Prewitt and Canny Methods on Inversion Image Edge Detection: An Evaluation

Abstract

The use of the edge detection method in an image will produce the edges of the image object. The goal is to mark the part that becomes the image's detail and fix the point of the blurred vision, which occurs because of an error or the effect of the image acquisition process. This study aims to see the combination of the Prewitt and Canny methods in detecting the edges of the inverted image. The image dataset used is a bonsai image consisting of 10 typical images, and ten bonsai images reversed based on the standard image dataset. The research dataset was obtained from the Caltech 101 website http://www.vision.caltech.edu/Image_Datasets/Caltech101/ with an image size of approximately 200 300 pixels. Based on the analysis of 10 experiments that have been carried out, the combination of the Prewitt and Canny methods can perform edge detection quite well with an average accuracy of 78.90% and an error rate of 21.10%. Thus it can be concluded that these methods combine to yield a reasonable level of precision, though the extent is very limited.