Classification of Citrus Peel Diseases Using Color Texture Feature Analysis

2008 ◽  
Author(s):  
Jianwei Qin ◽  
Thomas F Burks ◽  
Dae Gwan Kim ◽  
Duke M Bulanon
Keyword(s):  
Texture Feature ◽  
Feature Analysis ◽  
Citrus Peel ◽  
Color Texture

Classification of tea grains based upon image texture feature analysis under different illumination conditions

2013 ◽  
Vol 115 (2) ◽  
pp. 226-231 ◽  
Author(s):  
Amit Laddi ◽  
Shashi Sharma ◽  
Amod Kumar ◽  
Pawan Kapur
Keyword(s):  
Texture Feature ◽  
Image Texture ◽  
Feature Analysis

scholarly journals The Classification of Medicinal Plant Leaves Based on Multispectral and Texture Feature Using Machine Learning Approach

Agronomy ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 263
Author(s):  
Samreen Naeem ◽  
Aqib Ali ◽  
Christophe Chesneau ◽  
Muhammad H. Tahir ◽  
Farrukh Jamal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Medicinal Plant ◽  
Texture Feature ◽  
Stevia Rebaudiana ◽  
Ocimum Sanctum ◽  
Multi Layer Perceptron ◽  
Plant Leaves ◽  
Chi Square ◽  
Lemon Balm

This study proposes the machine learning based classification of medical plant leaves. The total six varieties of medicinal plant leaves-based dataset are collected from the Department of Agriculture, The Islamia University of Bahawalpur, Pakistan. These plants are commonly named in English as (herbal) Tulsi, Peppermint, Bael, Lemon balm, Catnip, and Stevia and scientifically named in Latin as Ocimum sanctum, Mentha balsamea, Aegle marmelos, Melissa officinalis, Nepeta cataria, and Stevia rebaudiana, respectively. The multispectral and digital image dataset are collected via a computer vision laboratory setup. For the preprocessing step, we crop the region of the leaf and transform it into a gray level format. Secondly, we perform a seed intensity-based edge/line detection utilizing Sobel filter and draw five regions of observations. A total of 65 fused features dataset is extracted, being a combination of texture, run-length matrix, and multi-spectral features. For the feature optimization process, we employ a chi-square feature selection approach and select 14 optimized features. Finally, five machine learning classifiers named as a multi-layer perceptron, logit-boost, bagging, random forest, and simple logistic are deployed on an optimized medicinal plant leaves dataset, and it is observed that the multi-layer perceptron classifier shows a relatively promising accuracy of 99.01% as compared to the competition. The distinct classification accuracy by the multi-layer perceptron classifier on six medicinal plant leaves are 99.10% for Tulsi, 99.80% for Peppermint, 98.40% for Bael, 99.90% for Lemon balm, 98.40% for Catnip, and 99.20% for Stevia.

scholarly journals Longitudinal T2 Mapping and Texture Feature Analysis in the Detection and Monitoring of Experimental Post-Traumatic Cartilage Degeneration

Life ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 201
Author(s):  
Marc Sebastian Huppertz ◽  
Justus Schock ◽  
Karl Ludger Radke ◽  
Daniel Benjamin Abrar ◽  
Manuel Post ◽  
...  
Keyword(s):  
T2 Mapping ◽  
Energy Levels ◽  
Texture Feature ◽  
Basic Research ◽  
Cartilage Degeneration ◽  
Quantitative Mri ◽  
Degenerative Changes ◽  
Feature Analysis ◽  
Cartilage Defects ◽  
Cartilage Injuries

Background: Traumatic cartilage injuries predispose articulating joints to focal cartilage defects and, eventually, posttraumatic osteoarthritis. Current clinical-standard imaging modalities such as morphologic MRI fail to reliably detect cartilage trauma and to monitor associated posttraumatic degenerative changes with oftentimes severe prognostic implications. Quantitative MRI techniques such as T2 mapping are promising in detecting and monitoring such changes yet lack sufficient validation in controlled basic research contexts. Material and Methods: 35 macroscopically intact cartilage samples obtained from total joint replacements were exposed to standardized injurious impaction with low (0.49 J, n = 14) or high (0.98 J, n = 14) energy levels and imaged before and immediately, 24 h, and 72 h after impaction by T2 mapping. Contrast, homogeneity, energy, and variance were quantified as features of texture on each T2 map. Unimpacted controls (n = 7) and histologic assessment served as reference. Results: As a function of impaction energy and time, absolute T2 values, contrast, and variance were significantly increased, while homogeneity and energy were significantly decreased. Conclusion: T2 mapping and texture feature analysis are sensitive diagnostic means to detect and monitor traumatic impaction injuries of cartilage and associated posttraumatic degenerative changes and may be used to assess cartilage after trauma to identify “cartilage at risk”.

scholarly journals The Feasibility Study of Megavoltage Computed Tomographic (MVCT) Image for Texture Feature Analysis

2018 ◽  
Vol 8 ◽  
Author(s):  
Jiabing Gu ◽  
Jian Zhu ◽  
Qingtao Qiu ◽  
Yungang Wang ◽  
Tong Bai ◽  
...  
Keyword(s):  
Feasibility Study ◽  
Texture Feature ◽  
Feature Analysis ◽  
Computed Tomographic

scholarly journals Variable Selection from Image Texture Feature for Automatic Classification of Concrete Surface Voids

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Ziting Zhao ◽  
Tong Liu ◽  
Xudong Zhao
Keyword(s):  
Variable Selection ◽  
Gabor Filter ◽  
Texture Feature ◽  
Concrete Surface ◽  
Automatic Classification ◽  
Ensemble Classification ◽  
Image Texture ◽  
Inspection Time ◽  
Selection Strategy

Machine learning plays an important role in computational intelligence and has been widely used in many engineering fields. Surface voids or bugholes frequently appearing on concrete surface after the casting process make the corresponding manual inspection time consuming, costly, labor intensive, and inconsistent. In order to make a better inspection of the concrete surface, automatic classification of concrete bugholes is needed. In this paper, a variable selection strategy is proposed for pursuing feature interpretability, together with an automatic ensemble classification designed for getting a better accuracy of the bughole classification. A texture feature deriving from the Gabor filter and gray-level run lengths is extracted in concrete surface images. Interpretable variables, which are also the components of the feature, are selected according to a presented cumulative voting strategy. An ensemble classifier with its base classifier automatically assigned is provided to detect whether a surface void exists in an image or not. Experimental results on 1000 image samples indicate the effectiveness of our method with a comparable prediction accuracy and model explicable.

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