COMPARISON OF SELECTED IMAGE PROCESSING TECHNIQUES

A. P. Adewole; M. O. Mustapha

A. P. Adewole
M. O. Mustapha

Keywords: Principal Component Analysis and Local Pixel Grouping, Denoising, Feature Extration, Non Linear Filtering, Object Identification, scale-invariant feature transform, speeded up robust features, Image Processing Algorithms

Abstract

Image as an important artifact faced with several constraints that may inhibit its usefulness. These constraints includes noise, Identification of objects in the image and extraction of features. In this paper, the denoising methods of Two Stages Image Denoising By Principal Component Analysis With Local Pixel Grouping(PCA - LPG) and Non Linear Filtering Algorithm For Underwater Images, the object identification methods of SCALE-INVARIANT FEATURE TRANSFORM (SIFT) and SPEEDED UP ROBUST FEATURES (SURF), the feature extraction methods of thresholding and subtraction and template matching are compared experimentally. The experimental evaluation of these algorithms made it possible to draw some conclusions. These conclusions are supported from the results of the implementations of each technique, hence the recommended technique for denoising is Local Pixel Grouping (PCA - LPG), the recommended technique for object identification is SPEEDED UP ROBUST FEATURES (SURF) and the recommended technique for feature extraction is tresholding and subtraction. The recommended techniques for each of the concept were implemented in C# programming language with the help of an open source computer vision library EmguCV.

Author Biographies

A. P. Adewole

Department of Computer Sciences, University of Lagos, Lagos. Nigeria

M. O. Mustapha

Department of Computer Sciences, University of Lagos, Lagos. Nigeria.