mosse¶
-
menpofit.math.
mosse
(X, y, l=0.01, boundary='constant', crop_filter=True)[source]¶ Minimum Output Sum of Squared Errors (MOSSE) filter.
- Parameters
X (
(n_images, n_channels, image_h, image_w)
ndarray) – The training images.y (
(1, response_h, response_w)
ndarray) – The desired response.l (float, optional) – Regularization parameter.
boundary (
{'constant', 'symmetric'}
, optional) – Determines how the image is padded.crop_filter (bool, optional) – If
True
, the shape of the MOSSE filter is the same as the shape of the desired response. IfFalse
, the filter’s shape is equal to:X[0].shape + y.shape - 1
- Returns
f (
(1, response_h, response_w)
ndarray) – Minimum Output Sum od Squared Errors (MOSSE) filter associated to the training images.sXY (
(N,)
ndarray) – The auto-correlation array, whereN = (image_h+response_h-1) * (image_w+response_w-1) * n_channels
.sXX (
(N, N)
ndarray) – The cross-correlation array, whereN = (image_h+response_h-1) * (image_w+response_w-1) * n_channels
.
References
- 1
D. S. Bolme, J. R. Beveridge, B. A. Draper, and Y. M. Lui. “Visual Object Tracking using Adaptive Correlation Filters”, IEEE Proceedings of International Conference on Computer Vision and Pattern Recognition (CVPR), 2010.