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Enhanced low-complexity pruning for corner detection

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Abstract

Low-complexity corner detection is essential for many real-time computer vision applications that need to be executed on low-cost/low-power embedded platforms such as robots. The widely used Shi–Tomasi and Harris corner detectors become prohibitive in such platforms due to their high computational complexity, which is attributed to the need to apply a complex corner measure on the entire image. In this paper, we introduce a novel and computationally efficient technique to accelerate the Shi–Tomasi and Harris corner detectors. The proposed technique consists of two steps. In the first step, the complex corner measure is replaced with simple approximations to quickly prune away non-corners. In the second step, the complex corner measure is applied to a small corner candidate set obtained after pruning. Evaluations using standard image benchmarks show that the proposed pruning technique achieves up to 75 % speedup on the Nios-II platform, while yielding corners with comparable or better accuracy than the conventional Shi–Tomasi and Harris detectors.

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Authors and Affiliations

  1. School of Computer Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore

    Nirmala Ramakrishnan, Meiqing Wu, Siew-Kei Lam & Thambipillai Srikanthan

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  1. Nirmala Ramakrishnan
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  2. Meiqing Wu
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  3. Siew-Kei Lam
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  4. Thambipillai Srikanthan
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Correspondence to Nirmala Ramakrishnan.

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Ramakrishnan, N., Wu, M., Lam, SK. et al. Enhanced low-complexity pruning for corner detection. J Real-Time Image Proc 12, 197–213 (2016). https://doi.org/10.1007/s11554-014-0396-z

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