Video Annotation by Using Heterogeneous Multiple Image Groups on the Web

In this paper we present a proposed multi-layered image format for use on the web. The format implements an algorithm for selecting adequate layer images depending on the image container's surroundings and size. The layer selection depends on the weighted average brightness of the underlying web page background within the bounds of the image. The proposed image format supports multiple image layers with adjoined thresholds and activation conditions. Depending on these conditions and the underlying background, a layer's visibility will be adequately set. The selection algorithm takes into account the background brightness, each layer's adjoined threshold values, and other newly introduced layer conditions. This article has been corrected. Link to the correction <a href="http://dx.doi.org/10.2298/SJEE1803371E">10.2298/SJEE1803371E</a>

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E-Learning: Educational Video Search through Dynamic Video Annotation

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.b1070.1292s419 ◽

2019 ◽

Vol 9 (2S4) ◽

pp. 37-41

Keyword(s):

Learning Environments ◽

Linked Data ◽

Learning Experiences ◽

High Accuracy ◽

Video Annotation ◽

Educational Video ◽

Multimedia Tools ◽

E Learning ◽

Accuracy Performance ◽

The Web

Multimedia tools available on the web, particularl y for learning environments, play an important role in the fiel d of education. With the growing number of these multimedia tools, it is important to explore, share, recycle and connect the m to enhance learning experiences. Many videos available on the web lack semantic connections between them even though they have some similarity through their content. Hence to bridge the gap between the semantically related videos, in the proposed system, we adopt the Linked Data technology, in order to semantically annotate the video resources. The relationships between those annotations are stored in the video annotation ontology. The proposed method provides the benefits of high accuracy, performance and good knowledge for the users.

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Video Annotation via Image Groups from the Web

IEEE Transactions on Multimedia ◽

10.1109/tmm.2014.2312251 ◽

2014 ◽

Vol 16 (5) ◽

pp. 1282-1291 ◽

Cited By ~ 13

Author(s):

Han Wang ◽

Xinxiao Wu ◽

Yunde Jia

Keyword(s):

Video Annotation ◽

The Web

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Computer processing of high-resolution images of periodic specimens

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100141834 ◽

1986 ◽

Vol 44 ◽

pp. 2-5

Author(s):

W. Chiu ◽

M.F. Schmid ◽

T.-W. Jeng

Keyword(s):

High Resolution ◽

Fourier Transforms ◽

Complex Structure ◽

Distribution Functions ◽

Multiple Image ◽

Cryo Electron Microscopy ◽

Structure Factors ◽

Unit Cells ◽

High Resolution Images ◽

Phase Probability Distribution

Cryo-electron microscopy has been developed to the point where one can image thin protein crystals to 3.5 Å resolution. In our study of the crotoxin complex crystal, we can confirm this structural resolution from optical diffractograms of the low dose images. To retrieve high resolution phases from images, we have to include as many unit cells as possible in order to detect the weak signals in the Fourier transforms of the image. Hayward and Stroud proposed to superimpose multiple image areas by combining phase probability distribution functions for each reflection. The reliability of their phase determination was evaluated in terms of a crystallographic “figure of merit”. Grant and co-workers used a different procedure to enhance the signals from multiple image areas by vector summation of the complex structure factors in reciprocal space.

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