By André Gagalowicz, Wilfried Philips
This e-book constitutes the refereed court cases of the 3rd foreign convention on desktop Vision/Computer snap shots collaboration innovations regarding photo analysis/synthesis techniques MIRAGE 2007, held in Rocquencourt, France, in March 2007.The fifty five revised complete papers provided have been rigorously reviewed and chosen from 198 submissions. The papers disguise foundational and methodological matters comparable to model-based imaging and research, image-based modeling and 3D reconstruction, info pushed animation, snapshot and video-based lighting fixtures and rendering, model-based imaginative and prescient methods, model-based indexing and database retrieval, model-based item monitoring in picture sequences, model-based photograph and form research, model-based video compression suggestions. program concerns addressed are human/computer interfaces, video-games and leisure undefined, media productions from and for motion pictures, proclaims and video games, post-production, machine animation, digital results, life like 3D simulation, digital prototyping, multimedia purposes, multimedia database type, digital and augmented fact, scientific and biomedical functions.
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Extra info for Computer Vision/Computer Graphics Collaboration Techniques: Third International Conference, Mirage 2007, Rocquencourt, France, March 28-30, 2007: Proceedings
The method dynamically determines the weighting scheme for diﬀerent feature vectors based on the prior knowledge. The experimental results show that the proposed method provides signiﬁcant improvements on retrieval eﬀectiveness of 3D shape search with several measures on a standard 3D database. Compared with two existing combination methods, the prior knowledge weighted combination technique has gained better retrieval eﬀectiveness. 1 Introduction With the rapid development of 3D scanner technology, graphic hardware, and the World-Wide Web, there has been an explosion in the number of 3D models available on the Internet.
The joint Bayesian reconstruction procedure can be summarized as: ( ) ( ) (θ , π ) = arg max(θ ,π ) P ( g / f n ) P ( f n / θ ,θ , π ) P (θ ,θ , π ) n = arg max (θ ,π ) P ( f / θ , θ , π ) P (θ , θ , π ) . f = arg max f P ( g / f ) P f / θ1 , θ2 , π1 P θ1 , θ2 , π1 n n n n 1 1 2 n 2 1 1 1 n 1 2 n 2 (17) 1 1 (18) We define function histogram (D) that returns the relevant histogram information of D histogram _ info . Because the gamma parameters can be totally determined by the information of histogram _ info which can also be totally determined by image n f , we can transform formula (18) into: (θ , π ) = arg max (θ ,π ) P ( histogram / θ , θ 1 2 , π 1 ) P (θ 1 , θ 2 , π 1 ) .
4. Retrieval results for a speciﬁc model with prior knowledge weighted combination and four single feature vectors. Irrelevant models retrieved are highlighted. Average Precesion vs. 9 1 Fig. 5. Average precision vs. recall ﬁgures for prior knowledge weighted combination, entropy impurity weighted combination, purity weighted combination and the best single feature vector Figure 6 compares the average second-tier among prior knowledge weighted technique, entropy impurity weighted method, purity weighted approach and the best single feature vector.
Computer Vision/Computer Graphics Collaboration Techniques: Third International Conference, Mirage 2007, Rocquencourt, France, March 28-30, 2007: Proceedings by André Gagalowicz, Wilfried Philips