scholarly journals Fuzzy Logic Using in Images Retrieval Depending on Their Features

2019 ◽  
Vol 54 (5) ◽  
Author(s):  
Faiez Musa Lahmood Alrufaye ◽  
Mohammed Muanis I. Al-Sagheer ◽  
Marwah Thamer Ali
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Fuzzy Logic ◽  
Computer Science ◽  
Image Recognition ◽  
Correct Result ◽  
Image Information ◽  
Fuzzy Logic Algorithm ◽  
Image Characteristics ◽  
Content Information

Image processing has become one of the most important branches of computer science, especially after entering into several areas of life such as medicine, engineering and various sciences. In our current research, we have developed a system of image recognition based on image characteristics and some content information using the most important artificial intelligence algorithms, a fuzzy logic algorithm, to obtain complete image information using small values ranging from 0 to 1. The program was executed on a set of standard database called the WANG database. It holds the contents of 1000 images from the Corel stock photo database, in JPEG format. The system was evaluated using the recall method. This method calculates the proportion of correct results identified by the system as correct results with correct result identified by the classic system.

Optimization of Grease Dispensing Unit Using Fuzzy Logic Algorithm

2016 ◽  
Vol 852 ◽  
pp. 859-866
Author(s):  
Milind Havanur ◽  
A. Arockia Selvakumar
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Logic ◽  
Process Parameters ◽  
Environmental Conditions ◽  
Fuzzy Logic System ◽  
Automated System ◽  
Factors Affecting ◽  
Fuzzy Logic Algorithm ◽  
Optimal Quantity ◽  
Logic System

Grease dispensing unit is a well invented tool for greasing application which preserves health of operator working and ensures optimal quantity. There are fluctuations in the process of grease dispensing which is dependent on process parameters which make the grease dispensing. The properties of grease vary which depend on environmental conditions. In this paper the modeling of grease dispensing process using artificial intelligence method, fuzzy logic to optimize the flow of grease by considering the factors affecting the flow of grease and usage of automated system for grease dispensing process. The work involves usage of LabVIEW for modeling of fuzzy logic network Based on the results obtained a detailed discussions were made on how to implement the fuzzy logic system for optimization of flow of grease for the existing process. Further, the work also details the future scope of work that can be carried out.

Data Analyzing via Probabilistic Modeling

2021 ◽  
pp. 25-51
Author(s):  
Dariusz Jacek Jakóbczak
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Computer Vision ◽  
Object Recognition ◽  
Computer Science ◽  
Classical Problem ◽  
Contour Reconstruction ◽  
Curve Interpolation ◽  
Characteristic Features ◽  
Unknown Object

Object recognition is one of the topics of artificial intelligence, computer vision, image processing, and machine vision. The classical problem in these areas of computer science is that of determining object via characteristic features. An important feature of the object is its contour. Accurate reconstruction of contour points leads to possibility to compare the unknown object with models of specified objects. The key information about the object is the set of contour points which are treated as interpolation nodes. Classical interpolations (Lagrange or Newton polynomials) are useless for precise reconstruction of the contour. The chapter is dealing with proposed method of contour reconstruction via curves interpolation. First stage consists in computing the contour points of the object to be recognized. Then one can compare models of known objects, given by the sets of contour points, with coordinates of interpolated points of unknown object. Contour points reconstruction and curve interpolation are possible using a new method of Hurwitz-Radon matrices.

scholarly journals Beat Defender: Integrating Fuzzy Logic into Video Game

2019 ◽  
Vol 8 (6) ◽  
pp. 1509-1514
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Logic ◽  
Video Game ◽  
Three Dimensional ◽  
Research Process ◽  
Design Development ◽  
Literature Study ◽  
C Language ◽  
Fuzzy Logic Algorithm ◽  
Main Component

