Application of computer vision in the automatic identification and classification of woven fabric weave patterns

An important feature of the Minoan culture is the pottery of Kamares style, that documents the Cretan cultural production between the first half of the 2nd millennium BC. This high level painted production, characterized by the combination of several diverse motifs, presents an enormous decorative repertoire. The extraordinary variety of combinations between elementary motifs according to a complex visual syntax makes interesting the automatic identification of the motifs, particularly upon potsherds. A complete pipeline to accomplish this task is still a challenge to Computer Vision and Pattern Recognition. Starting from a digital image ROI identification, motif extraction, robust contour detection should be performed to obtain a bag of digital shapes. In a second phase each of the extracted shapes has to be classified according to prototypes in a database produced by an expert. The co-occurrence of the different shapes in a specimen will, in turn, be used to help the archaeologists in the cultural and even chronological setting.

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Automatic Classification of Decorative Patterns in the Minoan Pottery of Kamares Style

Pattern Recognition and Signal Processing in Archaeometry ◽

10.4018/978-1-60960-786-9.ch006 ◽

2011 ◽

pp. 186-211

Author(s):

Filippo Stanco ◽

Davide Tanasi ◽

Giuseppe Claudio Guarnera ◽

Giovanni Gallo

Keyword(s):

Computer Vision ◽

Pattern Recognition ◽

Cultural Production ◽

Contour Detection ◽

Automatic Identification ◽

Second Phase ◽

Different Shapes ◽

Motif Extraction ◽

High Level

An important feature of the Minoan culture is the pottery of Kamares style, that documents the Cretan cultural production between the first half of the 2nd millennium BC. This high level painted production, characterized by the combination of several diverse motifs, presents an enormous decorative repertoire. The extraordinary variety of combinations between elementary motifs according to a complex visual syntax makes interesting the automatic identification of the motifs, particularly upon potsherds. A complete pipeline to accomplish this task is still a challenge to Computer Vision and Pattern Recognition. Starting from a digital image ROI identification, motif extraction, robust contour detection should be performed to obtain a bag of digital shapes. In a second phase each of the extracted shapes has to be classified according to prototypes in a database produced by an expert. The co-occurrence of the different shapes in a specimen will, in turn, be used to help the archaeologists in the cultural and even chronological setting.

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Geometric and Topological Bases of a New Classification of Wood Vascular Tissues Part 1: Shape and Arrangement Classifications of Vessels

Sustainability ◽

10.3390/su13147545 ◽

2021 ◽

Vol 13 (14) ◽

pp. 7545

Author(s):

Nikolai Bardarov ◽

Vladislav Todorov ◽

Nicole Christoff

Keyword(s):

Computer Vision ◽

Detailed Analysis ◽

Tree Species ◽

Complete Solution ◽

New Classification ◽

Topological Characteristics ◽

Definition Of ◽

Wood Science ◽

Quantitative Indicators

The need to identify wood by its anatomical features requires a detailed analysis of all the elements that make it up. This is a significant problem of structural wood science, the most general and complete solution of which is yet to be sought. In recent years, increasing attention has been paid to the use of computer vision methods to automate processes such as the detection, identification, and classification of different tissues and different tree species. The more successful use of these methods in wood anatomy requires a more precise and comprehensive definition of the anatomical elements, according to their geometric and topological characteristics. In this article, we conduct a detailed analysis of the limits of variation of the location and grouping of vessels in the observed microscopic samples. The present development offers criteria and quantitative indicators for defining the terms shape, location, and group of wood tissues. It is proposed to differentiate the quantitative indicators of the vessels depending on their geometric and topological characteristics. Thus, with the help of computer vision technics, it will be possible to establish topological characteristics of wood vessels, the extraction of which would be used to develop an algorithm for the automatic classification of tree species.

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Automatic identification and quantification of dense microcracks in high-performance fiber-reinforced cementitious composites through deep learning-based computer vision

Cement and Concrete Research ◽

10.1016/j.cemconres.2021.106532 ◽

2021 ◽

Vol 148 ◽

pp. 106532

Author(s):

Pengwei Guo ◽

Weina Meng ◽

Yi Bao

Keyword(s):

Computer Vision ◽

Deep Learning ◽

High Performance ◽

Cementitious Composites ◽

Automatic Identification ◽

Fiber Reinforced ◽

High Performance Fiber ◽

Fiber Reinforced Cementitious Composites ◽

Identification And Quantification

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A Computer-vision-based classification of road surface roughness grade using Machine Learning Techniques

International Journal of Highway Engineering ◽

10.7855/ijhe.2021.23.4.075 ◽

2021 ◽

Vol 23 (4) ◽

pp. 75-81

Author(s):

Young Jae Lee ◽

Sung Il Jeon ◽

Robin Eunju Kim

Keyword(s):

Machine Learning ◽

Computer Vision ◽

Surface Roughness ◽

Road Surface ◽

Machine Learning Techniques ◽

Learning Techniques ◽

Road Surface Roughness

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Automatic recognition and classification of microseismic waveforms based on computer vision

Tunnelling and Underground Space Technology ◽

10.1016/j.tust.2021.104327 ◽

2022 ◽

Vol 121 ◽

pp. 104327

Author(s):

Jiaming Li ◽

Shibin Tang ◽

Kunyao Li ◽

Shichao Zhang ◽

Liexian Tang ◽

...

Keyword(s):

Computer Vision ◽

Automatic Recognition

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Multiclass classification of dry beans using computer vision and machine learning techniques

Computers and Electronics in Agriculture ◽

10.1016/j.compag.2020.105507 ◽

2020 ◽

Vol 174 ◽

pp. 105507

Author(s):

Murat Koklu ◽

Ilker Ali Ozkan

Keyword(s):

Machine Learning ◽

Computer Vision ◽

Multiclass Classification ◽

Machine Learning Techniques ◽

Dry Beans ◽

Learning Techniques

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A Comparative Study on Application of Computer Vision and Fluorescence Imaging Spectroscopy for Detection of Huanglongbing Citrus Disease in the USA and Brazil

Journal of Spectroscopy ◽

10.1155/2013/841738 ◽

2013 ◽

Vol 2013 ◽

pp. 1-6 ◽

Cited By ~ 13

Author(s):

Caio B. Wetterich ◽

Ratnesh Kumar ◽

Sindhuja Sankaran ◽

José Belasque Junior ◽

Reza Ehsani ◽

...

Keyword(s):

Computer Vision ◽

Fluorescence Imaging ◽

Imaging Spectroscopy ◽

Texture Features ◽

Support Vector ◽

Infected Leaf ◽

Segmented Images ◽

The Usa ◽

Citrus Disease

The overall objective of this work was to develop and evaluate computer vision and machine learning technique for classification of Huanglongbing-(HLB)-infected and healthy leaves using fluorescence imaging spectroscopy. The fluorescence images were segmented using normalized graph cut, and texture features were extracted from the segmented images using cooccurrence matrix. The extracted features were used as an input into the classifier, support vector machine (SVM). The classification results were evaluated based on classification accuracies and number of false positives and false negatives. The results indicated that the SVM could classify HLB-infected leaf fluorescence intensities with up to 90% classification accuracy. Though the fluorescence intensities from leaves collected in Brazil and the USA were different, the method shows potential for detecting HLB.

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