Smartphone Technology Applications for Milkfish Image Segmentation Using OpenCV Library

Qashlim, Akhmad; Basri, Basri; Haeruddin, Haeruddin; Ardan, Ardan; Nurtanio, Inggrid; Ilham, Amil Ahmad

doi:10.3991/ijim.v14i08.12423

Cited by 6 publications

(4 citation statements)

References 17 publications

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“…Previous research has created many techniques with improving face emotion recognition ability. They trained a conventional neural network (CNN) to recognize face emotions in the study [6,7,8], this CNN is fed a 64*64 image as input. An input layer, five convolutional layers, three pooling layers, a fully connected layer, and an output layer make up the network.…”

Section: Related Workmentioning

confidence: 99%

Improved Methods for Automatic Facial Expression Recognition

Echoukairi¹,

Ghmary²,

Ziani³

et al. 2023

Int. J. Interact. Mob. Technol.

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Facial expressions constitute one of the most effective and instinctive methods that allow people to communicate their emotions and intentions. In this context, the both Machine Learning (ML) and Convolutional Neural Networks (CNNs) have been used for emotion recognition. Efficient recognition systems are required for good human-computer interaction. However, facial expression recognition is related to several methods that impact the performance of facial recognition systems. In this paper, we demonstrate a state-of-the-art of 65% accuracy on the FER2013 dataset by leveraging numerous techniques from recent research and we also proposed some new methods for improving accuracy by combining CNN architectures such as VGG-16 and Resnet-50 with auxiliary datasets such as JAFFE and CK. To predict emotions, we used a second approach based on geometric features and facial landmarks to calculate and transmit the feature vector to the SVM model. The results show that the ResNet50 model outperforms all other emotion prediction models in real time by maximizing.

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Section: Related Workmentioning

confidence: 99%

Improved Methods for Automatic Facial Expression Recognition

Echoukairi¹,

Ghmary²,

Ziani³

et al. 2023

Int. J. Interact. Mob. Technol.

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show abstract

“…Each frame was then converted to HSV format. To filter specific colours, the InRange function [20] was used to feed the boundary colour values to form a mask. Function Dilate [21] was used to expand maskincrease white area of the mask and get rid of possible noise.…”

Section: Fig 3 Application Flowchartmentioning

confidence: 99%

Using OpenCV for real-time image recognition through augmented reality devices

Dinara,

Evgeniy,

Vlada

2023

Proceedings of International Conference on Artificial Life and Robotics

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This article describes the peculiarities of writing libraries and the rapid development of augmented reality applications on the Unity platform and the OpenCV open-source library. The application of OpenCV functions to work with augmented reality objects is discussed in detail. On their basis, algorithms and methods were developed and an open library was created that implements the stable recognition of objects in various conditions by means of binding to the marker and their visualization on the augmented reality devices. On the basis of the developed library, a complete application was created to illustrate the possibility of applying virtual reality technologies for optical control of the assembly of radio electronic boards and for personnel training.

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“…OpenCV is an open source library, composed of a series of C languages such as C++, with interfaces such as python and MATLAB, It mainly includes three aspects: image processing, computer vision and machine learning [6,7] .It contains a wealth of image processing algorithms can complete the image recognition and processing and other tasks, Due to its powerful functions, it is widely used in industrial product testing, traffic monitoring, marine traffic and other fields [8,9] .Digital image processing is a method and technology that removes noise, enhances, restores, segments, extracts features and other processing methods and techniques for images by computers [10] .The image taken by the camera this time contains a lot of noise. The key difficulty in this particle identification is to extract the contour and calculate the average area.…”

Section: Basic Principles Of Particle Identificationmentioning

confidence: 99%

Particle image recognition in the measurement of workpiece cleanliness

Wang

et al. 2022

J. Phys.: Conf. Ser.

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Monitoring the cleaning degree of workpiece surface cleaning is a key step in high-end equipment manufacturing. The use of image recognition technology to identify the cleaning degree of workpiece surface can effectively improve the quality and efficiency of workpiece cleaning. In this paper, a particle image recognition technology based on opencv was proposed, which uses a series of methods such as grayscale, binarization, morphological processing, line contour detection, contour extraction to statistically calculate the number and average size of particles. So that the surface cleanliness of the workpiece can be determined. Judging the surface cleanliness classification according to the particle size and quantity is a precise quantitative cleaning method.

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Smartphone Technology Applications for Milkfish Image Segmentation Using OpenCV Library

Cited by 6 publications

References 17 publications

Improved Methods for Automatic Facial Expression Recognition

Improved Methods for Automatic Facial Expression Recognition

Using OpenCV for real-time image recognition through augmented reality devices

Particle image recognition in the measurement of workpiece cleanliness

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