An Efficient Bar Code Image Recognition Algorithm for Sorting System

The further development of catalytic elements has been plagued by activation and binary problems. The automatic shift model that has emerged in recent years helps components achieve full range. However, the detection data still remains unstable in the shift area (7%∼13%). This paper proposes a Catalytic Combustion and Thermal Conductivity (CCTC) model for the specified range, which can be explained from two aspects based on the existing methods. On the one hand, it uses iterative location search to process heterogeneous data, judges the prediction position of data points, and then gives weight evaluation. On the other hand, it corrects the abnormal points, determines the abnormal points in the horizontal direction, and gives the replacement value through the data of adjacent points. The experimental results show that the CCTC model reduces the sum of variance from 17 of the automatic shift model to 13, and the comparison of experimental variance is reduced by 23%. In the full-scale real-time data, the experimental variance of CCTC model and automatic shift model is reduced by 18%. In conclusion, CCTC is a cross section stability framework for full-scale methane measurement, in which the specified heterogeneous combination and anomaly point correction methods improve the stability.

Section: Related Work 21 Related Work Of Activate Problemmentioning

confidence: 99%

The Fusion Model of Catalytic Combustion and Thermal Conductivity

Lin¹,

Li²,

Dong³

et al. 2023

“…The obtained image Eq. ( 1) of the object can be considered as some combinatorial configuration [21,30,32,41], which is formed from the elements of the base set, using…”

Section: Related Workmentioning

confidence: 99%

Cluster Representation of the Structural Description of Images for Effective Classification

Daradkeh¹,

Gorokhovatskyi²,

Tvoroshenko³

et al. 2022

The problem of image recognition in the computer vision systems is being studied. The results of the development of efficient classification methods, given the figure of processing speed, based on the analysis of the segment representation of the structural description in the form of a set of descriptors are provided. We propose three versions of the classifier according to the following principles: "object-etalon", "object descriptor-etalon" and "vector description of the object-etalon", which are not similar in level of integration of researched data analysis. The options for constructing clusters over the whole set of descriptions of the etalon database, separately for each of the etalons, as well as the optimal method to compare sets of segment centers for the etalons and object, are implemented. An experimental rating of the efficiency of the created classifiers in terms of productivity, processing time, and classification quality has been realized of the applied. The proposed methods classify the set of etalons without error. We have formed the inference about the efficiency of classification approaches based on segment centers. The time of image processing according to the developed methods is hundreds of times less than according to the traditional one, without reducing the accuracy.

“…Recently, the researchers have focused more on the development of the methods that provide effective application. Due to the multidimensional and spatial nature of the image signal, the most popular approaches for solving this problem are the statistical approaches [1][2][3]13,[18][19][20] and approaches related to spatial data analysis, which are aimed at simplifying processing [1,8,9,12]. Specialized neural network software is being developed, based on the preliminary creation and training of the classification neural network within some fixed image database [11,12,21].…”

Section: Related Workmentioning

confidence: 99%

Classification of Images Based on a System of Hierarchical Features

Daradkeh¹,

Gorokhovatskyi²,

Tvoroshenko³

et al. 2022

The results of the development of the new fast-speed method of classification images using a structural approach are presented. The method is based on the system of hierarchical features, based on the bitwise data distribution for the set of descriptors of image description. The article also proposes the use of the spatial data processing apparatus, which simplifies and accelerates the classification process. Experiments have shown that the time of calculation of the relevance for two descriptions according to their distributions is about 1000 times less than for the traditional voting procedure, for which the sets of descriptors are compared. The introduction of the system of hierarchical features allows to further reduce the calculation time by 2-3 times while ensuring high efficiency of classification. The noise immunity of the method to additive noise has been experimentally studied. According to the results of the research, the marginal degree of the hierarchy of features for reliable classification with the standard deviation of noise less than 30 is the 8-bit distribution. Computing costs increase proportionally with decreasing bit distribution. The method can be used for application tasks where object identification time is critical.