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 laptop imaginative and prescient, ACCV 2014, held in Singapore, Singapore, in November 2014.
The overall of 227 contributions offered in those volumes was once conscientiously reviewed and chosen from 814 submissions. The papers are prepared in topical sections on acceptance; 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
More than 60) to ﬁt the local distribution. To validate this observation, we estimated the LAM models for all the color images in a published image dataset BSDS300  with the threshold of Lth = 64 and examined the distribution of necessary number of models. 9 % pixels were fulﬁlled the constraint in Sect. 1. Results are illustrated in Fig. , r = 5). 01×I3 for both cases. The majority of images generally required at most 50 models to meet the criterion. What’s more, the smaller the window size is, the fewer number of necessary models are required, which veriﬁes the discussions in Sect.
These scenarios would be unrealistic without extremely accurate systems which could even outperform highly trained human experts. Like any other developments of machine learning systems, the ﬁrst important step is the preparation of training and testing samples. Though posed or normal expression databases are popular and highly accessible, subtle expressions databases are not easy to build due to complex collecting procedures. Video samples need to be recorded spontaneously while subjects are told to conceal their emotions after viewing short video clips.
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.