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Journal articles on the topic 'Artificial vision system'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Fink, Wolfgang, and Mark A. Tarbell. "Artificial vision support system (AVS2) for improved prosthetic vision." Journal of Medical Engineering & Technology 38, no. 8 (2014): 385–95. http://dx.doi.org/10.3109/03091902.2014.957869.

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2

Loresco, Pocholo James M., and Elmer Dadios. "Vision-Based Lettuce Growth Stage Decision Support System Using Artificial Neural Networks." International Journal of Machine Learning and Computing 10, no. 4 (2020): 534–41. http://dx.doi.org/10.18178/ijmlc.2020.10.4.969.

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3

Mauledoux, Mauricio M., Carlos Hernandez, Crhistian C. G. Segura, and Oscar F. Aviles. "Object Tracking System Based on Artificial Vision Algorithms." Applied Mechanics and Materials 713-715 (January 2015): 420–23. http://dx.doi.org/10.4028/www.scientific.net/amm.713-715.420.

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This document describes the architecture of a tracking system with two degrees of freedom (pan and tilt), endowed with artificial vision to follow the path of a moving object. The mechanism with a fixed base was designed to cover lateral and vertical ranges of movement, similar to the visual field in humans, limiting its depth by the resolution of the camera. The object that defines the motion path presents color uniformity across its surface, becoming the main feature in which the recognition and tracking algorithm were based. The tracking is performed by reducing the error between the object

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4

Seo, Chang-Jin. "Artificial Vision System using Human Visual Information Processing." Journal of Digital Convergence 12, no. 11 (2014): 349–55. http://dx.doi.org/10.14400/jdc.2014.12.11.349.

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Sanmartín and Briceño. "Development of an Artificial Vision System for Underwater Vehicles." Proceedings 21, no. 1 (2019): 1. http://dx.doi.org/10.3390/proceedings2019021001.

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Beyond certain depth there is no light, supposing the main obstacle in the use of optical systems beneath the water. Therefore, the underwater vision system developed is composed of a set of underwater lights which allow the system to work properly and the cameras. These are integrated with the navigation system through the Robot Operating System (ROS) framework, which handles the acquisition and processing of information to be used as support for the navigation and which is also essential for its use in reconnaissance missions.

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Faber, Tracy L. "Next generation artificial vision systems: reverse engineering the human visual system." Academic Radiology 16, no. 5 (2009): 642. http://dx.doi.org/10.1016/j.acra.2009.01.013.

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7

Bourbakis, Nikolaos G., Mike Papazoglou, and George Alexiou. "Multiprocessor vision system." Microprocessors and Microsystems 14, no. 9 (1990): 573–82. http://dx.doi.org/10.1016/0141-9331(90)90092-a.

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8

MERTOGUNO, J. S., and N. G. BOURBAKIS. "KYDON VISION SYSTEM: THE ADAPTIVE LEARNING MODEL." International Journal on Artificial Intelligence Tools 04, no. 04 (1995): 453–69. http://dx.doi.org/10.1142/s021821309500022x.

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In this paper, an adaptive learning model for an autonomous vision system multi-layers architecture, called Kydon, are presented, modeled, and analyzed. In particular two critical (deletion and saturation) points on the learning curve are evaluated. These points represent two extreme states on the learning process. The Kydon architecture consists of ‘k’ layers array processors. The lowest layers consists of lower-level processing layers, and the rest consists of higher-level processing layers. The interconnectivity of the PEs in each array is based on a full hexagonal mesh structure. Kydon use

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9

STEFANOV, S. Z. "DAILY ARTIFICIAL DISPATCHER LONG-TERM VISION." New Mathematics and Natural Computation 09, no. 01 (2013): 65–75. http://dx.doi.org/10.1142/s1793005713500051.

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Long-term vision of an electrical power system (EPS) "Daily Artificial Dispatcher" is expressed as intentions for secure and effective leading for a day ahead, transferred into modes via memory and adaptation. This long-term vision is interpreted as a story for a nine or ten-year-old child for his efforts not to disperse his friends.

