A Comparative Study between Moravec and Harris Corner Detection of Noisy Images Using Adaptive Wavelet Thresholding Technique
This addresses the challenge of accurate corner detection in noisy images for image processing applications, but it is incremental as it combines existing techniques.
The paper tackled the problem of corner detection in noisy images by comparing Moravec and Harris methods after applying adaptive wavelet thresholding for denoising, resulting in improved feature extraction for object tracking and recognition.
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.