Download Computer Vision -- ACCV 2014: 12th Asian Conference on by Daniel Cremers, Ian Reid, Hideo Saito, Ming-Hsuan Yang PDF

April 4, 2017 | Storage Retrieval | By admin | 0 Comments

By Daniel Cremers, Ian Reid, Hideo Saito, Ming-Hsuan Yang

The five-volume set LNCS 9003--9007 constitutes the completely refereed post-conference lawsuits of the twelfth Asian convention on laptop imaginative and prescient, ACCV 2014, held in Singapore, Singapore, in November 2014.
The overall of 227 contributions awarded in those volumes was once rigorously reviewed and chosen from 814 submissions. The papers are geared up in topical sections on attractiveness; 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.

Show description

Read Online or Download Computer Vision -- ACCV 2014: 12th Asian Conference on Computer Vision, Singapore, Singapore, November 1-5, 2014, Revised Selected Papers, Part IV PDF

Best storage & retrieval books

Networked Digital Technologies, Part I: Second International Conference, NDT 2010, Prague, Czech Republic (Communications in Computer and Information Science)

This ebook constitutes the lawsuits of the second one foreign convention on Networked electronic applied sciences, held in Prague, Czech Republic, in July 2010.

The Cyberspace Handbook (Media Practice)

The our on-line world instruction manual is a entire consultant to all points of recent media, details applied sciences and the web. It supplies an summary of the industrial, political, social and cultural contexts of our on-line world, and gives sensible recommendation on utilizing new applied sciences for study, verbal exchange and book.

Multimedia Database Retrieval: Technology and Applications

This e-book explores multimedia purposes that emerged from desktop imaginative and prescient and computer studying applied sciences. those cutting-edge functions comprise MPEG-7, interactive multimedia retrieval, multimodal fusion, annotation, and database re-ranking. The application-oriented technique maximizes reader knowing of this advanced box.

Optimizing and Troubleshooting Hyper-V Storage

This scenario-focused identify presents concise technical tips and insights for troubleshooting and optimizing garage with Hyper-V. Written through skilled virtualization pros, this little ebook packs loads of worth right into a few pages, providing a lean learn with plenty of real-world insights and most sensible practices for Hyper-V garage optimization.

Extra resources for Computer Vision -- ACCV 2014: 12th Asian Conference on Computer Vision, Singapore, Singapore, November 1-5, 2014, Revised Selected Papers, Part IV

Example text

Image Process. 22, 55–69 (2012) 21. : Aggregating local image descriptors into compact codes. IEEE Trans. Pattern Anal. Mach. 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.

P(x|Mk ) is the probability of an image pixel x from the saliency map Mk to be salient. wk is the weighting K coefficient, given that k=1 wk = 1 and wk ≥ 0, ∀k. K is the number of saliency maps (K = 8 in our case). The main goal is to compute the weighting coefficients in order to improve the degree of similarity between the ground truth and the aggregated saliency map. These weights are computed thanks to the following methods: 1 ; – Uniform: weights w are uniform and spatially invariant, wk = K – Median: weights w are locally deduced from the saliency values.

G. Subject 17 of CASME2 database has 14 samples in C2 class but only 1 sample in C4 class. By this observation, it is clear that samples are non-uniformly distributed to each class and subject. Table 1. Distribution of samples in CASME2 and SMIC databases CASME2 Emotion SMIC Label # samples Emotion Label # samples Happiness C1 33 Positive Disgust C2 60 Negative S2 70 Repression C3 25 Surprise S3 43 Surprise C4 27 Tense C5 102 S1 51 Distribution of databases also depends on whether video frame or video samples are considered as a basic unit.

Download PDF sample

Rated 4.18 of 5 – based on 5 votes