scholarly journals Detection of Component Assembly Error using Computer Vision: A Review

2020 ◽  
Vol 9 (10) ◽  
pp. 402-405
Keyword(s):  
Computer Vision ◽  
Object Recognition ◽  
Manufacturing Industry ◽  
Human Intervention ◽  
Process Automation ◽  
Vision Systems ◽  
Component Assembly ◽  
Assembly Error ◽  
Production Industry

Object recognition (OR) is a main capability needed by most AI vision systems. The most recent R&D on this domain has been gaining incredible ground in numerous ways. OR has a variety of uses. In this paper we talk about applications of OR system in manufacturing industry. In recent era scenario increased level of process automation in production industry also demands process automation of quality examination with lesser human intervention.

Robot Process Automation (RPA) and Its Future

2022 ◽  
pp. 35-58
Author(s):  
Ozge Doguc
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computer Vision ◽  
Customer Satisfaction ◽  
Human Behavior ◽  
The Other ◽  
Human Intervention ◽  
Process Automation ◽  
Robotic Process Automation ◽  
Human Agent

Many software automation techniques have been developed in the last decade to cut down cost, improve customer satisfaction, and reduce errors. Robotic process automation (RPA) has become increasingly popular recently. RPA offers software robots (bots) that can mimic human behavior. Attended robots work in tandem with humans and can operate while the human agent is active on the computer. On the other hand, unattended robots operate behind locked screens and are designed to execute automations that don't require any human intervention. RPA robots are equipped with artificial intelligence engines such as computer vision and machine learning, and both robot types can learn automations by recording human actions.

Robot Process Automation (RPA) and Its Future

2020 ◽  
pp. 469-492
Author(s):  
Ozge Doguc
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computer Vision ◽  
Customer Satisfaction ◽  
Human Behavior ◽  
The Other ◽  
Human Intervention ◽  
Process Automation ◽  
Robotic Process Automation ◽  
Human Agent

Many software automation techniques have been developed in the last decade to cut down cost, improve customer satisfaction, and reduce errors. Robotic process automation (RPA) has become increasingly popular recently. RPA offers software robots (bots) that can mimic human behavior. Attended robots work in tandem with humans and can operate while the human agent is active on the computer. On the other hand, unattended robots operate behind locked screens and are designed to execute automations that don't require any human intervention. RPA robots are equipped with artificial intelligence engines such as computer vision and machine learning, and both robot types can learn automations by recording human actions.

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.

ON METHODS OF OBJECT DETECTION IN VIDEO STREAMS

2017 ◽  
Vol 2 (1) ◽  
pp. 80-87
Author(s):  
Puyda V. ◽  
◽  
Stoian. A.
Keyword(s):  
Computer Vision ◽  
Object Detection ◽  
Open Source ◽  
Feature Detection ◽  
Video Stream ◽  
Object Identification ◽  
Vision Systems ◽  
Modern Computer ◽  
Computer Vision Systems ◽  
Open Source Hardware

Detecting objects in a video stream is a typical problem in modern computer vision systems that are used in multiple areas. Object detection can be done on both static images and on frames of a video stream. Essentially, object detection means finding color and intensity non-uniformities which can be treated as physical objects. Beside that, the operations of finding coordinates, size and other characteristics of these non-uniformities that can be used to solve other computer vision related problems like object identification can be executed. In this paper, we study three algorithms which can be used to detect objects of different nature and are based on different approaches: detection of color non-uniformities, frame difference and feature detection. As the input data, we use a video stream which is obtained from a video camera or from an mp4 video file. Simulations and testing of the algoritms were done on a universal computer based on an open-source hardware, built on the Broadcom BCM2711, quad-core Cortex-A72 (ARM v8) 64-bit SoC processor with frequency 1,5GHz. The software was created in Visual Studio 2019 using OpenCV 4 on Windows 10 and on a universal computer operated under Linux (Raspbian Buster OS) for an open-source hardware. In the paper, the methods under consideration are compared. The results of the paper can be used in research and development of modern computer vision systems used for different purposes. Keywords: object detection, feature points, keypoints, ORB detector, computer vision, motion detection, HSV model color

scholarly journals Improving skeleton algorithm for helping Caenorhabditis elegans trackers