Sound has an important role in showing the atmosphere in a video game. But for most people, the sound element in the video game is not overly noticed. So the Beat Defender game was made which highlighted the sound elements in it. This research aims to design and build a game called Beat Defender using fuzzy logic. By using fuzzy logic, components in this game can have an artificial intelligence that reacts with sound. Beat Defender is a three-dimensional video game that relies on music as its main component. The player must keep an object from approaching enemies from all directions. The enemy's movements depend on the artificial intelligence of the fuzzy logic algorithm that reacts to the music that being played. To fight enemies, players use audio visualizations that react with sound input from the player's microphone. The programming language used in the making of this game is the C# language. The research process begins with conducting a literature study, then design, development, testing, report writing and consultation with supervisors throughout the research. After doing research, it can be concluded that designing and building Beat Defender game using fuzzy logic algorithm is a success.

scholarly journals Towards the Design and Implementation of an Image-Based Navigation System of an Autonomous Underwater Vehicle Combining a Color Recognition Technique and a Fuzzy Logic Controller

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4053
Author(s):  
Yu-Hsien Lin ◽  
Chao-Ming Yu ◽  
Chia-Yu Wu
Keyword(s):  
Image Processing ◽  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Noise Removal ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Water Tank ◽  
Image Information ◽  
Revolution Speed ◽  
Underwater Object

This study proposes the development of an underwater object-tracking control system through an image-processing technique. It is used for the close-range recognition and dynamic tracking of autonomous underwater vehicles (AUVs) with an auxiliary light source for image processing. The image-processing technique includes color space conversion, target and background separation with binarization, noise removal with image filters, and image morphology. The image-recognition results become more complete through the aforementioned process. After the image information is obtained for the underwater object, the image area and coordinates are further adopted as the input values of the fuzzy logic controller (FLC) to calculate the rudder angle of the servomotor, and the propeller revolution speed is defined using the image information. The aforementioned experiments were all conducted in a stability water tank. Subsequently, the FLC was combined with an extended Kalman filter (EKF) for further dynamic experiments in a towing tank. Specifically, the EKF predicts new coordinates according to the original coordinates of an object to resolve data insufficiency. Consequently, several tests with moving speeds from 0.2 m/s to 0.8 m/s were analyzed to observe the changes in the rudder angles and the sensitivity of the propeller revolution speed.

scholarly journals Machine Learning Guided 3D Image Recognition for Carbonate Pore and Mineral Volumes Determination

2021 ◽  
Author(s):  
Omar Alfarisi ◽  
Aikifa Raza ◽  
Hongtao Zhang ◽  
Mohamed Sassi ◽  
TieJun Zhang
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Image Processing ◽  
Image Recognition ◽  
Reference Values ◽  
Carbonate Rock ◽  
Image Resolution ◽  
Blind Test ◽  
Thin Sections ◽  
Clastic Rocks

<p>Automated image processing algorithms can improve the quality, efficiency, and consistency of classifying the morphology of heterogeneous carbonate rock and can deal with a massive amount of data and images seamlessly. Geoscientists and petroleum engineers face difficulties in setting the direction of the optimum method for determining petrophysical properties from core plug images of optical thin-sections, Micro-Computed Tomography (μCT), or Magnetic Resonance Imaging (MRI). Most of the successful work is from the homogeneous and clastic rocks focusing on 2D images with less focus on 3D and requiring numerical simulation. Currently, image analysis methods converge to three approaches: image processing, artificial intelligence, and combined image processing with artificial intelligence. In this work, we propose two methods to determine the porosity from 3D μCT and MRI images: an image processing method with Image Resolution Optimized Gaussian Algorithm (IROGA); advanced image recognition method enabled by Machine Learning Difference of Gaussian Random Forest (MLDGRF).</p><p>Meanwhile, we have built reference 3D micro models and collected images for calibration of the IROGA and MLDGRF methods. To evaluate the predictive capability of these calibrated approaches, we ran them on 3D μCT and MRI images of natural heterogeneous carbonate rock. We also measured the porosity and lithology of the carbonate rock using three and two industry-standard ways, respectively, as reference values. Notably, IROGA and MLDGRF have produced porosity results with an accuracy of 96.2% and 97.1% on the training set and 91.7% and 94.4% on blind test validation, respectively, in comparison with the three experimental measurements. We measured limestone and pyrite reference values using two methods, X-ray powder diffraction, and grain density measurements. MLDGRF has produced lithology (limestone and pyrite) volume fractions with an accuracy of 97.7% in comparison to reference measurements.</p>

scholarly journals OpenCV Image Processing for AI Pet Robot

2021 ◽  
Vol 03 (01) ◽  
pp. 65-82
Author(s):  
Abhishek Ghoshal ◽  
◽  
Aditya Aspat ◽  
Elton Lemos ◽  
◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Computer Science ◽  
Emotion Recognition ◽  
Face Tracking ◽  
The Face

