Abstract
In traditional Curvature Scale Space (CSS) corner detection algorithms, Gaussian filter is used to remove noise existing in canny edge detection results. Unfortunately, Gaussian filter will reduce the precision of corner detection. In this paper, a new method of robust corner detection based on bilateral filter in direct curvature scale space is proposed. In this method, bilateral filter is adopted to reduce image noise and keep image details. Instead of curvature scale space, direct curvature scale space is applied to reduce the computational complexity of the algorithm. Meanwhile, multi-scale curvature product with certain threshold is used to strengthen the corner detection. Experimental results show that our proposed method can improve the performance of corner detection in both accuracy and efficiency, and which can also gain more stable corners at the same time.
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© 2015 Springer International Publishing Switzerland
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Liao, B., Xu, J., Sun, H., Chen, H. (2015). Robust Corner Detection Based on Bilateral Filter in Direct Curvature Scale Space. In: Tan, Y., Shi, Y., Buarque, F., Gelbukh, A., Das, S., Engelbrecht, A. (eds) Advances in Swarm and Computational Intelligence. ICSI 2015. Lecture Notes in Computer Science(), vol 9142. Springer, Cham. https://doi.org/10.1007/978-3-319-20469-7_38
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DOI: https://doi.org/10.1007/978-3-319-20469-7_38
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