Comparative Study Of Image Edge Detection Algorithms
This is an incremental study for computer vision researchers, providing practical insights into algorithm selection for edge detection tasks.
The study compared two edge detection algorithms to determine which performs better under various conditions, focusing on their effectiveness in identifying object boundaries in images.
Since edge detection is in the forefront of image processing for object detection, it is crucial to have a good understanding of edge detection algorithms. The reason for this is that edges form the outline of an object. An edge is the boundary between an object and the background, and indicates the boundary between overlapping objects. This means that if the edges in an image can be identified accurately, all of the objects can be located and basic properties such as area, perimeter, and shape can be measured. Since computer vision involves the identification and classification of objects in an image, edge detection is an essential tool. We tested two edge detectors that use different methods for detecting edges and compared their results under a variety of situations to determine which detector was preferable under different sets of conditions.