Forensic Detection Based on Color Label and Oriented Texture Feature

2020 ◽  
pp. 383-395
Author(s):  
Tingge Zhu ◽  
Jiangbin Zheng ◽  
Mingchen Feng ◽  
Ying Liu ◽  
Wei Liu ◽  
...  
Keyword(s):  
Texture Feature ◽  
Oriented Texture

scholarly journals Research on college gymnastics teaching model based on multimedia image and image texture feature analysis

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Ying Wu ◽  
Jikun Liu
Keyword(s):  
Human Body ◽  
Rapid Development ◽  
Texture Feature ◽  
Texture Features ◽  
Dynamics Simulation ◽  
Image Texture ◽  
Whole Body ◽  
Database Modeling ◽  
Rigid Model ◽  
The Subject

AbstractWith the rapid development of gymnastics technology, novel movements are also emerging. Due to the emergence of various complicated new movements, higher requirements are put forward for college gymnastics teaching. Therefore, it is necessary to combine the multimedia simulation technology to construct the human body rigid model and combine the image texture features to display the simulation image in texture form. In the study, GeBOD morphological database modeling was used to provide the data needed for the modeling of the whole-body human body of the joint and used for dynamics simulation. Simultaneously, in order to analyze and summarize the technical essentials of the innovative action, this experiment compared and analyzed the hem stage of the cross-headstand movement of the subject and the hem stage of the 180° movement. Research shows that the method proposed in this paper has certain practical effects.

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 Effects of Interobserver Variability on 2D and 3D CT- and MRI-Based Texture Feature Reproducibility of Cartilaginous Bone Tumors

2021 ◽  
Author(s):  
Salvatore Gitto ◽  
Renato Cuocolo ◽  
Ilaria Emili ◽  
Laura Tofanelli ◽  
Vito Chianca ◽  
...  
Keyword(s):  
Bone Tumors ◽  
Intraclass Correlation ◽  
Texture Feature ◽  
Region Of Interest ◽  
Unenhanced Ct ◽  
Unenhanced Computed Tomography ◽  
2D And 3D ◽  
Feature Stability ◽  
Ct And Mri ◽  
T2 Weighted Images

AbstractThis study aims to investigate the influence of interobserver manual segmentation variability on the reproducibility of 2D and 3D unenhanced computed tomography (CT)- and magnetic resonance imaging (MRI)-based texture analysis. Thirty patients with cartilaginous bone tumors (10 enchondromas, 10 atypical cartilaginous tumors, 10 chondrosarcomas) were retrospectively included. Three radiologists independently performed manual contour-focused segmentation on unenhanced CT and T1-weighted and T2-weighted MRI by drawing both a 2D region of interest (ROI) on the slice showing the largest tumor area and a 3D ROI including the whole tumor volume. Additionally, a marginal erosion was applied to both 2D and 3D segmentations to evaluate the influence of segmentation margins. A total of 783 and 1132 features were extracted from original and filtered 2D and 3D images, respectively. Intraclass correlation coefficient ≥ 0.75 defined feature stability. In 2D vs. 3D contour-focused segmentation, the rates of stable features were 74.71% vs. 86.57% (p < 0.001), 77.14% vs. 80.04% (p = 0.142), and 95.66% vs. 94.97% (p = 0.554) for CT and T1-weighted and T2-weighted images, respectively. Margin shrinkage did not improve 2D (p = 0.343) and performed worse than 3D (p < 0.001) contour-focused segmentation in terms of feature stability. In 2D vs. 3D contour-focused segmentation, matching stable features derived from CT and MRI were 65.8% vs. 68.7% (p = 0.191), and those derived from T1-weighted and T2-weighted images were 76.0% vs. 78.2% (p = 0.285). 2D and 3D radiomic features of cartilaginous bone tumors extracted from unenhanced CT and MRI are reproducible, although some degree of interobserver segmentation variability highlights the need for reliability analysis in future studies.

Recognition of natural ice types on in-service glass insulators based on texture feature descriptor

2017 ◽  
Vol 24 (1) ◽  
pp. 535-542 ◽  
Author(s):  
Lin Yang ◽  
Xiaolan Jiang ◽  
Yanpeng Hao ◽  
Licheng Li ◽  
Hao Li ◽  
...  
Keyword(s):  
Texture Feature ◽  
Feature Descriptor

GLCM texture feature reduction for EEG spectrogram image using PCA

2010 ◽  
Author(s):  
Mahfuzah Mustafa ◽  
Mohd Nasir Taib ◽  
Zunairah Hj. Murat ◽  
Sahrim Lias
Keyword(s):  
Texture Feature ◽  
Feature Reduction
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