Genuine Forgery Signature Detection using Radon Transform and K-Nearest Neighbour

Forgery Above a Genuine Signature

k-Nearest Neighbour Classifiers

Perhaps the most straightforward classifier in the arsenal or machine learning techniques is the Nearest Neighbour Classifier – classification is achieved by identifying the nearest neighbours to a query example and using those neighbours to determine the class of the query. This approach to classification is of particular importance today because issues of poor run-time performance is not such...

Offline Signature Recognition using Radon Transform

In this work a new offline signature recognition system based on Radon Transform, Fractal Dimension (FD) and Support Vector Machine (SVM) is presented. In the first step, projections of original signatures along four specified directions have been performed using radon transform. Then, FDs of four obtained vectors are calculated to construct a feature vector for each signature. These vectors ar...

Line Detection Using Radon Transform

To extract features from digital images, it is useful to be able to find simple shapes (straight lines, circles, ellipses, etc.). In order to achieve this goal, one can be able to detect a group of pixels that are on a straight line or a smooth curve. That is what a Hough transform is supposed to do. Since the Hough transform is a special case of Radon transform then line detection process is a...

Localization using a Region-Based k-Nearest Neighbour Search

This paper explores a method of performing localization on local vision mobile robots. It describes a method of processing images, extracting regions, and comparing those regions against a database of preprocessed images. Localization is achieved using the k-nearest neighbour algorithm as the basis for approximating the current position. Initial results are provided that show the potential of t...