A Comparative Study between Moravec and Harris Corner Detection of Noisy Images Using Adaptive Wavelet Thresholding Technique

TL;DR

This paper compares Moravec and Harris corner detection methods on noisy images, utilizing adaptive wavelet thresholding for de-noising to improve feature detection for object tracking and recognition.

Contribution

It introduces a comparative analysis of corner detection techniques with an adaptive wavelet thresholding approach for de-noising noisy images.

Findings

Harris and Moravec methods are evaluated on noisy images.

Adaptive wavelet thresholding improves corner detection accuracy.

The study highlights the importance of de-noising in feature detection.

Abstract

In this paper a comparative study between Moravec and Harris Corner Detection has been done for obtaining features 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. As 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.