By David Fleet, Tomas Pajdla, Bernt Schiele, Tinne Tuytelaars
The seven-volume set comprising LNCS volumes 8689-8695 constitutes the refereed court cases of the thirteenth eu convention on machine imaginative and prescient, ECCV 2014, held in Zurich, Switzerland, in September 2014. The 363 revised papers provided have been conscientiously reviewed and chosen from 1444 submissions. The papers are equipped in topical sections on monitoring and job reputation; popularity; studying and inference; constitution from movement and have matching; computational images and low-level imaginative and prescient; imaginative and prescient; segmentation and saliency; context and 3D scenes; movement and 3D scene research; and poster sessions.
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Additional resources for Computer Vision – ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V
The results do not depend on the PnP method. Comparing relative method accuracy for ASPnP as baseline (table 5) we observe: • SP (D) is increasingly helpful with growing outlier contamination (ρ3,4,5 ). Similar to results on synthetic data, SP+C (E) does not help much. • In general, all LS variants (I,J,K,L) oﬀer signiﬁcantly better results than any other method. In contrast to our experiments on synthetic data, for real data Video Registration to SfM Models 13 LSWS (B) and, as a consequence LS (I) / LS+CP (J) have similar scores in positional accuracy to SP+LS (K) / SP+LS+CP (L).
Schmid, C. ) ECCV 2012, Part I. LNCS, vol. 7572, pp. 15–29. Springer, Heidelberg (2012) 26. : Location Recognition using Prioritized Feature Matching. , Paragios, N. ) ECCV 2010, Part II. LNCS, vol. 6312, pp. 791–804. Springer, Heidelberg (2010) 27. : Real-time image-based 6-dof localization in large-scale environments. In: CVPR (2012) 28. : Distinctive Image Features from Scale-Invariant Keypoints. IJCV (2004) 29. : Live dense reconstruction with a single moving camera. In: CVPR (2010) 30. : DTAM: Dense tracking and mapping in real-time.
4 Conclusions This paper has presented MultiAgg, a generic cost volume aggregation framework for eﬀectively addressing pixel-labeling problems. The key contribution is the idea of adaptively fusing the cost aggregation results from multi-resolutions in a coarse-to-ﬁne manner. Experimental results have shown that MultiAgg produces more accurate results than current state-of-the-art methods both visually and quantitatively. In addition to its eﬀectiveness, another advantage is its computational eﬃciency which is inherited from the fast cost aggregation methods.