Traditional methods in Edge, Corner and Boundary detection

TL;DR

This review paper comprehensively discusses traditional edge, corner, and boundary detection methods, highlighting their applications, performance comparisons, and the importance of preprocessing in real-world scenarios.

Contribution

It provides a detailed comparison of traditional detection techniques and emphasizes the role of preprocessing in enhancing detector performance.

Findings

Edge detectors are crucial in medical imaging and autonomous vehicles.

Preprocessing significantly improves detection accuracy.

Real-world images reveal limitations of traditional methods.

Abstract

This is a review paper of traditional approaches for edge, corner, and boundary detection methods. There are many real-world applications of edge, corner, and boundary detection methods. For instance, in medical image analysis, edge detectors are used to extract the features from the given image. In modern innovations like autonomous vehicles, edge detection and segmentation are the most crucial things. If we want to detect motion or track video, corner detectors help. I tried to compare the results of detectors stage-wise wherever it is possible and also discussed the importance of image prepossessing to minimise the noise. Real-world images are used to validate detector performance and limitations.