Fast Picture and Macroblock Level Adaptive Frame/Field Coding for H.264

2006 ◽  
Author(s):  
Lejun Yu ◽  
Jintao Li ◽  
Yongdong Zhang
Keyword(s):  
Frame Field

Hollow Notion of Corporate Social Responsibility. Introduction of a Frame Field Model to Investigate CSR in Public Discourses

MedienJournal ◽  
2018 ◽  
Vol 42 (1) ◽  
pp. 11-32 ◽  
Author(s):  
Franzisca Weder
Keyword(s):  
Corporate Social Responsibility ◽  
Social Responsibility ◽  
Public Discourse ◽  
Field Model ◽  
Communication Studies ◽  
Business Practices ◽  
Frame Field ◽  
Quantitative Content ◽  
German Speaking ◽  
Corporate Social

The present study examines the relevance and framing of Corporate Social Responsibility in the mass media. Challenged by the ethically (over)loaded issue of responsibility, communication studies are searching for a new understanding of framing to investigate phenomena of new economic values like Corporate Social Responsibility in public discourses. For the quantitative content analysis put forward herein, frames are described as footprints of diverse positions, which determine a given public discourse. The longitudinal analysis of 26 German-speaking newspapers in Germany, Austria, and Switzerland between 1999 and 2008, a phase where CSR was aligned in business practices and CSR communication established in public discourses, aims at identifying CSR-frames as well as inquiring into the existence of a public discourse about CSR. The results show that there is no discourse on CSR itself. Instead of the assumed multiple issue-specific frames, CSR itself is (ab)used as a masterframe or “buzz word” in economic discourses.

Gravity and the frame field

1988 ◽  
Vol 20 (4) ◽  
pp. 371-382 ◽  
Author(s):  
J. S. R. Chisholm ◽  
R. S. Farwell
Keyword(s):  
Frame Field

scholarly journals The Spectrum of Teleparallel Gravity

Universe ◽  
2019 ◽  
Vol 5 (3) ◽  
pp. 80 ◽  
Author(s):  
Tomi Koivisto ◽  
Georgios Tsimperis
Keyword(s):  
General Relativity ◽  
Gravitational Field ◽  
Quadratic Form ◽  
Degrees Of Freedom ◽  
Arbitrary Dimension ◽  
Scalar Potential ◽  
Ultra Violet ◽  
Teleparallel Gravity ◽  
Frame Field ◽  
Covariant Derivation

The observer’s frame is the more elementary description of the gravitational field than the metric. The most general covariant, even-parity quadratic form for the frame field in arbitrary dimension generalises the New General Relativity by nine functions of the d’Alembertian operator. The degrees of freedom are clarified by a covariant derivation of the propagator. The consistent and viable models can incorporate an ultra-violet completion of the gravity theory, an additional polarisation of the gravitational wave, and the dynamics of a magnetic scalar potential.

Fast decision on picture adaptive frame/field coding for H.264

2005 ◽  
Author(s):  
Peng Yin ◽  
Alexis Michael Tourapis ◽  
Jill Boyce
Keyword(s):  
Frame Field ◽  
Fast Decision

scholarly journals Zwei-Dreibein Gravity: A Two-Frame-Field Model of 3D Massive Gravity

2013 ◽  
Vol 111 (11) ◽  
Author(s):  
Eric A. Bergshoeff ◽  
Sjoerd de Haan ◽  
Olaf Hohm ◽  
Wout Merbis ◽  
Paul K. Townsend
Keyword(s):  
Field Model ◽  
Massive Gravity ◽  
Frame Field

scholarly journals A NEW APPROACH TO BLACK HOLE MICROSTATES

1998 ◽  
Vol 13 (23) ◽  
pp. 1875-1879 ◽  
Author(s):  
RICHARD J. EPP ◽  
R. B. MANN
Keyword(s):  
Black Hole ◽  
Degrees Of Freedom ◽  
Black Hole Entropy ◽  
Space Time ◽  
Orthonormal Frame ◽  
Great Promise ◽  
Frame Field ◽  
New Approach ◽  
Four Dimensions ◽  
First Order

If one encodes the gravitational degrees of freedom in an orthonormal frame field, there is a very natural first-order action one can write down (which in four dimensions is known as the Goldberg action). In this letter we will show that this action contains a boundary action for certain microscopic degrees of freedom living at the horizon of a black hole, and argue that these degrees of freedom hold great promise for explaining the microstates responsible for black hole entropy, in any number of space–time dimensions. This approach faces many interesting challenges, both technical and conceptual.

Erratum: Zwei-Dreibein Gravity: A Two-Frame-Field Model of 3D Massive Gravity [Phys. Rev. Lett. 111, 111102 (2013)]

2013 ◽  
Vol 111 (25) ◽  
Author(s):  
Eric A. Bergshoeff ◽  
Sjoerd de Haan ◽  
Olaf Hohm ◽  
Wout Merbis ◽  
Paul K. Townsend
Keyword(s):  
Field Model ◽  
Massive Gravity ◽  
Frame Field

Frame Field Generation for Mesh Parameterization

2010 ◽  
Author(s):  
Felix Kälberer ◽  
Konrad Polthier ◽  
Theodore E. Simos ◽  
George Psihoyios ◽  
Ch. Tsitouras
Keyword(s):  
Frame Field ◽  
Mesh Parameterization ◽  
Field Generation

scholarly journals BUILDING OUTLINE EXTRACTION FROM AERIAL IMAGERY AND DIGITAL SURFACE MODEL WITH A FRAME FIELD LEARNING FRAMEWORK

2021 ◽  
Vol XLIII-B2-2021 ◽  
pp. 487-493
Author(s):  
X. Sun ◽  
W. Zhao ◽  
R. V. Maretto ◽  
C. Persello
Keyword(s):  
Deep Learning ◽  
Semantic Segmentation ◽  
Aerial Images ◽  
Surface Model ◽  
Digital Surface Model ◽  
Frame Field ◽  
Learning Framework ◽  
Elevation Data ◽  
3D Information ◽  
Direction Information

Abstract. Deep learning-based semantic segmentation models for building delineation face the challenge of producing precise and regular building outlines. Recently, a building delineation method based on frame field learning was proposed by Girard et al. (2020) to extract regular building footprints as vector polygons directly from aerial RGB images. A fully convolution network (FCN) is trained to learn simultaneously the building mask, contours, and frame field followed by a polygonization method. With the direction information of the building contours stored in the frame field, the polygonization algorithm produces regular outlines accurately detecting edges and corners. This paper investigated the contribution of elevation data from the normalized digital surface model (nDSM) to extract accurate and regular building polygons. The 3D information provided by the nDSM overcomes the aerial images’ limitations and contributes to distinguishing the buildings from the background more accurately. Experiments conducted in Enschede, the Netherlands, demonstrate that the nDSM improves building outlines’ accuracy, resulting in better-aligned building polygons and prevents false positives. The investigated deep learning approach (fusing RGB + nDSM) results in a mean intersection over union (IOU) of 0.70 in the urban area. The baseline method (using RGB only) results in an IOU of 0.58 in the same area. A qualitative analysis of the results shows that the investigated model predicts more precise and regular polygons for large and complex structures.

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