By Timo Tossavainen (auth.), Ron Kimmel, Reinhard Klette, Akihiro Sugimoto (eds.)
The four-volume set LNCS 6492-6495 constitutes the completely refereed post-proceedings of the tenth Asian convention on laptop imaginative and prescient, ACCV 2009, held in Queenstown, New Zealand in November 2010.
All jointly the 4 volumes current 206 revised papers selected
from a complete of 739 Submissions. All present concerns in laptop imaginative and prescient are addressed starting from algorithms that try and instantly comprehend the content material of pictures, optical tools coupled with computational suggestions that improve and increase photographs, and taking pictures and examining the world's geometry whereas getting ready the better point photo and form realizing. Novel gemometry suggestions, statistical studying equipment, and sleek algebraic systems are handled to boot.
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Additional resources for Computer Vision – ACCV 2010: 10th Asian Conference on Computer Vision, Queenstown, New Zealand, November 8-12, 2010, Revised Selected Papers, Part III
3 Detecting the Horizon Note that, estimating the depth map needs detection of the horizon. Additionally, surface objects are usually around the horizon due to the perspective projection. Therefore, this subsection mainly discuss how to detect the horizon. To obtain robust detection, we apply the wavelet transforms based singularity analysis techniques. The wavelet we used is the orthogonal cubic dyadic spline wavelet transforms, whose coeﬃcients for decomposition are given in Table 1. The advantage of the method is that it can detect various types of edges through multi-resolution analysis .
State of the art comparison. Misclassiﬁcation rates on the Hopkins155 database. In brackets the number of sequences for each type of video. NA stands for value not available. 03 code ), LSA 4N (results taken from ), MSC+NSI (results taken from ), ALC (results taken from ), and SSC (results taken from ). Table 1 shows the average misclassiﬁcation rates and the standard deviations of each method. The misclassiﬁcation rates are presented for each type of video sequence (checkboards, articulated and traﬃc).
As a consequence of this rigidity, if the estimated rank is r = 6 all of the PAs have an aﬃnity value that falls before the inﬂection point. Opposite cases are when r = 10 and r = 16, in which most of the PAs have an aﬃnity value after the inﬂection point. Therefore, the cos2 function, as well as any other rigid function, is very sensitive to the rank estimation. The aﬃnity measure that we propose is able to adapt itself to the distribution of the PAs in Θir , so that it minimizes the negative eﬀects of a wrong rank estimation and it emphasizes the diﬀerence between similar and diﬀerent subspaces.
Computer Vision – ACCV 2010: 10th Asian Conference on Computer Vision, Queenstown, New Zealand, November 8-12, 2010, Revised Selected Papers, Part III by Timo Tossavainen (auth.), Ron Kimmel, Reinhard Klette, Akihiro Sugimoto (eds.)