Knowledge Sources for Understanding and Describing Image Sequences

1982 ◽  
pp. 1-21 ◽  
Author(s):  
Bernd Neumann
Keyword(s):  
Image Sequences ◽  
Knowledge Sources

Knowledge creation in small building and construction firms

2013 ◽  
Vol 1 (1) ◽  
pp. 125-142 ◽  
Author(s):  
Susanne Durst ◽  
Ingi Runar Edvardsson ◽  
Guido Bruns
Keyword(s):  
Construction Industry ◽  
Knowledge Creation ◽  
Vital Role ◽  
Knowledge Sources ◽  
Structured Interviews ◽  
Construction Firms ◽  
External Knowledge ◽  
Body Of Knowledge ◽  
Small Building

Studies on knowledge creation are limited in general, and there is a particular shortage of research on the topic in small and medium-sized enterprises (SMEs). Given the importance of SMEs for the economy and the vital role of knowledge creation in innovation, this situation is unsatisfactory. Accordingly, the purpose of our study is to increase our understanding of how SMEs create new knowledge. Data are obtained through semi-structured interviews with ten managing directors of German SMEs operating in the building and construction industry. The findings demonstrate the influence of external knowledge sources on knowledge creation activities. Even though the managing directors take advantage of different external knowledge sources, they seem to put an emphasis on informed knowledge sources. The study´s findings advance the limited body of knowledge regarding knowledge creation in SMEs.

Track-Before-Declare Methods in IR Image Sequences

1992 ◽  
Author(s):  
Steven M. Bolen
Keyword(s):  
Image Sequences

Knowledge Sources and Waste Reduction: Less is More

2016 ◽  
Author(s):  
Luca Berchicci ◽  
Will Mitchell
Keyword(s):  
Waste Reduction ◽  
Knowledge Sources ◽  
Less Is More

scholarly journals A Driver’s Visual Attention Prediction Using Optical Flow

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3722
Author(s):  
Byeongkeun Kang ◽  
Yeejin Lee
Keyword(s):  
Visual Attention ◽  
Optical Flow ◽  
Prediction Models ◽  
Apparent Movement ◽  
Image Sequences ◽  
Motion Information ◽  
Accuracy Improvement ◽  
Flow Maps ◽  
Current State ◽  
Motion Features

Motion in videos refers to the pattern of the apparent movement of objects, surfaces, and edges over image sequences caused by the relative movement between a camera and a scene. Motion, as well as scene appearance, are essential features to estimate a driver’s visual attention allocation in computer vision. However, the fact that motion can be a crucial factor in a driver’s attention estimation has not been thoroughly studied in the literature, although driver’s attention prediction models focusing on scene appearance have been well studied. Therefore, in this work, we investigate the usefulness of motion information in estimating a driver’s visual attention. To analyze the effectiveness of motion information, we develop a deep neural network framework that provides attention locations and attention levels using optical flow maps, which represent the movements of contents in videos. We validate the performance of the proposed motion-based prediction model by comparing it to the performance of the current state-of-art prediction models using RGB frames. The experimental results for a real-world dataset confirm our hypothesis that motion plays a role in prediction accuracy improvement, and there is a margin for accuracy improvement by using motion features.

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