What's the best approach to recognize patterns in data, and what's the best way to learn more on the topic?

Question

A developer I am working with is developing a program that analyzes images of pavement to find cracks in the pavement. For every crack his program finds, it produces an entry in a file that tells me which pixels make up that particular crack. There are two problems with his software though:

1) It produces several false positives

2) If he finds a crack, he only finds small sections of it and denotes those sections as being separate cracks.

My job is to write software that will read this data, analyze it, and tell the difference between false-positives and actual cracks. I also need to determine how to group together all the small sections of a crack as one.

I have tried various ways of filtering the data to eliminate false-positives, and have been using neural networks to a limited degree of success to group cracks together. I understand there will be error, but as of now, there is just too much error. Does anyone have any insight for a non-AI expert as to the best way to accomplish my task or learn more about it? What kinds of books should I read, or what kind of classes should I take?

EDIT My question is more about how to notice patterns in my coworker's data and identify those patterns as actual cracks. It's the higher-level logic that I'm concerned with, not so much the low-level logic.

EDIT In all actuality, it would take AT LEAST 20 sample images to give an accurate representation of the data I'm working with. It varies a lot. But I do have a sample here, here, and here. These images have already been processed by my coworker's process. The red, blue, and green data is what I have to classify (red stands for dark crack, blue stands for light crack, and green stands for a wide/sealed crack).

Answer 1

In addition to the useful comments about image processing, it also sounds like you're dealing with a clustering problem.

Clustering algorithms come from the machine learning literature, specifically unsupervised learning. As the name implies, the basic idea is to try to identify natural clusters of data points within some large set of data.

For example, the picture below shows how a clustering algorithm might group a bunch of points into 7 clusters (indicated by circles and color):

_{(source: natekohl.net)}

In your case, a clustering algorithm would attempt to repeatedly merge small cracks to form larger cracks, until some stopping criteria is met. The end result would be a smaller set of joined cracks. Of course, cracks are a little different than two-dimensional points -- part of the trick in getting a clustering algorithm to work here will be defining a useful distance metric between two cracks.

Popular clustering algorithms include k-means clustering (demo) and hierarchical clustering. That second link also has a nice step-by-step explanation of how k-means works.

EDIT: This paper by some engineers at Phillips looks relevant to what you're trying to do:

• Chenn-Jung Huang, Chua-Chin Wang, Chi-Feng Wu, "Image Processing Techniques for Wafer Defect Cluster Identification," IEEE Design and Test of Computers, vol. 19, no. 2, pp. 44-48, March/April, 2002.

They're doing a visual inspection for defects on silicon wafers, and use a median filter to remove noise before using a nearest-neighbor clustering algorithm to detect the defects.

Here are some related papers/books that they cite that might be useful:

• M. Taubenlatt and J. Batchelder, “Patterned Wafer Inspection Using Spatial Filtering for Cluster Environment,” Applied Optics, vol. 31, no. 17, June 1992, pp. 3354-3362.
• F.-L. Chen and S.-F. Liu, “A Neural-Network Approach to Recognize Defect Spatial Pattern in Semiconductor Fabrication.” IEEE Trans. Semiconductor Manufacturing, vol. 13, no. 3, Aug. 2000, pp. 366-373.
• G. Earl, R. Johnsonbaugh, and S. Jost, Pattern Recognition and Image Analysis, Prentice Hall, Upper Saddle River, N.J., 1996.