Dynamic Texture Modeling Applied on Computer Vision Based Fire Recognition

2011 ◽  
pp. 545-553
Author(s):  
Yang Zhao ◽  
Jianhui Zhao ◽  
Erqian Dong ◽  
Bingyu Chen ◽  
Jun Chen ◽  
...  
Keyword(s):  
Computer Vision ◽  
Dynamic Texture ◽  
Texture Modeling ◽  
Fire Recognition

Research on Forest Fire Recognition Based on Computer Vision Technology

2021 ◽  
pp. 518-523
Author(s):  
Kai Yao ◽  
Jinglan Zhang ◽  
Jiuqing Liu
Keyword(s):  
Computer Vision ◽  
Forest Fire ◽  
Computer Vision Technology ◽  
Fire Recognition

Dynamic texture modeling and synthesis using multi-kernel Gaussian process dynamic model

2016 ◽  
Vol 124 ◽  
pp. 63-71 ◽  
Author(s):  
Ziqi Zhu ◽  
Xinge You ◽  
Shujian Yu ◽  
Jixin Zou ◽  
Haiquan Zhao
Keyword(s):  
Gaussian Process ◽  
Dynamic Model ◽  
Dynamic Texture ◽  
Process Dynamic ◽  
Texture Modeling

Underground Fuel Depot Fire Recognition Based on Computer Vision

2008 ◽  
Author(s):  
Yang Du ◽  
Dong Wang ◽  
Kang-Ning Li ◽  
Jun Chen
Keyword(s):  
Computer Vision ◽  
Fire Recognition

scholarly journals CONVOLUTIONAL RECOGNITION OF DYNAMIC TEXTURES WITH PRELIMINARY CATEGORIZATION

2017 ◽  
Vol XLII-2/W4 ◽  
pp. 47-54
Author(s):  
M. N. Favorskaya ◽  
A. V. Pyataeva
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Convolutional Neural Networks ◽  
Single Type ◽  
Dynamic Texture ◽  
Dynamic Textures ◽  
Rigid Objects ◽  
Periodic Movement ◽  
Motion Features ◽  
Binary Features

Dynamic Texture (DT) can be considered as an extension of the static texture additionally comprising the motion features. The DT is very wide but the weak studied type of textures that is employed in many tasks of computer vision. The proposed method of the DTs recognition includes a preliminary categorization based on the proposed four categories, such as natural particles with periodic movement, natural translucency/transparent non-rigid blobs with randomly changed movement, man-made opaque rigid objects with periodic movement, and man-made opaque rigid objects with stationary or chaotic movement. Such formulation permitted to construct the separate spatial and temporal Convolutional Neural Networks (CNNs) for each category. The inputs of the CNNs are a pair of successive frames (taken through 1, 2, 3, or 4 frames according to a category), while the outputs store the sets of binary features in a view of histograms. In test stage, the concatenated histograms are compared with the histograms of the classes using the Kullback-Leibler distance. The experiments demonstrate the efficiency of the designed CNNs and provided the recognition rates up 97.46–98.32% for the sequences with a single type of the DT conducted on the DynTex database.

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.

Modern Technology for Image processing and Computer vision -A Review

2018 ◽  
Vol 1 (2) ◽  
pp. 17-23
Author(s):  
Takialddin Al Smadi
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Augmented Reality ◽  
Video Compression ◽  
Video Processing ◽  
Vision System ◽  
Citrus Fruit ◽  
Modern Technology ◽  
Image And Video Processing ◽  
Depth Video

This survey outlines the use of computer vision in Image and video processing in multidisciplinary applications; either in academia or industry, which are active in this field.The scope of this paper covers the theoretical and practical aspects in image and video processing in addition of computer vision, from essential research to evolution of application.In this paper a various subjects of image processing and computer vision will be demonstrated ,these subjects are spanned from the evolution of mobile augmented reality (MAR) applications, to augmented reality under 3D modeling and real time depth imaging, video processing algorithms will be discussed to get higher depth video compression, beside that in the field of mobile platform an automatic computer vision system for citrus fruit has been implemented ,where the Bayesian classification with Boundary Growing to detect the text in the video scene. Also the paper illustrates the usability of the handed interactive method to the portable projector based on augmented reality.   © 2018 JASET, International Scholars and Researchers Association

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