scholarly journals Insight Natural Vision Processing like Engineering for Machine Vision

2018 ◽  
pp. 1-4
Keyword(s):  
Computer Vision ◽  
Machine Vision ◽  
Error Detection ◽  
Artificial Vision ◽  
Vision Processing ◽  
Natural Vision ◽  
Error Detection And Correction ◽  
Quality Check

Artificial Vision or Computer vision or machine vision are the terms which maximum authors defined as Vision for calibration, inspection, error detection and correction, quality check or anything else all process electronically and engineering based on Natural human which is unique. Hence with this sense simply term is Vision Engineering, because Artificial, Computer or Machine all designed electronically with similarities. I have shown in my modeling with help of two models how we can engineer it.

Data normalization for training and analysis of the MaskRCNN model using the k-means method for computer vision of smart refrigerator

2021 ◽  
Vol 4 ◽  
pp. 74-80
Author(s):  
M. G. Dorrer ◽  
◽  
A.E. Alekhina ◽  
Keyword(s):  
Computer Vision ◽  
Semantic Segmentation ◽  
Correct Choice ◽  
Training Sample ◽  
Artificial Vision ◽  
K Value ◽  
High K ◽  
Real World Datasets ◽  
Segmentation Problem

This paper proposes using the k-means method for the controlled adjustment of the training sample for semantic image segmentation in the artificial vision of a smart refrigerator. To solve this problem, a new two-stage architecture for computer vision is proposed. In the proposed architecture, various sets of settings for optimizing the contrast of images are used to classify pixels according to their belonging to fragments of the studied image. Extensive experimental evaluation shows that the proposed method has critical advantages over existing work. Firstly, the obtained pixel classes can be directly clustered into semantic groups using k-means. Secondly, the method can be used for additional training of artificial intelligence in solving the semantic segmentation problem. The developers propose an approach to the correct choice of the number k of centroids to obtain good quality clusters, which is difficult to determine at a high k value. To overcome the problem of initializing the k-means method, an incremental k-means clustering method is proposed, which improves the quality of clusters to reduce the sum of squared errors. Comprehensive experiments have been carried out compared to the traditional k-means algorithm and its new versions to evaluate the performance of the proposed method on synthetically generated datasets and some real-world datasets.

scholarly journals Optical Comparison Operation for 8-Bit QPSK-Modulated Signal by Using Serially-Cascaded Delay Line Interferometer

2018 ◽  
Vol 8 (9) ◽  
pp. 1440 ◽  
Author(s):  
Yohei Aikawa
Keyword(s):  
Error Detection ◽  
Communication Systems ◽  
Delay Line ◽  
Hamming Distance ◽  
Code Word ◽  
Modulation Format ◽  
Comparison Operation ◽  
Error Detection And Correction ◽  
All Optical ◽  
Shift Keying

An all-optical comparator is desirable to realize large-capacity, fully-transparent, and energy-efficient communication systems, as it is considered to be a fundamental component to perform most of the operations, including packet switching, label recognition, error detection and correction, and so on. However, most of the previous studies have been confined to the on–off keying (OOK) modulation format, not phase-shift keying (PSK) modulation. In this paper, the author provides a novel optical comparator designed for quadrature PSK (QPSK)-modulated signal, which comprises a code word with 8-bit length, using a serially-cascaded delay line interferometer. The proposed comparator yields constellations having the information of a Hamming distance based on the designed code, when several patterns of QPSK signal are injected into the comparator. The paper experimentally demonstrates the feasibility of the optical comparison operation for 8-bit QPSK-modulated return-to-zero (RZ) signal at 10 Gbaud.

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