By Xiaofei Di, Hong Chang, Xilin Chen (auth.), Kyoung Mu Lee, Yasuyuki Matsushita, James M. Rehg, Zhanyi Hu (eds.)
The four-volume set LNCS 7724--7727 constitutes the completely refereed post-conference complaints of the eleventh Asian convention on desktop imaginative and prescient, ACCV 2012, held in Daejeon, Korea, in November 2012. the full of 226 contributions provided in those volumes used to be conscientiously reviewed and chosen from 869 submissions. The papers are geared up in topical sections on item detection, studying and matching; item reputation; characteristic, illustration, and popularity; segmentation, grouping, and type; photo illustration; photo and video retrieval and clinical photo research; face and gesture research and popularity; optical movement and monitoring; movement, monitoring, and computational images; video research and motion attractiveness; form reconstruction and optimization; form from X and photometry; functions of desktop imaginative and prescient; low-level imaginative and prescient and functions of laptop vision.
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Additional info for Computer Vision – ACCV 2012: 11th Asian Conference on Computer Vision, Daejeon, Korea, November 5-9, 2012, Revised Selected Papers, Part II
In ), often resulting in local optima. , [7–10]) allows ﬁnding the global optimum. M. Lee et al. ): ACCV 2012, Part II, LNCS 7725, pp. 25–37, 2013. c Springer-Verlag Berlin Heidelberg 2013 26 W. Liao, S. W¨ orz, and K. Rohr Fig. 1. Classical minimal path methods: For a structure with high curvature and gaps (left), a static speed is used (middle), and the result is usually a short cut (right). The yellow points xs and xe denote the start and end point, respectively.
Then, a graph is constructed and the minimum cut is computed as in . 5 pixels (Fig. 6a). For σn < 30, all three approaches have very similar T P , while Li/Yezzi FM has a slightly higher F P . For σn ≥ 30, the performance of Li/Yezzi FM decreases strongly, while the performance of FM-V and our approach decreases only slowly, and with σn = 55 (see Fig. 5d), they both still achieve a T P of about 83%, while F P remains low. For structures with high curvature and small radius, the results are quite diﬀerent.
The procedure is graphically detailed in Figure 1 and in Algorithm 1. Algorithm 1. Iterative Foreground/Background Modeling 1: Initialize Xt , Xr ← (objectness & saliency), ∀t ∈ T , ∀r ∈ R. 2: repeat 3: Train SV Mk ← Xt , ∀Vk ∈ V 4: Train SV Ml ← Xr , ∀Vl ∈ V 5: Query regressor k: Pˆ (Ht ) ← SV Mk (Ht ), ∀t ∈ T 6: Query regressor l: Pˆ (LBPr ) ← SV Ml (LBPr ), ∀r ∈ R 7: X∗ ← arg maxX P (X|Y) ∝ P (Y|X)P (X) 8: Update labels Xt , Xr ← X∗ 9: until stop criteria / limit num. iterations 4 Modeling the Prior An iterative procedure such as the one described in the previous section does not work in practice if we assume a uniform prior distribution.