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10

Komati, Karin S., and Alberto F. De Souza. "Using Weightless Neural Networks for Vergence Control in an Artificial Vision System." Applied Bionics and Biomechanics 1, no. 1 (2003): 21–31. http://dx.doi.org/10.1155/2003/626283.

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This paper presents a methodology we have developed and used to implement an artificial binocular vision system capable of emulating the vergence of eye movements. This methodology involves using weightless neural networks (WNNs) as building blocks of artificial vision systems. Using the proposed methodology, we have designed several architectures of WNN-based artificial vision systems, in which images captured by virtual cameras are used for controlling the position of the ‘foveae’ of these cameras (high-resolution region of the images captured). Our best architecture is able to control the f

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11

García-Luna, F., and A. Morales-Díaz. "Towards an artificial vision-robotic system for tomato identification." IFAC-PapersOnLine 49, no. 16 (2016): 365–70. http://dx.doi.org/10.1016/j.ifacol.2016.10.067.

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12

Kim, Hanguen, Donghoon Kim, Byeolteo Park, and Seung-Mok Lee. "Artificial Intelligence Vision-Based Monitoring System for Ship Berthing." IEEE Access 8 (2020): 227014–23. http://dx.doi.org/10.1109/access.2020.3045487.

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13

Quesada-Arencibia, Alexis, Roberto Moreno-Díaz, and Miguel Aleman-Flores. "A Two-Channel Artificial Vision System for Motion Analysis." Systems Analysis Modelling Simulation 43, no. 9 (2003): 1271–79. http://dx.doi.org/10.1080/02329290310001600327.

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14

FERNAÁNDEZ, J. R., S. FEIJOO, R. BALASA, and J. TABOADA. "SYSTEM OF ARTIFICIAL VISION FOR THE ACTUATION OF ROBOTS." Cybernetics and Systems 25, no. 2 (1994): 321–34. http://dx.doi.org/10.1080/01969729408902330.

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15

Yan, Bai, and Liu Mei. "Design of intelligent invigilator system based on artificial vision." Journal of Physics: Conference Series 1881, no. 4 (2021): 042054. http://dx.doi.org/10.1088/1742-6596/1881/4/042054.

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16

O.A., Shkuropat, Shelehov I.V., and Myronenko M.A. "Intelligence system of artificial vision for unmanned aerial vehicle." Artificial Intelligence 25, no. 4 (2020): 53–58. http://dx.doi.org/10.15407/jai2020.04.053.

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The article considers the method of factor cluster analysis which allows automatically retrain the onboard recognition system of an unmanned aerial system. The task of informational synthesis of an on-board system for identifying frames is solved within the information-extreme intellectual technology of data analysis, based on maxi- mizing the informational ability of the system during machine learning. Based on the functional approach to modeling cognitive processes inherent to humans during forming and making classification decisions, it was proposed a categorical model in the form of a dire

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17

Bourbakis, N. G., F. Barlos, and J. S. Mertoguno. "Hermes Autonomous Vision System." International Journal of Pattern Recognition and Artificial Intelligence 12, no. 03 (1998): 265–91. http://dx.doi.org/10.1142/s0218001498000191.

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This paper deals with the modeling of an extended quartet multiprocessor kernel used for the evaluation of the Hermes system. Hermes is a multiprocessor hybrid system architecture used as a machine vision system. The functionality of Hermes requires an asynchronous information flow upwards and downwards, where "orders"(in a form of code and data) go down and "abstracted" or processed picture information goes up along the system's hierarchy. Moreover, the overall functional behavior of the Hermes system can be considered as an extended quartet kernel in an abstracted manner. The extended quarte

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18

Aguilera, Cristhian, Mario Ramos, and Gabriel Roa. "An Automatic Grading System for Panels Surfaces Using Artificial Vision." International Journal of Computers Communications & Control 1, no. 2 (2006): 15. http://dx.doi.org/10.15837/ijccc.2006.2.2281.

