The algorithm development for operation of a computer vision system via the OpenCV library

Sivkov, Stepan; Novikov, L. V.; Romanova, Galina; Romanova, Anastasia O.; Vaganov, Denis; Valitov, Marat; Vasiliev, Sergey

doi:10.1016/j.procs.2020.02.193

Cited by 28 publications

(9 citation statements)

References 1 publication

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“…Raspbian PI OS, a Debian‐based operating system for Raspberry Pi was used as the official operating system in this study. The software for the acquisition of the response values of each intelligent sensory sensor was developed in‐house using Python programming language in conjunction with OpenCV library, which is commonly used in machine vision (Janga & Enugala, 2020; Sivkov et al, 2020; Zhu & Cheng, 2020). And the software for the generation of the electric pulses to control the device's hardware was developed by python based on Raspberry Pi OS.…”

Section: Methodsmentioning

confidence: 99%

Development and test of a smart multisensory device for preserved eggs

Ren

Huang

Aheto

et al. 2022

J Food Process Engineering

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Preserved eggs have exceptional sensory quality traits such as pine‐needle‐like flowers on the surface, reddish‐brown and elastic albumen, dark green umami and runny yolk, ammonia smell, and so forth. In this work, a smart multisensory device equipped with a raspberry Pi, a camera, and visual sensor arrays for odor and taste perception was developed to assist in the evaluation of preserved eggs sensory quality. The feasibility of the device to nondestructively detect the maturation stages of preserved eggs during curing was conducted on preserved eggs with five different maturation stages, yielding an accuracy of 95.00%. To further increase the accuracy of identifying preserved egg brands, this study incorporated three types of intelligent sensory data. The three parts of data were combined into a two‐dimensional matrix, normalization and principal component analysis were carried out, and the linear discriminant analysis (LDA) model was finally established, achieving 91.67% accuracy for brand recognition. The results demonstrated that the device could be employed to provide rapid and accurate characterization of the sensory qualities of preserved eggs, as well as provide useful reference for smart sensory characterization of other food products. Practical Applications Processors prioritize food safety and quality, which is why producers of preserved eggs frequently face complex and time‐consuming workflows, such as the appointment of sensory evaluators to assess the quality of preserved eggs. The sensory panel, on the other hand, is prone to fatigue. This study presents a smart multi‐sensory device that can collect and analyze sensory data trends from preserved eggs at a speed and interconnectedness that human sensory panels cannot match. The device has an easy‐to‐use interface that allows for the real‐time collection of critical information about consumer acceptance of preserved eggs. The results showed that the device could be used by preserved egg producers to provide rapid and accurate sensory characterization of preserved eggs, as well as a useful reference for smart sensory characterization of other food products.

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Section: Methodsmentioning

confidence: 99%

Development and test of a smart multisensory device for preserved eggs

Ren

Huang

Aheto

et al. 2022

J Food Process Engineering

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“…In typical systems, photos and the computer vision files are stored on a pre-saved disc. This can be rendered more improved using OpenCV [5]. Developing a quick and powerful algorithm using OpenCV will guarantee real time identification of image attributes.…”

Section: Image Analytics Using Open Computer Visionmentioning

confidence: 99%

Dynamic Image Forensics and Forgery Analytics using Open Computer Vision Framework

Sumathi¹

2021

WJCMS

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The key advances in Computer Vision and Optical Image Processing are the emerging technologies nowadays in diverse fields including Facial Recognition, Biometric Verifications, Internet of Things (IoT), Criminal Investigation, Signature Identification in banking and several others. Thus, these applications use image and live video processing for facilitating different applications for analyzing and forecasting." Computer vision is used in tons of activities such as monitoring, face recognition, motion recognition, object detection, among many others. The development of social networking platforms such as Facebook and Instagram led to an increase in the volume of image data that was being generated. Use of image and video processing software is a major concern for Facebook because the photos and videos that people post to the social network are doctored images. These kind of images are frequently cited as fake and used in malevolent ways such as motivating violence and death. You need to authenticate the questionable images before take action. It is very hard to ensure photo authenticity due to the power of photo manipulations. Image formation can be determined by image forensic techniques. The technique of image duplication is used to conceal missing areas.

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“…Se diseñó el algoritmo de visión artificial teniendo los diagramas por cada paso o hito del programa que tienen ver con procesos de tratamiento de imágenes para el fotograma de las muestras (Sivkov et al, 2020), en el sistema de visión artificial se ejecuta la detección de los parámetros de la cámara, luego se inicia la cámara de video y se muestra en una interfaz gráfica.…”

Section: Materiales Y Métodosunclassified

Reconocimiento de patrones de imágenes a través de un sistema de visión artificial en MATLAB

Pinedo

López

Grández

et al. 2021

rcsi

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La visión artificial es una disciplina de la inteligencia artificial que aplica el procesamiento de imágenes para el reconocimiento de patrones, con el uso algoritmos en ambientes controlados con una cantidad de iteraciones en el procesamiento de imágenes. La proliferación de dispositivos de capturas de imágenes ha generado imágenes digitales en todo el mundo, estas imágenes contienen información que deberían ser utilizadas por las organizaciones públicas y privadas para la toma de decisiones. Los objetivos fueron mejorar el reconocimiento de patrones mediante un sistema de visión artificial, medir el proceso de reconocimiento de patrones, implementar un sistema de visión artificial y medir la relación que existe entre reconocimiento de patrones y el un sistema de visión artificial. Esta fue una investigación aplicada, de tipo cuasi experimental, con corte transversal, la población y muestra de estudio fueron 8 patrones de imágenes, la técnica fue la verificación con lista de chequeo, aplicada a 2 grupos, un grupo control y un grupo experimental. Se concluyó que el tiempo de procesamiento para el reconocimiento de 8 patrones de imágenes del grupo experimental fue de 10,75 segundos y de 67,75 segundos para el grupo control y con un grado de relación entre el reconocimiento de patrones y el sistema de visión artificial de 72 %.

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The algorithm development for operation of a computer vision system via the OpenCV library

Cited by 28 publications

References 1 publication

Development and test of a smart multisensory device for preserved eggs

Development and test of a smart multisensory device for preserved eggs

Dynamic Image Forensics and Forgery Analytics using Open Computer Vision Framework

Reconocimiento de patrones de imágenes a través de un sistema de visión artificial en MATLAB

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