A Novel Approach of Harris Corner Detection of Noisy Images using Adaptive Wavelet Thresholding Technique

TL;DR

This paper introduces a new method combining adaptive wavelet thresholding with Harris corner detection to improve feature extraction in noisy images, addressing the challenge of noise corruption during image acquisition and transmission.

Contribution

It presents a novel approach that integrates adaptive wavelet thresholding with Harris corner detection specifically for noisy images, enhancing feature detection accuracy.

Findings

Improved corner detection accuracy in noisy images

Effective noise reduction using adaptive wavelet thresholding

Enhanced robustness of feature extraction methods

Abstract

In this paper we propose a method of corner detection for obtaining features which is required to track and recognize objects within a noisy image. Corner detection of noisy images is a challenging task in image processing. Natural images often get corrupted by noise during acquisition and transmission. Though Corner detection of these noisy images does not provide desired results, hence de-noising is required. Adaptive wavelet thresholding approach is applied for the same.