The Artificial Intelligence (AI) Pet Robot is a culmination of multiple fields of computer science. This paper showcases the capabilities of our robot. Most of the functionalities stem from image processing made available through OpenCV. The functions of the robot discussed in this paper are face tracking, emotion recognition and a colour-based follow routine. Face tracking allows the robot to keep the face of the user constantly in the frame to allow capturing of facial data. Using this data, emotion recognition achieved an accuracy of 66% on the FER-2013 dataset. The colour-based follow routine enables the robot to follow the user as they walk based on the presence of a specific colour.

Object Recognition via Contour Points Reconstruction Using Hurwitz - Radon Matrices

2013 ◽  
pp. 998-1018
Author(s):  
Dariusz Jakóbczak
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Computer Vision ◽  
Object Recognition ◽  
Computer Science ◽  
Classical Problem ◽  
Contour Reconstruction ◽  
Curve Interpolation ◽  
Characteristic Features ◽  
Unknown Object

Object recognition is one of the topics of artificial intelligence, computer vision, image processing and machine vision. The classical problem in these areas of computer science is that of determining object via characteristic features. Important feature of the object is its contour. Accurate reconstruction of contour points leads to possibility to compare the unknown object with models of specified objects. The key information about the object is the set of contour points which are treated as interpolation nodes. Classical interpolations (Lagrange or Newton polynomials) are useless for precise reconstruction of the contour. The chapter is dealing with proposed method of contour reconstruction via curves interpolation. First stage consists in computing the contour points of the object to be recognized. Then one can compare models of known objects, given by the sets of contour points, with coordinates of interpolated points of unknown object. Contour points reconstruction and curve interpolation is possible using new method of Hurwitz - Radon Matrices.

Object Recognition via Contour Points Reconstruction Using Hurwitz - Radon Matrices

2011 ◽  
pp. 87-107 ◽  
Author(s):  
Dariusz Jakóbczak
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Computer Vision ◽  
Object Recognition ◽  
Computer Science ◽  
Classical Problem ◽  
Contour Reconstruction ◽  
Curve Interpolation ◽  
Characteristic Features ◽  
Unknown Object

Object recognition is one of the topics of artificial intelligence, computer vision, image processing and machine vision. The classical problem in these areas of computer science is that of determining object via characteristic features. Important feature of the object is its contour. Accurate reconstruction of contour points leads to possibility to compare the unknown object with models of specified objects. The key information about the object is the set of contour points which are treated as interpolation nodes. Classical interpolations (Lagrange or Newton polynomials) are useless for precise reconstruction of the contour. The chapter is dealing with proposed method of contour reconstruction via curves interpolation. First stage consists in computing the contour points of the object to be recognized. Then one can compare models of known objects, given by the sets of contour points, with coordinates of interpolated points of unknown object. Contour points reconstruction and curve interpolation is possible using new method of Hurwitz - Radon Matrices.

scholarly journals Research on the Method of Machine Learning Algorithms in Ecological Construction

2021 ◽  
Vol 2136 (1) ◽  
pp. 012061
Author(s):  
Binjie Xia
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Image Processing ◽  
Support Vector Machine ◽  
Deep Learning ◽  
Image Recognition ◽  
Rapid Development ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Ecological Construction

Abstract In the rapid development of modern artificial intelligence, for the development of ecological construction, how to rationally use machine learning to promote the development of agricultural economy has become a focus of practice and scientific research. This paper takes ecological image recognition as an example to analyze how to use support vector machine in image processing technology and machine learning in deep learning to conduct in-depth research, and to optimize and improve the algorithm to build an ecological image recognition model.

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