Robustness of Point Feature Detection
| Autor: | Reinhard Klette, Zijiang Song |
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| Rok vydání: | 2013 |
| Předmět: |
Brightness
Maximally stable extremal regions Computer science business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Scale-invariant feature transform Binary number Pattern recognition RANSAC Invariant (physics) Robustness (computer science) Computer Science::Computer Vision and Pattern Recognition Scalability Artificial intelligence business |
| Zdroj: | Computer Analysis of Images and Patterns ISBN: 9783642402456 CAIP (2) |
| Popis: | This paper evaluates 2D feature detection methods with respect to invariance and efficiency properties. The studied feature detection methods are as follows: Speeded Up Robust Features, Scale Invariant Feature Transform, Binary Robust Invariant Scalable Keypoints, Oriented Binary Robust Independent Elementary Features, Features from Accelerated Segment Test, Maximally Stable Extremal Regions, Binary Robust Independent Elementary Features, and Fast Retina Keypoint. A long video sequence of traffic scenes is used for testing these feature detection methods. A brute-force matcher and Random Sample Consensus are used in order to analyse how robust these feature detection methods are with respect to scale, rotation, blurring, or brightness changes. After identifying matches in subsequent frames, RANSAC is used for removing inconsistent matches; remaining matches are taken as correct matches. This is the essence of our proposed evaluation technique. All the experiments use a proposed repeatability measure, defined as the ratio of the numbers of correct matches, and of all keypoints. |
| Databáze: | OpenAIRE |
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