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Pablo E. Layana Castro ◽  
Joan Carles Puchalt ◽  
Antonio-José Sánchez-Salmerón
Keyword(s):  
Computer Vision ◽  
Caenorhabditis Elegans ◽  
Transformation Function ◽  
Simple Solution ◽  
New Method ◽  
Vision Systems ◽  
Distance Transformation ◽  
Automatic Computer ◽  
C Elegans ◽  
Computer Vision Systems

AbstractOne of the main problems when monitoring Caenorhabditis elegans nematodes (C. elegans) is tracking their poses by automatic computer vision systems. This is a challenge given the marked flexibility that their bodies present and the different poses that can be performed during their behaviour individually, which become even more complicated when worms aggregate with others while moving. This work proposes a simple solution by combining some computer vision techniques to help to determine certain worm poses and to identify each one during aggregation or in coiled shapes. This new method is based on the distance transformation function to obtain better worm skeletons. Experiments were performed with 205 plates, each with 10, 15, 30, 60 or 100 worms, which totals 100,000 worm poses approximately. A comparison of the proposed method was made to a classic skeletonisation method to find that 2196 problematic poses had improved by between 22% and 1% on average in the pose predictions of each worm.

Computer Vision Tagging the Metropolitan Museum of Art's Collection

2021 ◽  
Vol 14 (3) ◽  
pp. 1-17
Author(s):  
Elena Villaespesa ◽  
Seth Crider
Keyword(s):  
Computer Vision ◽  
Selection Criteria ◽  
Object Image ◽  
Metropolitan Museum ◽  
Museum Collections ◽  
Vision Systems ◽  
Computer Vision Systems ◽  
Similarities And Differences ◽  
Ibm Watson

Computer vision algorithms are increasingly being applied to museum collections to identify patterns, colors, and subjects by generating tags for each object image. There are multiple off-the-shelf systems that offer an accessible and rapid way to undertake this process. Based on the highlights of the Metropolitan Museum of Art's collection, this article examines the similarities and differences between the tags generated by three well-known computer vision systems (Google Cloud Vision, Amazon Rekognition, and IBM Watson). The results provide insights into the characteristics of these taxonomies in terms of the volume of tags generated for each object, their diversity, typology, and accuracy. In consequence, this article discusses the need for museums to define their own subject tagging strategy and selection criteria of computer vision tools based on their type of collection and tags needed to complement their metadata.

Accelerated HOG+SVM for object recognition

2021 ◽  
Keyword(s):  
Computer Vision ◽  
Object Recognition ◽  
International Symposium ◽  
Fast Track ◽  
Industrial Applications ◽  
Electronic Imaging ◽  
Intelligent Robotics

Fast track article for IS&T International Symposium on Electronic Imaging 2021: Intelligent Robotics and Industrial Applications using Computer Vision 2021 proceedings.

scholarly journals Algorithm for recognition of details in the machine vision systems at automation of assembly processes

2018 ◽  
Vol 224 ◽  
pp. 01088 ◽  
Author(s):  
Yaroslav Kulkov ◽  
Arkady Zhiznyakov ◽  
Denis Privezentsev
Keyword(s):  
Computer Vision ◽  
Experimental Research ◽  
Mean Square ◽  
Robot Arm ◽  
Vision Systems ◽  
Binary Images ◽  
Image Contour ◽  
Computer Vision Systems ◽  
Assembly Operations ◽  
Unknown Object

The aim is an experimental research on the flat objects recognition using dimensionless marks of the contours of their binary images and determining the possibility of applying this method in computer vision systems of assembly robots. The main problem with the automation of assembly operations is the recognition of parts for the subsequent picking up of the robot arm. The basis for the formation of attribute vectors is the characteristics of the image contour. Recognition of a class of an unknown object consists in receipt of its contour, calculation of primary parameters and forming of a vector of dimensionless marks. Further mean square deviations of its vector of dimensionless marks from all reference are calculated. The minimum value of a deviation will specify probable belonging to the corresponding class.

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