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This work describes an automatic grading system using artificial vision to improve the quality of wood panels surfaces. The objective is to control stains on the surface. Artificial Vision techniques like Thresholding and Transformed Watershed methods are applied. Defects quantitative measures found on the surface are also presented, in particular quantity, area, intensity and distribution.

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19

Gil, Ángel, Miguel Márquez, Erica Chacón, and Angélica Ramirez. "An Artificial Vision-Based Computer Interface." Journal of Konbin 8, no. 1 (2008): 27–34. http://dx.doi.org/10.2478/v10040-008-0097-4.

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An Artificial Vision-Based Computer InterfaceAn application has been developed to assist in using software applications to individuals that have null or limited mobility of their upper limbs. The system uses a Web camera for movement patterns recognition of the user's face. The image analysis allows emulating the basic functions of the computer mouse. The face detection process is carried out through the implementation of an algorithm that uses a cascade sort key of Haar-Like type. The application was developed in C++ to be used on Windows XP platform. Tryout of the application has been perfor

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20

Jankar, Ms P. A. "Computer Vision Crowd Detection System." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 438–41. http://dx.doi.org/10.22214/ijraset.2021.34996.

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Now days, everyone is facing with the problem of very dangerous disease that is COVID-19. The virus is transmitted through infected person coughs, sneezes or exhales. So, maintaining close face to face contact with other peoples is the best solution (way) to minimize the spread of corona virus disease 2019(covid19). COVID-19 spreads mainly among people who are in close contact for a prolonged period so this problem is mostly occurred in public places like collages, schools, malls, stations, etc. We need of surveillance system that could be monitor and detect if the people are following social

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21

Cristian, Manuel Agudelo Restrepo, Eduardo Roa-Guerrero Edgar, and Numpaque López Humberto. "Leaf detector box: Artificial vision system for leaf area identification." African Journal of Agricultural Research 12, no. 20 (2017): 1702–12. http://dx.doi.org/10.5897/ajar2016.11698.

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22

Zidek, Kamil, Jan Pitel, and Alexander Hosovsky. "DESIGN OF ADJUSTABLE SMART VISION SYSTEM BASEDON ARTIFICIAL MUSCLE ACTUATORS." MM Science Journal 2016, no. 03 (2016): 947–51. http://dx.doi.org/10.17973/mmsj.2016_09_201635.

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23

Pinto, Andry Maykol G., A. Paulo Moreira, and Paulo G. Costa. "Indoor Localization System based on Artificial Landmarks and Monocular Vision." TELKOMNIKA (Telecommunication Computing Electronics and Control) 10, no. 4 (2012): 609. http://dx.doi.org/10.12928/telkomnika.v10i4.848.

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24

Fernandez Villan, Alberto, Rodrigo Garcia Acevedo, Eduardo Alvarez Alvarez, et al. "Low-Cost System for Weld Tracking Based on Artificial Vision." IEEE Transactions on Industry Applications 47, no. 3 (2011): 1159–67. http://dx.doi.org/10.1109/tia.2011.2124432.

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25

Qiu, Weijie, Yulong Huang, Ling‐An Kong, et al. "Optoelectronic In‐Ga‐Zn‐O Memtransistors for Artificial Vision System." Advanced Functional Materials 30, no. 40 (2020): 2002325. http://dx.doi.org/10.1002/adfm.202002325.

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26

Shafiee, Sahameh, Saeid Minaei, Nasrollah Moghaddam-Charkari, and Mohsen Barzegar. "Honey characterization using computer vision system and artificial neural networks." Food Chemistry 159 (September 2014): 143–50. http://dx.doi.org/10.1016/j.foodchem.2014.02.136.

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27

Dieste, J. A., A. Fernández, C. Javierre, and J. Santolaria. "Environmentally Conscious Polishing System Based on Robotics and Artificial Vision." Advances in Mechanical Engineering 7, no. 2 (2014): 798907. http://dx.doi.org/10.1155/2014/798907.

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28

Facco, Pierantonio, Andrea C. Santomaso, and Massimiliano Barolo. "Artificial vision system for particle size characterization from bulk materials." Chemical Engineering Science 164 (June 2017): 246–57. http://dx.doi.org/10.1016/j.ces.2017.01.053.

