Message Concealing in Vector Images

2021 ◽  
Author(s):  
Alexandr Kuznetsov ◽  
Anna Kononchenko
Keyword(s):  
Vector Images

Text, Images, and Video Analytics for Fog Computing

2018 ◽  
pp. 390-410 ◽  
Author(s):  
A. Jayanthiladevi ◽  
S. Murugan ◽  
K. Manivel
Keyword(s):  
Big Data ◽  
Visual Cortex ◽  
Fog Computing ◽  
Unstructured Data ◽  
Video Analytics ◽  
Color Vector ◽  
Physical Features ◽  
Vector Images ◽  
Text Images ◽  
The Brain

Today, images and image sequences (videos) make up about 80% of all corporate and public unstructured big data. As growth of unstructured data increases, analytical systems must assimilate and interpret images and videos as well as they interpret structured data such as text and numbers. An image is a set of signals sensed by the human eye and processed by the visual cortex in the brain creating a vivid experience of a scene that is instantly associated with concepts and objects previously perceived and recorded in one's memory. To a computer, images are either a raster image or a vector image. Simply put, raster images are a sequence of pixels with discreet numerical values for color; vector images are a set of color-annotated polygons. To perform analytics on images or videos, the geometric encoding must be transformed into constructs depicting physical features, objects and movement represented by the image or video. This chapter explores text, images, and video analytics in fog computing.

SketchFormer: transformer-based approach for sketch recognition using vector images

2020 ◽  
Author(s):  
Anil Singh Parihar ◽  
Gaurav Jain ◽  
Shivang Chopra ◽  
Suransh Chopra
Keyword(s):  
Sketch Recognition ◽  
Vector Images

Solid Rocket Motor Finite Element Analysis Based on the Reverse Modeling of ICT Images

2014 ◽  
Vol 599-601 ◽  
pp. 1708-1711
Author(s):  
Peng Li ◽  
Hong Mei Zhou ◽  
Hong Yi Lu ◽  
Min Zhu ◽  
Qing Gui Chen
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Rocket Motor ◽  
Experimental Result ◽  
Solid Rocket Motor ◽  
Element Analysis ◽  
Contour Segmentation ◽  
Modeling Software ◽  
Solid Rocket ◽  
Vector Images

To Evaluate SRM effectively, according to the structure characteristics of solid rocket motor, the series of solid rocket motor ICT images were processed with edge detection ,edge thinning, contour tracing, contour segmentation, and contour fitting. The raster images were converted to vector images which can be recognized by the CAD modeling software. Then, according to the vector images, SRM was modeled by the software, and the model was studied with finite-element analysis. The experimental result indicates that the quality of the model is good, and the result of the finite-element analysis can reflect the state of the experimental SRM.

Hypercomplex correlation techniques for vector images

2003 ◽  
Vol 51 (7) ◽  
pp. 1941-1953 ◽  
Author(s):  
C.E. Moxey ◽  
S.J. Sangwine ◽  
T.A. Ell
Keyword(s):  
Correlation Techniques ◽  
Vector Images

Tuning a CBIR system for vector images

2004 ◽  
Author(s):  
Tania Di Mascio ◽  
Marco Francesconi ◽  
Daniele Frigioni ◽  
Laura Tarantino
Keyword(s):  
Vector Images

VISTO: A new CBIR system for vector images

2010 ◽  
Vol 35 (7) ◽  
pp. 709-734 ◽  
Author(s):  
Tania Di Mascio ◽  
Daniele Frigioni ◽  
Laura Tarantino
Keyword(s):  
Vector Images

scholarly journals Logistic Regression with Wave Preprocessing to Solve Inverse Problem in Industrial Tomography for Technological Process Control

Energies ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 8116
Author(s):  
Tomasz Rymarczyk ◽  
Konrad Niderla ◽  
Edward Kozłowski ◽  
Krzysztof Król ◽  
Joanna Maria Wyrwisz ◽  
...  
Keyword(s):  
Inverse Problem ◽  
Logistic Regression ◽  
Image Reconstruction ◽  
Tap Water ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Impedance Tomography ◽  
The Subject ◽  
Vector Images ◽  
Selection Of

The research presented here concerns the analysis and selection of logistic regression with wave preprocessing to solve the inverse problem in industrial tomography. The presented application includes a specialized device for tomographic measurements and dedicated algorithms for image reconstruction. The subject of the research was a model of a tank filled with tap water and specific inclusions. The research mainly targeted the study of developing and comparing models and methods for data reconstruction and analysis. The application allows choosing the appropriate method of image reconstruction, knowing the specifics of the solution. The novelty of the presented solution is the use of original machine learning algorithms to implement electrical impedance tomography. One of the features of the presented solution was the use of many individually trained subsystems, each of which produces a unique pixel of the final image. The methods were trained on data sets generated by computer simulation and based on actual laboratory measurements. Conductivity values for individual pixels are the result of the reconstruction of vector images within the tested object. By comparing the results of image reconstruction, the most efficient methods were identified.

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