Vision-model-based impairment metric to evaluate blocking artifacts in digital video

2002 ◽  
Vol 90 (1) ◽  
pp. 154-169 ◽  
Author(s):  
Zhenghua Yu ◽  
Hong Ren Wu ◽  
S. Winkler ◽  
Tao Chen
Keyword(s):  
Digital Video ◽  
Blocking Artifacts ◽  
Model Based

Trends in ROV Development

2008 ◽  
Vol 42 (1) ◽  
pp. 38-43 ◽  
Author(s):  
Steve Cohan
Keyword(s):  
Control Systems ◽  
Digital Video ◽  
Sensor Data ◽  
Motion Coordination ◽  
Model Based Control ◽  
Remote Diagnostics ◽  
Model Based ◽  
End Use ◽  
And Control ◽  
Productive Time

This paper describes three new directions in the development of ROV control systems: exploiting the capabilities of digital video, using model-based control techniques for ROV operations, and providing ROV systems with sophisticated remote diagnostics. Goals of these trends are to increase the value of ROV operations end products (such as video), to increase efficiency of operations by adding automation, and to increase productive time by bringing outside resources to the ROV for maintenance and troubleshooting.Advances in digital video can enhance the value of video imagery by facilitating the combination of video with other data, improving utility, and increasing the range of analyses and products that can be created from video. Current digital video technology enables systems to treat video in a manner close to the transmission and processing of other sensor data, reducing the number of components and interfaces between the camera and the video imagery's end use. Model-based control has the potential to relieve pilots from managing all tasks concerning low-level motion, coordination, and control, enabling pilots to concentrate on higher-level task planning and execution. Applying software techniques designed specifically for managing data flow in distributed control systems can give remote experts access to all the tools and information necessary to assist with worksite maintenance and troubleshooting.

scholarly journals Production model based digital video segmentation

1995 ◽  
Vol 1 (1) ◽  
pp. 9-46 ◽  
Author(s):  
Arun Hampapur ◽  
Ramesh Jain ◽  
Terry E Weymouth
Keyword(s):  
Digital Video ◽  
Video Segmentation ◽  
Production Model ◽  
Model Based

Representation, abstraction, and simple-minded sophisticates

2020 ◽  
Vol 43 ◽  
Author(s):  
Peter Dayan
Keyword(s):  
Decision Theory ◽  
Predictive Coding ◽  
Bayesian Decision Theory ◽  
Bayesian Decision ◽  
Model Based ◽  
Model Free

Abstract Bayesian decision theory provides a simple formal elucidation of some of the ways that representation and representational abstraction are involved with, and exploit, both prediction and its rather distant cousin, predictive coding. Both model-free and model-based methods are involved.

Quantitative Model-Based Image Analysis of NuMa Distribution Links Nuclear Organization with Cell Phenotype

2001 ◽  
Vol 7 (S2) ◽  
pp. 578-579
Author(s):  
David W. Knowles ◽  
Sophie A. Lelièvre ◽  
Carlos Ortiz de Solόrzano ◽  
Stephen J. Lockett ◽  
Mina J. Bissell ◽  
...  
Keyword(s):  
Image Analysis ◽  
Mammary Epithelial Cells ◽  
Nuclear Organization ◽  
Critical Role ◽  
Quantitative Model ◽  
Mammary Epithelial ◽  
Cell Phenotype ◽  
Proliferating Cells ◽  
Mitotic Apparatus ◽  
Model Based

The extracellular matrix (ECM) plays a critical role in directing cell behaviour and morphogenesis by regulating gene expression and nuclear organization. Using non-malignant (S1) human mammary epithelial cells (HMECs), it was previously shown that ECM-induced morphogenesis is accompanied by the redistribution of nuclear mitotic apparatus (NuMA) protein from a diffuse pattern in proliferating cells, to a multi-focal pattern as HMECs growth arrested and completed morphogenesis . A process taking 10 to 14 days.To further investigate the link between NuMA distribution and the growth stage of HMECs, we have investigated the distribution of NuMA in non-malignant S1 cells and their malignant, T4, counter-part using a novel model-based image analysis technique. This technique, based on a multi-scale Gaussian blur analysis (Figure 1), quantifies the size of punctate features in an image. Cells were cultured in the presence and absence of a reconstituted basement membrane (rBM) and imaged in 3D using confocal microscopy, for fluorescently labeled monoclonal antibodies to NuMA (fαNuMA) and fluorescently labeled total DNA.

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