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29

MENG, YICONG, ERIC K. C. TSANG, STANLEY Y. M. LAM, and BERTRAM E. SHI. "THE HKUST MULTIMAP SYSTEM FOR ACTIVE VISION." International Journal of Humanoid Robotics 06, no. 03 (2009): 505–36. http://dx.doi.org/10.1142/s0219843609001838.

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This paper describes the HKUST MultiMap system, a hardware system we have developed to support the computation and integration of large numbers of neurally inspired feature maps at frame rates. The computed maps could serve as the basis for a generic image representation to enable robots to perform a variety of different tasks. In order to support the computation of feature representations with increasing complexity, the system can split the processing among a scalable number of processors, each on a different printed circuit board. We describe the hardware design of this board, as well as the

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30

Noguchi, Noboru, John F. Reid, Qin Zhang, Lei Tian, and Al C. Hansen. "Vision Intelligence for Mobile Agro-Robotic System." Journal of Robotics and Mechatronics 11, no. 3 (1999): 193–99. http://dx.doi.org/10.20965/jrm.1999.p0193.

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We developed an intelligent vision system for mobile robot field operations. Fuzzy logic was used to classify crops and weeds. A genetic algorithm (GA) was used to optimize and tune fuzzy logic membership rules. Field studies confirmed that our method accurately classified crops and weeds throughout their growth cycle. After separating out weeds, an artificial neural network (ANN) was used to estimate crop height and width. The r2 for estimating crop height was 0.92 for training data and 0.83 for test data. A geographic information system (GIS) was used to create a crop growth map.

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31

Chien, Chiun-Hong. "A computer vision system for Extravehicular Activity Helper/Retriever." Applied Intelligence 5, no. 3 (1995): 251–68. http://dx.doi.org/10.1007/bf00872225.

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32

Kılıç, Kıvanç, İsmail Hakki Boyacı, Hamit Köksel, and İsmail Küsmenoğlu. "A classification system for beans using computer vision system and artificial neural networks." Journal of Food Engineering 78, no. 3 (2007): 897–904. http://dx.doi.org/10.1016/j.jfoodeng.2005.11.030.

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33

Kitagaki, Kosei, Takashi Suehiro, and Motoyoshi Fujiwara. "Sensor-Based Artificial Skills with Force and Vision Information." Journal of Robotics and Mechatronics 13, no. 3 (2001): 254–66. http://dx.doi.org/10.20965/jrm.2001.p0254.

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To add flexibility against changes in assembly processes to a manufacturing system, we introduce artificial skills with advanced sensor-based information. Tasks are divided into a phase in local (contact) and global (noncontact) states and we developed pseudo contact point monitoring and feature-oriented coordination to achieve artificial skills for tasks in each phase. Pseudo contact point monitoring is a new technique that detects contact states by monitoring a pseudo contact point calculated from a set of observed force and moment. Effectiveness of artificial skills is shown by experimental

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34

Chen, Su-Shing. "An intelligent computer vision system." International Journal of Intelligent Systems 1, no. 1 (1986): 15–28. http://dx.doi.org/10.1002/int.4550010104.

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35

HORIASHCHENKO, S., and K. HORIASHCHENKO. "TECHNICAL VISION SYSTEM WITH ARTIFICIAL INTELLIGENCE FOR CAPTURING CYLINDRICAL OBJECTS BY ROBOT." Computer Systems and Information Technologies 1, no. 1 (2020): 47–53. http://dx.doi.org/10.31891/csit-2020-1-6.

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The article presents a variant of working with a system of technical vision, which recognizes cylindrical objects. This vision system based on artificial intelligence, which allows you to determine the circles in the image. The coordinates of the value of the circle are necessary for the exact positioning of the robot manipulator. The calculation of the gradient and threshold separation determine the gaps in the intensity of the image of the object. These methods define pixels lying on the border between the object and the background. The further process consists in connection of the segments

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Krüger, Norbert, and Florentin Wörgötter. "Symbolic Pointillism: Computer Art Motivated by Human Brain Structures." Leonardo 38, no. 4 (2005): 337–40. http://dx.doi.org/10.1162/0024094054762052.

