Pattern Detection Using a New Haralick Quaternion Color Extraction Model and Support Vector Machine Classifier

There are many applications in image analysis where it is important to detect accurately patterns that include color and texture, e.g., plastic or concrete traffic barriers. This paper proposes a new method and extends a general machine vision approach for on-line pattern detection using color and textural information. Our proposed method includes the following steps: division of each image into sub-images, use of the Haralick and Binary Quaternion-Moment-Preserving methods to extract texture and color features, support vector machines for classification, and a post processing stage using clustering. The method was tested in two databases. The first one with three pattern types and the results yielded a detection rate of 96.4% with 14 false positives. The second database has nine pattern types and the results yielded a detection rate of 98.4% with 9 false positives. The results were compared advantageously with Haralick and BQMP methods separately.