Negative Determinant of Hessian Features
Negative Determinant of Hessian Features
复制标题
Hessian 特征的负行列式
DOI：
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
S. Sridharan
中科院分区：
文献类型：
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作者：
Ruan Lakemond;C. Fookes;S. Sridharan
选择 Hessian 函数行列式局部最大值的局部图像特征提取器已被证明表现良好并被广泛使用。本文引入Hessian函数行列式的负局部极小值进行局部特征提取。对这些特征的属性和尺度空间行为进行了检查，发现它们对于特征提取来说是理想的。它展示了如何以可忽略的额外处理成本与现有的局部最大值方法一起实现这种新的特征类型。演示了仿射协变特征提取和亚像素精确角点提取的应用。实验结果表明，新的角点检测器比现有方法对图像模糊和噪声具有更强的鲁棒性。它对于更广泛的拐角几何形状也是准确的。通过将 Hessian 行列式的最小值与现有的尺度和形状自适应方法相结合来实现仿射协变特征提取器。该提取器可以与现有的 Hessian 最大值提取器一起实现，只需在初始提取阶段找到最小值和最大值即可。最小特征将对应数量增加了两到四倍。附加的最小特征与描述符空间中的最大特征非常不同，并且不会使匹配过程更加模糊。
Local image feature extractors that select local maxima of the determinant of Hessian function have been shown to perform well and are widely used. This paper introduces the negative local minima of the determinant of Hessian function for local feature extraction. The properties and scale-space behaviour of these features are examined and found to be desirable for feature extraction. It is shown how this new feature type can be implemented along with the existing local maxima approach at negligible extra processing cost. Applications to affine covariant feature extraction and sub-pixel precise corner extraction are demonstrated. Experimental results indicate that the new corner detector is more robust to image blur and noise than existing methods. It is also accurate for a broader range of corner geometries. An affine covariant feature extractor is implemented by combining the minima of the determinant of Hessian with existing scale and shape adaptation methods. This extractor can be implemented along side the existing Hessian maxima extractor simply by finding both minima and maxima during the initial extraction stage. The minima features increase the number of correspondences by two to four fold. The additional minima features are very distinct from the maxima features in descriptor space and do not make the matching process more ambiguous.