By Erik Jonsson
ISBN-10: 9173939889
ISBN-13: 9789173939881
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Additional resources for Channel-coded feature maps for computer vision and machine learning
Sample text
11), which to summarize gives us K[p, ·] ∗ cmp [p, ·] . 13) p In words, in order to convert from a monopiece encoding to a CCFM, we must perform one multidimensional convolution in each p-layer, and then sum over all layers. 7) that the filter kernel K[p, ·] is separable. 13) can be computed as a sequence of one-dimensional convolutions with kernel Kd [p[d], ·] in dimension d. The complete algorithm is summarized as Alg. 1. 5 Generalized Notation In the next section, I describe how the derivatives are computed in the piecewise polynomial framework.
The corresponding λn from the MEM tends towards −∞, leading to numerical problems. To stabilize the solution in these cases, a small background DC level was introduced ( -regularization). 1 Qualitative Behavior In Fig. 8, the qualitative behavior of the MEM and MN reconstructions is examined. The feature vector d was calculated for some different distributions with the channel centers located at the integers. The two Gaussians (c-d) were reconstructed almost perfectly using MEM, but rather poorly using MN.
On the other hand, in frame 5-8, the occluding object has a color similar to the tracked object. The tracking finally breaks down in frame 8. 4: Example of tracking using CCFMs. See the text for details. where we take advantage of the piecewiseness of the B-spline basis functions, almost get lost in a haze of multidimensional convolutions, but eventually manage to find our way. The motivation behind this chapter was to seek an efficient method for computing channel-coded feature maps, which turned out to be more tricky than was first anticipated.
Channel-coded feature maps for computer vision and machine learning by Erik Jonsson
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