Texture Feature Abstraction Based on Assessment of HOG and GLDM Features for Diagnosing Brain Abnormalities in MRI Images
Recognition of vehicles has always been a desired technology for curbing the crimes done with the help of vehicles. Number imprinted on plates of cars and motorbikes are consist of numerals and alphabets, and these plates can be easily recognized. The uniqueness of combination of characters and numbers can be easily utilized for multiple purposes. For instance, fines can be imposed on people automatically for wrong parking, toll fee can be automatically collected just by recognizing the number plate, apart from these two there may be several numbers of uses can be accommodated. Computer vision is comprehended as a sub space of the computerized reasoning furthermore software engineering fields. Alternate ranges most firmly identified with computer vision are picture handling, picture examination and machine vision. As an exploratory order, computer vision is apprehensive with the counterfeit frameworks that concentrate data from pictures and recordings. The picture information can take numerous structures, for instance, segmentations of videos, taken from several cameras. This thesis presents a training based approach for the recognition of vehicle number plate. The whole process has been divided into three stages i.e. capturing the image, plate localization and recognition of digits over the plate. The characteristics of HOG have been utilized for training and SVM has been used for adopted for classifying while recognizing. This algorithm has been checked for more than 100 pictures.
