By Sujung Kim, Hee-Dong Kim, Wook-Joong Kim, Seong-Dae Kim (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 machine 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 to immediately comprehend the content material of pictures, optical equipment coupled with computational ideas that increase and increase photos, and shooting and reading the world's geometry whereas getting ready the better point picture and form figuring out. Novel gemometry thoughts, statistical studying tools, and sleek algebraic methods are handled as well.

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Additional info for Computer Vision – ACCV 2010: 10th Asian Conference on Computer Vision, Queenstown, New Zealand, November 8-12, 2010, Revised Selected Papers, Part IV

Example text

The threshold is determined empirically from experiments. In our experiments, we found that the case of outliers is not serious. This can be observed by the variance of the furthest nearest neighbour distances for active learning rounds which are found to be small. Hence, the sensitivity of the performance on the threshold is minor. In principle though, note that by the very nature of the nearest neighbour rule, outliers will not be selected. Active Learning with the FNN Criterion for Facial Age Estimation 19 Fig.

G. digital signage in which the statistics of peoples’ age ranges can provide reference to manage the advertising. Another interesting application is for interactive games. g. it can automatically provide easy games such as popular crossword puzzles or board games when an elderly person is detected and card games, video games and computer games when a teenager is detected. One of the difficulties in age estimation using face images is that the training database is highly incomplete. In order to collect photos of a person, the subject could be required to scan his/her photos captured during the past at his/her different age.

The difference between an active learner and a passive learner is in the querying component, which brings us to the criterion for choosing the next unlabeled instance to query. All active learning scenarios involve evaluating the probability/believe of informativeness of unlabeled instances, which can either be generated de novo or sampled from a given distribution. There Active Learning with the FNN Criterion for Facial Age Estimation 17 have been many ways of formulating such query strategies described in the literature.

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