Circular-harmonic spectra are a compact representation of local image features in two dimensions. It is well known that the computational complexity of such transforms is greatly reduced when polar separability is exploited in steerable filter-banks. Further simplifications are possible when Cartesian separability is incorporated using the radial apodization (i.e. weight, window, or taper) described here, as a consequence of the Laguerre/Hermite correspondence over polar/Cartesian coordinates. The chosen form also mitigates undesirable discretization artefacts due to angular aliasing. The local angular spectrum at each pixel is deployed in a novel test-statistic to detect and characterize corners of arbitrary angle and orientation (i.e. wedges). The test-statistic considers uncertainty due to finite sampling and clutter/noise. The possible utility of this detector, and circular-harmonic spectra for the description of simple features in general, is illustrated using real data from an overhead electro-optic sensor. Monte-Carlo simulations are also performed to quantify performance relative to other simple corner detectors. Possible computer realizations for a small remote-sensing platform are discussed.

圆谐谱是二维局部图像特征的紧凑表示。众所周知，当在可控滤波器组中利用极性可分离性时，这种变换的计算复杂度将大大降低。当使用此处描述的径向切趾法（即，重量，窗口或锥度）合并笛卡尔可分离性时，由于极坐标/笛卡尔坐标上的拉盖尔/赫尔米特对应关系，可能会进一步简化。所选择的形式还减轻了由于角度混叠而引起的不期望的离散伪像。将每个像素的局部角光谱部署在一种新颖的测试统计量中，以检测和表征任意角度和方向的角（即，楔形）。检验统计量考虑了由于有限采样和杂波/噪声引起的不确定性。通常使用来自架空电光传感器的实际数据来说明此探测器的可能用途，以及用于描述简单特征的圆谐波频谱。相对于其他简单的角检测器，还执行了蒙特卡洛模拟以量化性能。讨论了小型遥感平台的可能计算机实现。