Collaborative Supervision of Machine Vision Systems: Breaking a Sequential Bottleneck in the Supervised Learning Process

2003 ◽  
pp. 619-628
Author(s):  
Steve Drew ◽  
Sven Venema ◽  
Phil Sheridan ◽  
Chengzheng Sun
Keyword(s):  
Machine Vision ◽  
Supervised Learning ◽  
Learning Process ◽  
Vision Systems

Classification and Recognition of Electronic Components Based on Improved Cooperative Semi-supervised Learning Algorithm

2020 ◽  
Vol 14 ◽  
Author(s):  
Dan Luo
Keyword(s):  
Deep Learning ◽  
Machine Vision ◽  
Supervised Learning ◽  
Image Recognition ◽  
Production Efficiency ◽  
Learning Algorithm ◽  
Electronic Components ◽  
Electron Device ◽  
Actual Recognition ◽  
The Subject

Background: As known that the semi-supervised algorithm is a classical algorithm in semi-supervised learning algorithm. Methods: In the paper, it proposed improved cooperative semi-supervised learning algorithm, and the algorithm process is presented in detailed, and it is adopted to predict unlabeled electronic components image. Results: In the experiments of classification and recognition of electronic components, it show that through the method the accuracy the proposed algorithm in electron device image recognition can be significantly improved, the improved algorithm can be used in the actual recognition process . Conclusion: With the continuous development of science and technology, machine vision and deep learning will play a more important role in people's life in the future. The subject research based on the identification of the number of components is bound to develop towards the direction of high precision and multi-dimension, which will greatly improve the production efficiency of electronic components industry.

scholarly journals A strategy learning model for autonomous agents based on classification

2015 ◽  
Vol 25 (3) ◽  
pp. 471-482 ◽  
Author(s):  
Bartłomiej Śnieżyński
Keyword(s):  
Reinforcement Learning ◽  
Supervised Learning ◽  
Learning Process ◽  
Autonomous Agents ◽  
Good Alternative ◽  
Learning Model ◽  
Learning Method ◽  
Complex Environments ◽  
Agent Based ◽  
Proposed Model

AbstractIn this paper we propose a strategy learning model for autonomous agents based on classification. In the literature, the most commonly used learning method in agent-based systems is reinforcement learning. In our opinion, classification can be considered a good alternative. This type of supervised learning can be used to generate a classifier that allows the agent to choose an appropriate action for execution. Experimental results show that this model can be successfully applied for strategy generation even if rewards are delayed. We compare the efficiency of the proposed model and reinforcement learning using the farmer-pest domain and configurations of various complexity. In complex environments, supervised learning can improve the performance of agents much faster that reinforcement learning. If an appropriate knowledge representation is used, the learned knowledge may be analyzed by humans, which allows tracking the learning process

scholarly journals Machine Vision Systems for Plant Growth Measurement and Modeling

2006 ◽  
Vol 44 (3) ◽  
pp. 181-187 ◽  
Author(s):  
Ta-Te LIN ◽  
Chung-Fang CHIEN ◽  
Wen-Chi LIAO ◽  
Kuo-Chi CHUNG ◽  
Jen-Min CHANG
Keyword(s):  
Plant Growth ◽  
Machine Vision ◽  
Vision Systems ◽  
Growth Measurement ◽  
Plant Growth Measurement

scholarly journals A Functionally Separate Autoencoder

2020 ◽  
Author(s):  
Jinxin Wei
Keyword(s):  
Neural Network ◽  
Supervised Learning ◽  
Negative Feedback ◽  
Learning Process ◽  
Inverse Function ◽  
Convolution Neural Network ◽  
Training Process ◽  
New Knowledge

<p><b>According to kids’ learning process, an auto</b><b>-</b><b>encoder</b><b> is designed</b><b> which can be split into two parts. The two parts can work well separately.The top half is an abstract network which is trained by supervised learning and can be used to classify and regress. The bottom half is a concrete network which is accomplished by inverse function and trained by self-supervised learning. It can generate the input of abstract network from concept or label. The network can achieve its intended functionality through testing by mnist dataset and convolution neural network.</b><b> R</b><b>ound function</b><b> is added between the abstract network and concrete network in order</b><b> to get the the representative generation of class.</b><b> T</b><b>he generation ability </b><b> can be increased </b><b>by adding jump connection and negative feedback. At last, the characteristics of </b><b>the</b><b> network</b><b> is discussed</b><b>. </b><b>T</b><b>he input can </b><b>be </b><b>change</b><b>d </b><b>to any form by encoder and then change it back by decoder through inverse function. The concrete network can be seen as the memory stored by the parameters.</b><b> </b><b>Lethe is that when new knowledge input,</b><b> </b><b>the training process make</b><b>s</b><b> the parameter</b><b>s</b><b> change.</b><b></b></p>

scholarly journals Fusing Data Processing in the Construction of Machine Vision Systems in Robotic Complexes

2019 ◽  
Vol 224 ◽  
pp. 04009 ◽  
Author(s):  
Aleksandr Zelensky ◽  
Evgenii Semenishchev ◽  
Aleksandr Gavlicky ◽  
Irina Tolstova ◽  
V. Frantc
Keyword(s):  
Machine Vision ◽  
Electromagnetic Interference ◽  
Visual Information ◽  
Vision Systems ◽  
Noise Component ◽  
New Approach ◽  
Common Causes ◽  
Robotic Cells ◽  
Combined Images

The development of machine vision systems is based on the analysis of visual information recorded by sensitive matrices. This information is most often distorted by the presence of interfering factors represented by a noise component. The common causes of the noise include imperfect sensors, dust and aerosols, used ADCs, electromagnetic interference, and others. The presence of these noise components reduces the quality of the subsequent analysis. To implement systems that allow operating in the presence of a noise, a new approach, which allows parallel processing of data obtained in various electromagnetic ranges, has been proposed. The primary area of application of the approach are machine vision systems used in complex robotic cells. The use of additional data obtained by a group of sensors allows the formation of arrays of usefull information that provide successfull optimization of operations. The set of test data shows the applicability of the proposed approach to combined images in machine vision systems.

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