The results show the efficiency of proposed satellite image enhancement with color balances and not introducing unnecessarily artifacts. The proposed technique has been tested on satellite benchmark images. The quantitative (PSRN, MSE, RMSE, EME) and visual results show the efficiency of the proposed enhancement technique.