Computer Vision–Aided Video Coding

2017 ◽  
pp. 57-84
Author(s):  
Manoranjan Paul ◽  
Weisi Lin
Keyword(s):  
Computer Vision ◽  
Video Coding

Application of Computer Vision Techniques for Exploiting New Video Coding Mechanisms

2018 ◽  
pp. 1864-1892
Author(s):  
Artur Miguel Arsenio
Keyword(s):  
Computer Vision ◽  
Image Quality ◽  
Video Coding ◽  
Quality Of Experience ◽  
Service Providers ◽  
End User ◽  
Application Service Providers ◽  
Application Service ◽  
Content Consumption

One of the main concerns for current multimedia platforms is the provisioning of content that provides a good Quality of Experience to end-users. This can be achieved through new interactive, personalized content applications, as well by improving the image quality delivered to the end-user. This chapter addresses these issues by describing mechanisms for changing content consumption. The aim is to give Application Service Providers (ASPs) new ways to allow users to configure contents according to their personal tastes while also improving their Quality of Experience, and to possibly charge users for such functionalities. The authors propose to employ computer vision techniques to produce extra object information, which further expands the range of video personalization possibilities on the presence of new video coding mechanisms1.

3-D Video based Disparity Estimation and Object Segmentation

2013 ◽  
pp. 194-205 ◽  
Author(s):  
Tao Gao
Keyword(s):  
Computer Vision ◽  
Video Coding ◽  
Moving Objects ◽  
Video Retrieval ◽  
Object Segmentation ◽  
Disparity Estimation ◽  
Video Object Segmentation ◽  
Video Object ◽  
Critical Technology ◽  
New Generation

Stereo video object segmentation is a critical technology of the new generation of video coding, video retrieval and other emerging interactive multimedia fields. Determinations of distinctive depth of a frame features have become more popular in everyday life for automation industries like machine vision and computer vision technologies. This paper examines the evaluation of depth cues through dense of two frame stereo correspondence method. Experimental results show that the method can segment the stationary and moving objects with better accuracy and robustness. The contributions have higher accuracy in matching and reducing time of convergence.

Application of Computer Vision Techniques for Exploiting New Video Coding Mechanisms

2013 ◽  
pp. 156-182 ◽  
Author(s):  
Artur Miguel Arsenio
Keyword(s):  
Computer Vision ◽  
Image Quality ◽  
Video Coding ◽  
Quality Of Experience ◽  
Service Providers ◽  
End User ◽  
Application Service Providers ◽  
Application Service ◽  
Content Consumption

One of the main concerns for current multimedia platforms is the provisioning of content that provides a good Quality of Experience to end-users. This can be achieved through new interactive, personalized content applications, as well by improving the image quality delivered to the end-user. This chapter addresses these issues by describing mechanisms for changing content consumption. The aim is to give Application Service Providers (ASPs) new ways to allow users to configure contents according to their personal tastes while also improving their Quality of Experience, and to possibly charge users for such functionalities. The authors propose to employ computer vision techniques to produce extra object information, which further expands the range of video personalization possibilities on the presence of new video coding mechanisms.

Human Vision and Computer Vision

1985 ◽  
Vol 30 (1) ◽  
pp. 47-47
Author(s):  
Herman Bouma
Keyword(s):  
Computer Vision ◽  
Human Vision

Computer vision for industrial applications

1983 ◽  
Vol 2 (5) ◽  
pp. 130
Author(s):  
J.A. Losty ◽  
P.R. Watkins
Keyword(s):  
Computer Vision ◽  
Industrial Applications

The machine training in problems of satellite images’s processing

Metrologiya ◽  
2020 ◽  
pp. 15-37
Author(s):  
L. P. Bass ◽  
Yu. A. Plastinin ◽  
I. Yu. Skryabysheva
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Computer Vision ◽  
Satellite Images ◽  
Vision Systems ◽  
Earth Remote Sensing ◽  
Practical Applications ◽  
Convolution Neural Networks ◽  
Computer Vision Systems ◽  
Trained Neural Network

Use of the technical (computer) vision systems for Earth remote sensing is considered. An overview of software and hardware used in computer vision systems for processing satellite images is submitted. Algorithmic methods of the data processing with use of the trained neural network are described. Examples of the algorithmic processing of satellite images by means of artificial convolution neural networks are given. Ways of accuracy increase of satellite images recognition are defined. Practical applications of convolution neural networks onboard microsatellites for Earth remote sensing are presented.

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