Affiliations: [a]
Key Laboratory on Adaptive Optics and Institute of Optics and Electronics Chinese Academy of Sciences, Chengdu, China
| [b] School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| [c]
University of Chinese Academy of Sciences, Beijing, China
Correspondence:
[*]
Corresponding author. Ping Yang, Key Laboratory on Adaptive Optics and Institute of Optics and Electronics Chinese Academy of Sciences, Chengdu 610209, China. E-mail: [email protected].
Abstract: The tough challenges of object recognition in long-distance scene involves contour shape deformation invariant features construction. In this work, an effective contour shape descriptor integrating critical points structure and Scale-invariant Heat Kernel Signature (SI-HKS) is proposed for long-distance object recognition. We firstly propose a general feature fusion model. Then, we capture the object contour structure feature with Critical-points Inner-distance Shape Context (CP-IDSC). Meanwhile, we pull-in the SI-HKS for capturing the local deformation-invariant properties of 2D shape. Based on the integration of the above two feature descriptors, the fusion descriptor is compacted by mapping into a low dimensional subspace using the bags-of-features, allowing for an efficient Bayesian classifier recognition. The extensive experiments on synthetic turbulence-degraded shapes and real-life infrared image show that the proposed method outperformed other compared approaches in terms of the recognition precision and robustness.
