By Qiong Liu, You Yang, Xu Wang, Liujuan Cao (auth.), Shipeng Li, Abdulmotaleb El Saddik, Meng Wang, Tao Mei, Nicu Sebe, Shuicheng Yan, Richang Hong, Cathal Gurrin (eds.)
The two-volume set LNCS 7732 and 7733 constitutes the completely refereed complaints of the nineteenth overseas convention on Multimedia Modeling, MMM 2012, held in Huangshan, China, in January 2013.
The 30 revised typical papers, forty six specific consultation papers, 20 poster consultation papers, and 15 demo consultation papers, and six video browser showdown have been conscientiously reviewed and chosen from numeroues submissions. the 2 volumes include papers offered within the topical sections on multimedia annotation I and II, interactive and cellular multimedia, class, popularity and monitoring I and II, rating in seek, multimedia illustration, multimedia platforms, poster papers, detailed consultation papers, demo consultation papers, and video browser showdown.
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Additional resources for Advances in Multimedia Modeling: 19th International Conference, MMM 2013, Huangshan, China, January 7-9, 2013, Proceedings, Part II
And text localization based on CRF are introduced in Section 5. Conclusions are given in the last section. 2 Related Work Until now, many methods focus on text detection and recognition. Text recognition in the wild is a hot topic recently. Some researchers paid attentions to recognizing the words or letters in a word image which only contains the text . Some researchers studied the end-to-end text recognition , in which a systematic framework from the text detection to text recognition was designed.
4. Fig. 4. The SWT results combined with CCA and MMF. The green boxes are the textual candidate regions, and the red regions are ﬁltered by CCA and MMF. 1 Text Detection in a Character Level Multiple Channels of Text Features In this subsection, we introduce the proposed text detection in a character level which uses text multiple features. There are four types of text features which are Hierarchical Text Detection 29 widely used in text localization: normalized gray intensity (N-gray), color cues, constant gradient variance (CGV), and histogram of gradient (HOG).
8 is mean rate/distortion in terms of bit-rate (2552 /QY ) vs PSNR, which is deﬁned as 10 · Log10 . 2kbit/s, and our system is superior to  in rate/distortion at all bit-rates. 2D/3D Model-Based Facial Video Coding/Decoding at Ultra-Low Bit-Rate 21 Table 4. 8 Fig. 8. 264 are performed at 3,5,7 kbit/s, then participants compare avatar in decoded videos with human being in input videos, and complete the questionnaire. Table 5 is the mean scores, and the range is 0-10. Our system obtains the highest scores, indicating that it has more suitability of subjective face identiﬁcation at ultra-low bit-rate.
Advances in Multimedia Modeling: 19th International Conference, MMM 2013, Huangshan, China, January 7-9, 2013, Proceedings, Part II by Qiong Liu, You Yang, Xu Wang, Liujuan Cao (auth.), Shipeng Li, Abdulmotaleb El Saddik, Meng Wang, Tao Mei, Nicu Sebe, Shuicheng Yan, Richang Hong, Cathal Gurrin (eds.)