Deformation invariant image matching based on dissimilarity of spatial features
| Autor: | Sophia Jamila Zahra, Amir Hossein Ashtari, Jan Nordin, Seyed Mostafa Mousavi Kahaki |
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| Rok vydání: | 2016 |
| Předmět: |
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Image matching business.industry Cognitive Neuroscience 0211 other engineering and technologies Image registration Pattern recognition 02 engineering and technology Computer Science Applications Correlation Artificial Intelligence 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Invariant (mathematics) business Eigenvalues and eigenvectors Feature matching 021101 geological & geomatics engineering Mathematics |
| Zdroj: | Neurocomputing. 175:1009-1018 |
| ISSN: | 0925-2312 |
| Popis: | In this paper, a new deformation invariant image matching method, known as spatial orientation feature matching (SOFM), is presented. A new similarity value, which measures the similarity of the signal through the path based on triple-wise signal eigenvector correlation, is proposed. The proposed method extracts similarity feature values by relying on the distinct path between two specific interest points and following the alternation of the signal while traversing the path. Because these similarity values of the path are deformation invariant, the proposed method supports various types of transformation in the original image, such as scale, translation, rotation, intensity noises and occlusion. Moreover, the triple-wise similarity scores are accumulated in a 2-D similarity space; thus, robust matched correspondence points are obtained using cumulative similarity space. SOFM was compared to the most recent related methods using corner correspondence (CC) and precision-recall evaluation metrics. The findings confirmed that SOFM provides higher correspondence ratios, and the results indicate that it outperforms currently utilized methods in terms of accuracy and generalization. |
| Databáze: | OpenAIRE |
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