Automatic Detection of the Cracks on the Concrete Railway Sleepers

2019 ◽  
Vol 33 (09) ◽  
pp. 1955010
Author(s):  
Seyed Amir Hossein Tabatabaei ◽  
Ahmad Delforouzi ◽  
Muhammad Hassan Khan ◽  
Tim Wesener ◽  
Marcin Grzegorzek
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Intelligent Systems ◽  
Crack Detection ◽  
Image Data ◽  
Experimental Comparison ◽  
Pattern Recognition Techniques ◽  
Comparison Results ◽  
Recognition Systems ◽  
Computer Recognition

A vision-based method for detecting the cracks in the concrete sleepers of the railway tracks will be introduced in this paper. The method is able to detect and partially classify the cracks of the concrete sleepers in two successive steps based on the image processing and pattern recognition techniques. The method has been implemented on the acquired image data frames followed by the analysis, experimental, comparison results and evaluation. The presented results are reasonable which indicates the goodness of the introduced method. The preliminary results of this work have been presented in [A. Delforouzi, A. H. Tabatabaei, M. H. Khan and M. Grzegorzek, A vision-based method for automatic crack detection in railway sleepers, in Kurzynski, M., Wozniak, M., Burduk, R. (eds.), Proceedings of the 10th International Conference on Computer Recognition Systems CORES 2017, Polanica Zdroj, Poland. CORES 2017. Advances in Intelligent Systems and Computing, Vol. 578 (Springer, Cham, 2018), pp. 130–139, doi: 10.1007/978-3-319-59162-9_14].

scholarly journals Face Recognition Based on Statistical Texture Features

2021 ◽  
Vol 7 (1) ◽  
pp. 10-15
Author(s):  
Lama Akram Ibrahim ◽  
Nasser Nasser ◽  
Majd Ali
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Face Recognition ◽  
Processing Time ◽  
Facial Recognition ◽  
Texture Features ◽  
Memory Consumption ◽  
Methods And Techniques ◽  
Recognition Systems ◽  
Occurrence Matrix

Facial recognition has attracted the attention of researchers and has been one of the most prominent topics in the fields of image processing and pattern recognition since 1990. This resulted in a very large number of recognition methods and techniques with the aim of increasing the accuracy and robustness of existing systems. Many techniques have been developed to address the challenges and reliable recognition systems have been reached but require considerable processing time, suffer from high memory consumption and are relatively complex. The focus of this paper is on extracting subset of descriptors (less correlated and less calculations) from the co-occurrence matrix with the goal of enhancing the performance of Haralick’s descriptors. Improvements are achieved by adding the image pre-processing and selecting the proper method according to the database problem and by extracting features from image local regions.

ON THE RELATIONSHIPS BETWEEN STATISTICAL PATTERN RECOGNITION AND ARTIFICIAL NEURAL NETWORKS

1991 ◽  
Vol 05 (04) ◽  
pp. 655-661 ◽  
Author(s):  
C. H. CHEN
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Artificial Neural Networks ◽  
Statistical Pattern Recognition ◽  
Experimental Comparison ◽  
Comparison Results ◽  
Statistical Pattern ◽  
Artificial Neural

Although statistical pattern recognition and artificial neural networks were initially developed independently, they are now closely related. Such relationships are examined in detail in this paper. Some experimental comparison results are also presented.

Inspection of tire tread defects using image processing and pattern recognition techniques

1993 ◽  
Author(s):  
Penny Chen ◽  
Gary D. Shubinsky ◽  
Kwan-Hwa Jan ◽  
Chien-An Chen ◽  
Oliver Sidla ◽  
...  
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Pattern Recognition Techniques

Deep-learning-based crack detection with applications for the structural health monitoring of gas turbines

2019 ◽  
Vol 19 (5) ◽  
pp. 1440-1452 ◽  
Author(s):  
Mahtab Mohtasham Khani ◽  
Sahand Vahidnia ◽  
Leila Ghasemzadeh ◽  
Y Eren Ozturk ◽  
Mustafa Yuvalaklioglu ◽  
...  
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Gas Turbines ◽  
Crack Detection ◽  
Image Data ◽  
Classification Performance ◽  
Data Set ◽  
Structural Health ◽  
Repair Costs ◽  
Classical Image

Gas turbine maintenance requires consistent inspections of cracks and other structural anomalies. The inspections provide information regarding the overall condition of the structures and yield information for estimating structural health and repair costs. Various image processing techniques have been used in the past to address the problem of automated visual crack detection with varying degrees of success. In this work, we propose a novel crack detection framework that utilizes techniques from both classical image processing and deep learning methodologies. The main contribution of this work is demonstrating that applying filters to image data in the pre-processing phase can significantly boost the classification performance of a convolutional neural network–based model. The developed architecture outperforms compared works by yielding a 96.26% classification accuracy on a data set of cracked surface images collected from gas turbines.

Automatic Number Plate Goyal Recognition-Image Processing Technique

2016 ◽  
Vol 10 (2) ◽  
Author(s):  
Priti Rajvanshi ◽  
Vijaypal Singh Dhaka
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Pattern Recognition ◽  
Mathematical Morphology ◽  
Geometric Analysis ◽  
Processing Technique ◽  
Image Processing Technique ◽  
License Plate ◽  
License Plate Recognition ◽  
Recognition Systems

Automated Number Plate Recognition (ANPR) is also known as Automated License Plate Recognition (ALPR).Automatic Number Plate Recognition or ANPR is a technology that uses pattern recognition to ‘read’ vehiclenumber plates. The design of ANPR systems is a field of research in artificial intelligence and pattern recognition. The main goal of this paper is to study algorithmic and mathematical principles of automatic number plate recognition systems. ANPR can be used to store the images captured by the cameras as well as the text from the number plate and using mathematical morphology methods to detect the edges of the rectangular plate. Mathematical morphology is a part of digital image processing which is concerned withimage filtering and geometric analysis by using structuring elements (SE).

scholarly journals Advances in Computer Recognition, Image Processing and Communications

Entropy ◽  
2022 ◽  
Vol 24 (1) ◽  
pp. 108
Author(s):  
Michał Choraś ◽  
Robert Burduk ◽  
Agata Giełczyk ◽  
Rafał Kozik ◽  
Tomasz Marciniak
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Pattern Recognition ◽  
Special Issue ◽  
High Quality ◽  
Recognition Pattern ◽  
Computer Recognition

This Special Issue aimed to gather high-quality advancements in theoretical and practical aspects of computer recognition, pattern recognition, image processing and machine learning (shallow and deep), including, in particular, novel implementations of these techniques in the areas of modern telecommunications and cybersecurity [...]

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