By Oscar Danielsson, Stefan Carlsson (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 desktop 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 computing device imaginative and prescient are addressed starting from algorithms that try to immediately comprehend the content material of pictures, optical equipment coupled with computational concepts that increase and increase pictures, and shooting and studying the world's geometry whereas getting ready the better point snapshot and form knowing. Novel gemometry suggestions, statistical studying tools,
and smooth 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 II
The original images are shown in ﬁgure 3(a), 3(d) and ﬁgures 3(b) and 3(e) show the saliency map generated by , in which the image graph model is designed with equal importance to all features, whether relevant or irrelevant . Figure 3(c)and 3(f) shows the saliency maps resulting from the proposed feature selection method used with . It can be seen that the saliency maps are more accurate in Figure 3(c) and 3(f) where the features are weighted according to their relevance. For example, consider the ﬁrst image in Figure 3(a).
The ground truth available for the dataset allows us to objectively compare various algorithms using precision, recall and F-measure. Precision is calculated as ratio of the total saliency in the saliency map captured inside the user annotated rectangle (sum of intensities inside user box) to the total saliency computed for the image (sum of intensities for full image). Recall is calculated as the ratio of the total saliency captured inside the user annotated window to the area of the user annotated window.
Cipolla, R. ) Shape, Contour, and Grouping 1999. LNCS, vol. 1681, p. 58. Springer, Heidelberg (1999) 14 O. Danielsson and S. Carlsson 8. : Contour-based learning for object detection. In: Proc. of the International Conference of Computer Vision (2005) 9. : A boundary-fragment-model for object detection. , Pinz, A. ) ECCV 2006. LNCS, vol. 3952, pp. 575–588. Springer, Heidelberg (2006) 10. : A short introduction to boosting. Journal of Japanese Society for Artiﬁcial Intelligence 14, 771–780 (1999) 11.