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请问一下在哪里可以找到feature descriptor的解释?看上去与正态分布的函数很相近但是看不太明白。谢谢。
fx = np.arange(-np.pi, np.pi, 3 * np.pi / 180) features = np.zeros((pts_num, len(fx))) for i in range(k): sigma = 2.5 * np.exp(-rho_feature[:, i] * 100) + .04 tmp = np.exp(-np.subtract.outer(theta_feature[:, i], fx) ** 2 / 2 / sigma[:, np.newaxis] ** 2) tmp = tmp * (vol_feature[:, i] * rho_feature[:, i] ** 2 / sigma)[:, np.newaxis] features += tmp features = features / np.sqrt(np.sum(features ** 2, axis=1)).reshape((pts_num, 1))
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请问一下在哪里可以找到feature descriptor的解释?看上去与正态分布的函数很相近但是看不太明白。谢谢。
The text was updated successfully, but these errors were encountered: