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Read e-book online Computer Aided Verification: 9th International Conference, PDF

By F. Erich Marschner (auth.), Orna Grumberg (eds.)

ISBN-10: 3540631666

ISBN-13: 9783540631668

This ebook constitutes the strictly refereed complaints of the ninth overseas convention on computing device Aided Verification, CAV '97, held in Haifa, Israel, in June 1997.
The quantity provides 34 revised complete papers chosen from a complete of eighty four submissions. additionally incorporated are 7 invited contributions in addition to 12 software descriptions. the amount is devoted to the speculation and perform of machine aided formal tools for software program and verification, with an emphasis on verification instruments and algorithms and the options wanted for his or her implementation. The publication is a distinct list documenting the hot growth within the area.

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Read Online or Download Computer Aided Verification: 9th International Conference, CAV'97 Haifa, Israel, June 22–25, 1997 Proceedings PDF

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Additional resources for Computer Aided Verification: 9th International Conference, CAV'97 Haifa, Israel, June 22–25, 1997 Proceedings

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.

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Computer Aided Verification: 9th International Conference, CAV'97 Haifa, Israel, June 22–25, 1997 Proceedings by F. Erich Marschner (auth.), Orna Grumberg (eds.)


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