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<p>In the realm of image and vision computing, shape recognition remains challenging due to intra-class variations that can degrade performance. To address this, we propose an enhanced shape recognition method leveraging dual constraint reduction and adaptive orthogonal wavelet transform (ADCR). Firstly, we refine shape contours by reducing salient points to strong feature points (bone points), mitigating the impact of shape deformation. Secondly, we employ an adaptive orthogonal wavelet transform to ensure scale and mirror invariance, significantly accelerating recognition speed. Thirdly, we streamline the feature structure after wavelet transformation, retaining only minimum, mean, and maximum eigenvectors for efficient shape representation. Experimental results on multiple datasets demonstrate the effectiveness of our method, achieving a MAP score of 79.2% on the ICL dataset with an average recognition time of 26.9 ms, outperforming similar methods by over 200 times in efficiency. Our ADCR method significantly enhances shape recognition capabilities.</p>

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Fast shape recognition through dual constraint reduction and adaptive orthogonal wavelet transform

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月2日

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  • Zekun Li,
  • Hock Soon Seah,
  • Shicheng Jia,
  • Dongsheng Wang,
  • Dingheng Wang

摘要

In the realm of image and vision computing, shape recognition remains challenging due to intra-class variations that can degrade performance. To address this, we propose an enhanced shape recognition method leveraging dual constraint reduction and adaptive orthogonal wavelet transform (ADCR). Firstly, we refine shape contours by reducing salient points to strong feature points (bone points), mitigating the impact of shape deformation. Secondly, we employ an adaptive orthogonal wavelet transform to ensure scale and mirror invariance, significantly accelerating recognition speed. Thirdly, we streamline the feature structure after wavelet transformation, retaining only minimum, mean, and maximum eigenvectors for efficient shape representation. Experimental results on multiple datasets demonstrate the effectiveness of our method, achieving a MAP score of 79.2% on the ICL dataset with an average recognition time of 26.9 ms, outperforming similar methods by over 200 times in efficiency. Our ADCR method significantly enhances shape recognition capabilities.