Pattern Detection Using a New Haralick Quaternion Color Extraction Model and Support Vector Machine Classifier
| Autor: | Daniel A. Schulz, Jacob A. Saravia, Carlos M. Aravena, Carlos F. Navarro, Claudio A. Perez |
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| Rok vydání: | 2013 |
| Předmět: |
Contextual image classification
Color image business.industry Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition Object detection Support vector machine Image texture Feature (computer vision) Computer vision Artificial intelligence business Feature detection (computer vision) Mathematics |
| Zdroj: | SMC |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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