The Moving Object Detection and Research Effects of Noise on Images Based on Cellular Automata With a Hexagonal Coating Form and Radon Transform

2020 ◽  
pp. 330-359
Author(s):  
Ruslan Leonidovich Motornyuk ◽  
Stepan Mykolayovych Bilan
Keyword(s):  
Mathematical Model ◽  
Image Processing ◽  
Cellular Automata ◽  
Radon Transform ◽  
Experimental Analysis ◽  
Moving Objects ◽  
Image Identification ◽  
Radon Projections ◽  
Software And Hardware ◽  
Characteristic Features

Methods for image identification based on the Radon transform using hexagonal-coated cellular automata in the chapter are considered. A method and a mathematical model for the detection of moving objects based on hexagonal-coated cellular automata are described. The advantages of using hexagonal coverage for detecting moving objects in the image are shown. The technique of forming Radon projections for moving regions in the image, which is designed for a hexagonal-coated cellular automata, is described. The software and hardware implementation of the developed methods are presented. Based on the obtained results, a hexagonal-coated cellular automata was developed to identify images of objects based on the Radon transform. The Radon transform allowed to effectively extract the characteristic features of images with a large percentage of noise. Experimental analysis showed the advantages of the proposed methods of image processing and identification of moving objects.

Methods for Extracting the Skeleton of an Image Based on Cellular Automata With a Hexagonal Coating Form and Radon Transform

2020 ◽  
pp. 289-329
Author(s):  
Ruslan Leonidovich Motornyuk ◽  
Stepan Mykolayovych Bilan
Keyword(s):  
Mathematical Model ◽  
Image Processing ◽  
Cellular Automata ◽  
Radon Transform ◽  
Experimental Analysis ◽  
Moving Objects ◽  
History Of ◽  
Software And Hardware ◽  
Characteristic Features ◽  
Selection Of

The chapter describes a brief history of the emergence of the theory of cellular automata, their main properties, and methods for constructing. The image skeletonization methods based on the Euler zero differential are described. The advantages of using hexagonal coverage for detecting moving objects in the image are shown. The software and hardware implementation of the developed methods are presented. Based on the obtained results, a hexagonal-coated cellular automata was developed to identify images of objects based on the Radon transform. The method and mathematical model of the selection of characteristic features for the selection of the skeleton and implementation on cellular automata with a hexagonal coating are described. The Radon transform allowed to effectively extract the characteristic features of images with a large percentage of noise. An experiment for different images with different noises was conducted. Experimental analysis showed the advantages of the proposed methods of image processing and extraction of characteristic features.

Protection of Information From the Influence of Noise on the Transmission of Video Data in a Geoinformation System

2020 ◽  
pp. 227-262
Author(s):  
Olga Galan
Keyword(s):  
Image Processing ◽  
Experimental Analysis ◽  
High Performance ◽  
Noise Immunity ◽  
Energy Systems ◽  
Video Data ◽  
Mode Of Transmission ◽  
Characteristic Features ◽  
High Degree ◽  
Influence Of Noise

The chapter describes parallel-hierarchical technologies that are characterized by a high degree of parallelism, high performance, noise immunity, parallel-hierarchical mode of transmission and processing of information. The peculiarities of the design of automated geoinformation and energy systems on the basis of parallel-hierarchical technologies and modified confidential method of Q-transformation of information are presented. Experimental analysis showed the advantages of the proposed methods of image processing and extraction of characteristic features.

scholarly journals Linear Transformation Recognition Using Radon Transform

2017 ◽  
Vol 1 (2) ◽  
pp. 40-47
Author(s):  
Fawaz Hjouj
Keyword(s):  
Image Processing ◽  
Linear Transformation ◽  
Radon Transform ◽  
General Linear ◽  
Regular Functions ◽  
Matrix Vector Multiplication ◽  
Special Cases ◽  
Radon Projections ◽  
The Matrix ◽  
Matrix Vector

Given two regular functions (images) f and g on R2 where g is formed from f by a general linear transformation, g(x) = f (Ax + b). We present a procedure to determine the transformation ‘parameters’ A and b using Radon projections of f and only two projections of g. We use these projections together with simple facts on matrix vector multiplication to recover the matrix A. The assumptions we have here are: f is nonnegative and A is nonsingular. Commonly used transformations in image processing such as rotation, scaling and others are special cases of our approach.

Design and Experimental Analysis of Novel Refractometer Based on Photoelectric Sensor Technology

2011 ◽  
Vol 383-390 ◽  
pp. 5211-5215
Author(s):  
Yin Lin Li ◽  
Zhong Hua Huang ◽  
Kai Bo Hu
Keyword(s):  
Mathematical Model ◽  
Experimental Analysis ◽  
Differential Method ◽  
Theoretical Solution ◽  
Experimental Results ◽  
Sensor Technology ◽  
Photoelectric Sensor ◽  
Better Than

A novel refractometer based on photoelectric sensor technology and differential method is proposed. Sensing principle and mathematical model are introduced; structure and key parameters of sensing probe are designed through detail calculation. Theoretical solution shows resolution reaches order of 10-5. Preliminary experiments verify the feasibility of the design, experimental results show stability error better than ±1.02×10-4, error caused by temperature is 6.65×10-6/°C.

scholarly journals Strobing the moving objects marks in the image processing system with stationary video camera

ScienceRise ◽  
2017 ◽  
Vol 3 (1) ◽  
pp. 66-71 ◽  
Author(s):  
Oleksii Bieliaiev ◽  
Volodymyr Kartashov ◽  
Francy Loutouangou
Keyword(s):  
Image Processing ◽  
Moving Objects ◽  
Processing System ◽  
Video Camera ◽  
Image Processing System

scholarly journals A Real Time Image Processing Bird Repellent System Using Raspberry Pi

2020 ◽  
Vol 5 (2) ◽  
Author(s):  
Oluwole Arowolo ◽  
Adefemi A Adekunle ◽  
Joshua A Ade-Omowaye
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Moving Objects ◽  
Detection System ◽  
Local Binary Patterns ◽  
Raspberry Pi ◽  
Feature Identification ◽  
Data Set ◽  
Haar Features ◽  
Haar Feature

Rice is one of the most consumed foods in Nigeria, therefore it’s production should be on the high as to meet the demand for it. Unfortunately, the quantity of rice produced is being affected by pests such as birds on fields and sometimes in storage. Due to the activities of birds, an effective repellent system is required on rice fields. The proposed effective repellent system is made up of hardware components which are the raspberry pi for image processing, the servo motors for rotation of camera for better field of view controlled by Arduino connected to the raspberry pi, a speaker for generating predator sounds to scare birds away and software component consisting of python and Open Cv library for bird feature identification. The model was trained separately using haar features, HOG (Histogram of Oriented Gradients) and LBP (Local Binary Patterns).Haar features resulted in the highest accuracy of 76% while HOG and LBP were, 27% and 72% respectively. Haar trained model was tested with two recorded real time videos with birds, the false positives were fairly low, about 41%. This haar feature trained model can distinguish between birds and other moving objects unlike a motion detection system which detects all moving objects. This proposed system can be improved to have a higher accuracy with a larger data set of positive and negative images. Keywords—Electronic pest repeller Haar cascade classifier, ultrasonic

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