Traditional methods in Edge, Corner and Boundary detection
It addresses the problem of feature extraction in applications like medical image analysis and autonomous vehicles, but it is incremental as it reviews existing methods without introducing new ones.
This review paper examines traditional methods for edge, corner, and boundary detection, comparing their results stage-wise and discussing the importance of image preprocessing to minimize noise, using real-world images to validate performance and limitations.
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.