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The authors introduce a new kind of computer art motivated by cortical structures in the human visual system. This type of computer art is related to the sub-group of the impressionist art movement called pointillism. However, while pointillism visualizes and makes use of processes that have been associated with the human eye, Symbolic Pointillism also makes cortical processes explicit. The visual representations underlying this art have been developed during a project that aims at the transfer of functional aspects of human vision to artificial systems. The authors have applied their findings

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37

Ahmed Fadzil, M. H., and C. J. Weng. "LED cosmetic flaw vision inspection system." Pattern Analysis and Applications 1, no. 1 (1998): 62–70. http://dx.doi.org/10.1007/bf01238027.

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38

Chen, Ai Hua, Bing Wei He, and Cheng Hui Gao. "Three-Dimensional Information Acquisition Based on Artificial Compound Eye Vision System." Applied Mechanics and Materials 678 (October 2014): 290–94. http://dx.doi.org/10.4028/www.scientific.net/amm.678.290.

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In this paper, the artificial compound eye vision system is used to develop an effective algorithm for acquiring 3D information of object. According to compound eye vision system constructed, the imaging principle and correlation between the object point and the imaging point is analyzed. Then the cross correlation matching algorithm is present to extract the depth and obtain the 3D information. The influence factors such as number of matched elemental images and matching window size are considered and analyzed. The experimental is implemented to verify the validity of the proposed method.

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39

Song, Andy, Vic Ciesielski, and Peter Rogers. "Vision system development by machine learning: Mashing assessment in brewing." Applied Artificial Intelligence 15, no. 8 (2001): 777–95. http://dx.doi.org/10.1080/088395101317018609.

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40

Ampatzidis, Y., A. Cruz, R. Pierro, et al. "Vision-based system for detecting grapevine yellow diseases using artificial intelligence." Acta Horticulturae, no. 1279 (June 2020): 225–30. http://dx.doi.org/10.17660/actahortic.2020.1279.33.

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41

Yang, Jianzhong, Xianyang Li, Yu Jiang, Guihua Qiu, and S. Buckdahn. "Target recognition system of dynamic scene based on artificial intelligence vision." Journal of Intelligent & Fuzzy Systems 35, no. 4 (2018): 4373–83. http://dx.doi.org/10.3233/jifs-169757.

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42

Draghici, Sorin. "A Neural Network Based Artificial Vision System for Licence Plate Recognition." International Journal of Neural Systems 08, no. 01 (1997): 113–26. http://dx.doi.org/10.1142/s0129065797000148.

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This paper presents a neural network based artificial vision system able to analyze the image of a car given by a camera, locate the registration plate and recognize the registration number of the car. The paper describes in detail various practical problems encountered in implementing this particular application and the solution used to solve them. The main features of the system presented are: controlled stability-plasticity behavior, controlled reliability threshold, both off-line and on-line learning, self assessment of the output reliability and high reliability based on high level multip

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43

TISDALE, E. ROBERT, and WALTER J. KARPLUS. "SYSTEM IDENTIFICATION WITH ARTIFICIAL NEURAL NETWORKS." International Journal of Pattern Recognition and Artificial Intelligence 06, no. 01 (1992): 93–111. http://dx.doi.org/10.1142/s0218001492000059.

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System identification is the term scientists and engineers use to refer to the process of building mathematical models of dynamical systems based on observed data. This paper approaches system identification as a pattern recognition problem. We use computers to simulate the system response for a variety of different mathematical models. For each distinct system model, simulated system responses tend to remain segregated in one or more amorphous regions of system response space despite (1) large variations in system parameters, (2) experimental errors, and (3) noise. The actual system response

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44

RADUCANU, BOGDAN, and JORDI VITRIÀ. "FACE RECOGNITION BY ARTIFICIAL VISION SYSTEMS: A COGNITIVE PERSPECTIVE." International Journal of Pattern Recognition and Artificial Intelligence 22, no. 05 (2008): 899–913. http://dx.doi.org/10.1142/s0218001408006545.

