A hybrid system for embedded machine vision using FPGAs and neural networks

2008 ◽  
Vol 20 (6) ◽  
pp. 379-394 ◽  
Author(s):  
Miguel S. Prieto ◽  
Alastair R. Allen
Keyword(s):  
Neural Networks ◽  
Machine Vision ◽  
Hybrid System

scholarly journals Data Augmentation Methods Applying Grayscale Images for Convolutional Neural Networks in Machine Vision

2021 ◽  
Vol 11 (15) ◽  
pp. 6721
Author(s):  
Jinyeong Wang ◽  
Sanghwan Lee
Keyword(s):  
Neural Networks ◽  
Machine Vision ◽  
Object Detection ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Data Augmentation ◽  
Image Data ◽  
Manufacturing Productivity ◽  
Smart Factories ◽  
Grayscale Images

In increasing manufacturing productivity with automated surface inspection in smart factories, the demand for machine vision is rising. Recently, convolutional neural networks (CNNs) have demonstrated outstanding performance and solved many problems in the field of computer vision. With that, many machine vision systems adopt CNNs to surface defect inspection. In this study, we developed an effective data augmentation method for grayscale images in CNN-based machine vision with mono cameras. Our method can apply to grayscale industrial images, and we demonstrated outstanding performance in the image classification and the object detection tasks. The main contributions of this study are as follows: (1) We propose a data augmentation method that can be performed when training CNNs with industrial images taken with mono cameras. (2) We demonstrate that image classification or object detection performance is better when training with the industrial image data augmented by the proposed method. Through the proposed method, many machine-vision-related problems using mono cameras can be effectively solved by using CNNs.

scholarly journals Nonlinear Time-Series Analysis of Pulsation of Post-AGB Stars by Genetic Algorithm/Neural Network Hybrid Systems

2000 ◽  
Vol 176 ◽  
pp. 135-136
Author(s):  
Toshiki Aikawa
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Hybrid System ◽  
Nonlinear Time Series ◽  
Photometric Data ◽  
Nonlinear Time Series Analysis ◽  
Agb Stars

AbstractSome pulsating post-AGB stars have been observed with an Automatic Photometry Telescope (APT) and a considerable amount of precise photometric data has been accumulated for these stars. The datasets, however, are still sparse, and this is a problem for applying nonlinear time series: for instance, modeling of attractors by the artificial neural networks (NN) to the datasets. We propose the optimization of data interpolations with the genetic algorithm (GA) and the hybrid system combined with NN. We apply this system to the Mackey–Glass equation, and attempt an analysis of the photometric data of post-AGB variables.

scholarly journals A STUDY ON COLOR SORTING OF TOMATOES MATURITY USING MACHINE VISION AND ARTIFICIAL NEURAL NETWORKS

2015 ◽  
Vol 93 (1) ◽  
pp. 147-161
Author(s):  
ABD EL-WAHAB S. KASSEM ◽  
MOHAMED A. SABBAH ◽  
ABED EL WAHED M. ABOUKARIMA ◽  
RABAB M. KAMEL
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Machine Vision ◽  
Artificial Neural

scholarly journals To contemplate quantitative and qualitative water features by neural networks method

2011 ◽  
Vol 48 (No. 7) ◽  
pp. 322-326 ◽  
Author(s):  
M. Neruda ◽  
R. Neruda
Keyword(s):  
Neural Networks ◽  
Fourier Series ◽  
Computer Science ◽  
Hybrid System ◽  
Back Propagation ◽  
Neural Model ◽  
Black Box ◽  
Layer Number ◽  
Accurate Forecast ◽  
Hidden Layer

An application deals with calibration of neural model and Fourier series model for Ploučnice catchment. This approach has an advantage, that the network choice is independent of other example’s parameters. Each networks, and their variants (different units and hidden layer number) can be connected in as a black box and tested independently. A Stuttgart neural simulator SNNS and a multiagent hybrid system Bang2 developed in Institute of Computer Science, AS CR have been used for testing. A perceptron network has been constructed, which was trained by back propagation method improved with a momentum term. The network is capable of an accurate forecast of the next day runoff based on the runoff and rainfall values from previous day.

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