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Edge Detection Algorithm Comparison Essay

The use of technological innovations in production will increase the number of product and quality. With proposed method in this paper, it is aimed to improve the production process of glass which used almost every field. In this study, some of the popular edge detection algorithms (Roberts, Prewitt, Sobel, LoG and Canny) are used for the texture analysis process. It is aimed to determine glass surface defect with the applied of mentioned edge detection operators to same image. The results obtained from application are compared with the reference image and texture analysis performance of edge detection algorithms are evaluated. In this study the used material is glass and it is aimed to determine the glass surface defect such as scratch, crack and bubble with the use of edge detection operators. Glass is a difficult material to examine with cameras because glass has reflection and the transparency features. So, some improvement are applied in the image before edge detection algorithms are applied. Performed controlled experiments showed that LoG edge detection algorithm is better than other edge detection algorithms in determining texture analysis.


Edge Detector Comparison

Heath, M., Sarkar, S., Sanocki, T., and Bowyer, K

Initial Work

The initial phase of this work was presented at CVPR '96. A copy of the paper submitted for the proceedings is available.


Comparison of edge detectors: a methodology and initial study,
Heath, M., Sarkar, S., Sanocki, T., and Bowyer, K.
Computer Vision and Pattern Recognition '96, San Francisco, June 1996.


Most Recent Work

In a newer phase of work on the comparison of edge detection algorithms, five edge detectors were evaluated. A masters thesis detailing the desription of this comparison is available. This work was published in Patten Analysis and Machine Intelligence. The reference is:

M. Heath, S. Sarkar, T. Sanocki, and K.W. Bowyer, "A Robust Visual Method for Assessing the Relative Performance of Edge-Detection Algorithms" IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 19, No. 12, December 1997, pp. 1338-1359.

The method for evaluating the performance of the edge detectors used 28 images. These images, and the edges detected in them by five edge detection algorithms are being provided for others to use. They can be downloaded by anonymous ftp from

ftp://figment.csee.usf.edu/pub/Edge_Comparison/images/results

or can be viewed and saved using a web browser.

There are 12 edge maps for each image generated by each edge detector. Each of the edge images was generated with different input parameter values. All 12 edge images can be viewed by clicking on any of the images below. Alternativly, the best edge image for each algorithm can be viewed. The best edge images were determined in two different ways; by finding the best parameters to use for the set of images (fixed parameters) and by adapting the parameters to each individual image (adapted parameters). Tables of the files best_fixed and best_adaptive reference these images for easy access to them.


EDGE DETECTOR CODE


The source code for our implementation of the Canny edge detector is being made available (see below). Recently (on 9/24/99) we made the source code for our implementation of the Rothwell edge detector available. It is listed below. We are not redistributing implementations of the other edge detectors (sorry).

The source code for the implementation of the Canny edge detector compared in this study is available.
The source code for the implementation of the Rothwell edge detector compared in this study is available.


IMAGES


(Helpful hint: If your web browser allows you to run more than one copy of it at the same time, as Netscape does, then you can use that feature to compare edge images.)

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