By Daniel Cremers, Ian Reid, Hideo Saito, Ming-Hsuan Yang
The five-volume set LNCS 9003--9007 constitutes the completely refereed post-conference complaints of the twelfth Asian convention on machine imaginative and prescient, ACCV 2014, held in Singapore, Singapore, in November 2014.
The overall of 227 contributions provided in those volumes was once rigorously reviewed and chosen from 814 submissions. The papers are prepared in topical sections on reputation; 3D imaginative and prescient; low-level imaginative and prescient and contours; segmentation; face and gesture, monitoring; stereo, physics, video and occasions; and poster periods 1-3.
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Extra resources for Computer Vision -- ACCV 2014: 12th Asian Conference on Computer Vision, Singapore, Singapore, November 1-5, 2014, Revised Selected Papers, Part IV
Pattern Anal. Mach. Intell. 35, 185–207 (2013) 3. : Graph-based visual saliency. In: Proceedings of Neural Information Processing Systems (NIPS) (2006) 4. : A coherent computational approach to model the bottom-up visual attention. IEEE Trans. PAMI 28, 802– 817 (2006) 5. : Saliency from hierarchical adaptation through decorrelation and variance normalization. Image Vis. Comput. 30, 51–64 (2012) 6. : A model for saliency-based visual attention for rapid scene analysis. IEEE Trans. PAMI 20, 1254–1259 (1998) 7.
Intell. 34, 1704–1716 (2012) 32 O. Le Meur and Z. Liu 22. : A spatiotemporal saliency model for video surveillance. Cogn. Comput. 3, 241–263 (2011) 23. : Superpixel-based spatiotemporal saliency detection. IEEE Trans. Circuits Syst. Video Technol. 24, 1522–1540 (2014) 24. : Exploring the role of salient distracting clinical features in the emergence of diagnostic errors and the mechanisms through which reﬂection counteracts mistakes. BMJ Qual. Saf. 21, 295–300 (2012) 25. : Implementation of road traﬃc signs detection based on saliency map model.
Given an input image, the ﬁrst thing to do is to retrieve its nearest image from the dataset. This problem can be eﬃciently handled by using the VLAD (Vector of Locally Aggregated Descriptors) method introduced by J´egou et al. . VLAD is an image descriptor which has been designed to be very low dimensional: only 16 bytes are required per image. The computation of VLAD descriptor is based on the vector quantizing a locally invariant descriptor such as SIFT. From the weights of the most similar image, the aggregated saliency map is computed.