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Cognitive development refers to the ability of a system to gradually acquire knowledge through experiences during its existence. As a consequence, the learning strategy should be represented as an integrated, online process that aims to build a model of the "world" and a continuous update of this model. Considering as reference the Modal Model of Memory introduced by Atkinson and Schiffrin, we propose an online learning algorithm for cognitive systems design. The incremental part of the algorithm is responsible of updating existing information or creating new data categories and the decrementa

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PRETLOVE, J. R. G., and G. A. PARKER. "THE SURREY ATTENTIVE ROBOT VISION SYSTEM." International Journal of Pattern Recognition and Artificial Intelligence 07, no. 01 (1993): 89–107. http://dx.doi.org/10.1142/s0218001493000066.

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This paper presents the design and development of a real-time eye-in-hand stereo-vision system to aid robot guidance in a manufacturing environment. The stereo vision head comprises a novel camera arrangement with servo-vergence, focus, and aperture that continuously provides high-quality images to a dedicated image processing system and parallel processing array. The stereo head has four degrees of freedom but it relies on the robot end-effector for all remaining movement. This provides the robot with exploratory sensing abilities allowing it to undertake a wider variety of less constrained t

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Kim, Jeong-Min, Youngsik Kim, Shin-Dug Kim, Tack-Don Han, and Sung-Bong Yang. "An Adaptive Parallel Computer Vision System." International Journal of Pattern Recognition and Artificial Intelligence 12, no. 03 (1998): 311–34. http://dx.doi.org/10.1142/s021800149800021x.

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An approach for designing a hybrid parallel system that can perform different levels of parallelism adaptively is presented. An adaptive parallel computer vision system (APVIS) is proposed to attain this goal. The APVIS is constructed by integrating two different types of parallel architectures, i.e. a multiprocessor based system (MBS) and a memory based processor array (MPA), tightly into a single machine. One important feature in the APVIS is that the programming interface to execute data parallel code onto the MPA is the same as the usual subroutine calling mechanism. Thus the existence of

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Pathirana, Chandima, Keigo Watanabe, Kiyotaka Izumi, H. Y. Aruna Hewawasam, and Lanka Udawatta. "Intelligent vision system for dynamic environments." Artificial Life and Robotics 10, no. 1 (2006): 59–63. http://dx.doi.org/10.1007/s10015-005-0382-4.

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48

Wang, Yong, and Hui Guo. "Machine Vision and Applications." Applied Mechanics and Materials 457-458 (October 2013): 1377–80. http://dx.doi.org/10.4028/www.scientific.net/amm.457-458.1377.

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With the development of modern industry, manual labor has been far from meeting the needs of modern industry. In recent years,with the development of artificial intelligence algorithms and other related field. Machine vision has been applied in a wide field of industry. An overview of key machine vision system, and demonstrates some machine vision applications will be proposed in this paper.

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49

Dorrer, M. G., A. A. Popov, and A. E. Tolmacheva. "Building an artificial vision system of an agricultural robot based on the DarkNet system." IOP Conference Series: Earth and Environmental Science 548 (September 2, 2020): 032032. http://dx.doi.org/10.1088/1755-1315/548/3/032032.

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50

Soria, Carlos M., Ricardo Carelli, Rafael Kelly, and Juan M. Ibarra Zannatha. "Coordinated Control Of Mobile Robots Based On Artificial Vision." International Journal of Computers Communications & Control 1, no. 2 (2006): 85. http://dx.doi.org/10.15837/ijccc.2006.2.2288.

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This work presents a control strategy for coordination of multiple robots based on artificial vision to measure the relative posture between them, in order to reach and maintain a specified formation. Given a leader robot that moves about an unknown trajectory with unknown velocity, a controller is designed to maintain the robots following the leader at a certain distance behind, by using visual information about the position of the leader robot. The control system is proved to be asymptotically stable at the equilibrium point, which corresponds to the accomplishment of the navigation objectiv

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