Machine vision for field-level wood identification

IAWA Journal ◽  
2020 ◽  
Vol 41 (4) ◽  
pp. 681-698 ◽  
Author(s):  
Bruno Geike de Andrade ◽  
Vanessa Maria Basso ◽  
João Vicente de Figueiredo Latorraca
Keyword(s):  
Intelligent Systems ◽  
Wood Anatomy ◽  
Support Vector ◽  
Technical Knowledge ◽  
Macroscopic Description ◽  
Automated Identification ◽  
Vector Machines ◽  
Texture Characteristics ◽  
Identification Of Species ◽  
Occurrence Matrix

Abstract Identifying wood species using wood anatomy is an important tool for various purposes. The traditionally used method is based on the macroscopic description of the physical and anatomical characteristics of the wood. This requires that the identifier has thorough technical knowledge about wood anatomy. A possible alternative to this task is to use intelligent systems capable of identifying species through an analysis of digital images. In this work, 21 species were used to generate a set of 2000 macroscopic images. These were produced with a smartphone under field conditions, from samples manually polished with knives. Texture characteristics obtained through a gray level co-occurrence matrix were used in developing classifiers based on support vector machines. The best model achieved a 97.7% accuracy. Our study concluded that the automated identification of species can be performed in the field in a practical, simple and precise way.

Diabetes Diagnosis System Based on Support Vector Machines Trained by Vortex Optimization Algorithm

2018 ◽  
pp. 203-218 ◽  
Author(s):  
Sadi Fuat Cankaya ◽  
Ibrahim Arda Cankaya ◽  
Tuncay Yigit ◽  
Arif Koyun
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Support Vector Machines ◽  
Optimization Algorithm ◽  
Intelligent Systems ◽  
Support Vector ◽  
Diabetes Diagnosis ◽  
Diagnosis System ◽  
Vector Machines ◽  
Expert Support

Artificial intelligence is widely enrolled in different types of real-world problems. In this context, developing diagnosis-based systems is one of the most popular research interests. Considering medical service purposes, using such systems has enabled doctors and other individuals taking roles in medical services to take instant, efficient expert support from computers. One cannot deny that intelligent systems are able to make diagnosis over any type of disease. That just depends on decision-making infrastructure of the formed intelligent diagnosis system. In the context of the explanations, this chapter introduces a diagnosis system formed by support vector machines (SVM) trained by vortex optimization algorithm (VOA). As a continuation of previously done works, the research considered here aims to diagnose diabetes. The chapter briefly gives information about details of the system and findings reached after using the developed system.

CO-OCCURRENCE PROBABILITIES AND WAVELET-BASED SPOOF FINGERPRINT DETECTION

2009 ◽  
Vol 09 (02) ◽  
pp. 171-199 ◽  
Author(s):  
SHANKAR BHAUSAHEB NIKAM ◽  
SUNEETA AGARWAL
Keyword(s):  
Nearest Neighbor ◽  
Texture Features ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Network Support ◽  
Wavelet Energy ◽  
Texture Characteristics ◽  
Energy Signature ◽  
Occurrence Matrix ◽  
Fingerprint Database

Perspiration phenomenon is very significant to detect the liveness of a finger. However, it requires two consecutive fingerprints to notice perspiration, and therefore may not be suitable for real time authentications. Some other methods in the literature need extra hardware to detect liveness. To alleviate these problems, in this paper, to detect liveness a new texture-based method using only the first fingerprint is proposed. It is based on the observation that real and spoof fingerprints exhibit different texture characteristics. Textural measures based on gray level co-occurrence matrix (GLCM) are used to characterize fingerprint texture. This is based on structural, orientation, roughness, smoothness and regularity differences of diverse regions in a fingerprint image. Wavelet energy signature is also used to obtain texture details. Dimensionalities of feature sets are reduced by Sequential Forward Floating Selection (SFFS) method. GLCM texture features and wavelet energy signature are independently tested on three classifiers: neural network, support vector machine and K-nearest neighbor. Finally, two best classifiers are fused using the "Sum Rule''. Fingerprint database consisting of 185 real, 90 Fun-Doh and 150 Gummy fingerprints is created. Multiple combinations of materials are used to create casts and moulds of spoof fingerprints. Experimental results indicate that, the new liveness detection method is very promising, as it needs only one fingerprint and no extra hardware to detect vitality.

scholarly journals Improvised Admissible Kernel Function for Support Vector Machines in Banach Space for Multiclass Data

2013 ◽  
Vol 11 (2) ◽  
pp. 2273-2278
Author(s):  
Sangeetha Rajendran ◽  
B. Kalpana
Keyword(s):  
Banach Space ◽  
Learning Theory ◽  
Intelligent Systems ◽  
Learning Algorithm ◽  
Kernel Functions ◽  
Learning Ability ◽  
Support Vector ◽  
Training Time ◽  
Vector Machines ◽  
Optimal Kernel

Classification based on supervised learning theory is one of the most significant tasks frequently accomplished by so-called Intelligent Systems. Contrary to the traditional classification techniques that are used to validate or contradict a predefined hypothesis, kernel based classifiers offer the possibility to frame new hypotheses using statistical learning theory (Sangeetha and Kalpana, 2010). Support Vector Machine (SVM) is a standard kernel based learning algorithm where it improves the learning ability through experience. It is highly accurate, robust and optimal kernel based classification technique that is well-suited to many real time applications. In this paper, kernel functions related to Hilbert space and Banach Space are explained. Here, the experimental results are carried out using benchmark multiclass datasets which are taken from UCI Machine Learning Repository and their performance are compared using various metrics like support vector, support vector percentage, training time and accuracy.

Two-Stage Automated Operational Modal Analysis Based on Power Spectrum Density Transmissibility and Support-Vector Machines

2021 ◽  
pp. 2150068
Author(s):  
Zhi-Wei Chen ◽  
Kui-Ming Liu ◽  
Wang-Ji Yan ◽  
Jian-Lin Zhang ◽  
Wei-Xin Ren
Keyword(s):  
Support Vector Machines ◽  
Power Spectrum ◽  
Modal Analysis ◽  
Power Spectrum Density ◽  
Support Vector ◽  
Svm Classifier ◽  
Automated Identification ◽  
Two Stage ◽  
Vector Machines ◽  
Spectrum Density

Power spectrum density transmissibility (PSDT) functions have attracted widespread attention in operational modal analysis (OMA) because of their robustness to excitations. However, the selection of the peaks and stability axes are still subjective and requires further investigation. To this end, this study took advantage of PSDT functions and support-vector machines (SVMs) to propose a two-stage automated modal identification method. In the first stage, the automated identification of peaks is achieved by introducing the peak slope (PS) as a critical index and determining its threshold using the SVM classifier. In the second stage, the automated identification of the stability axis is achieved by introducing the relative difference coefficients (RDCs) of the modal parameters as indicators and determining their thresholds using the SVM classifier. To verify its feasibility and accuracy, the proposed method was applied to an ASCE-benchmark structure in the laboratory and in a high-rise building installed with a structural health monitoring system (SHMS). The results showed that the automated identification method could effectively eliminate spurious modes and accurately identify the closely spaced modes. The proposed method can be automatically applied without manual intervention, and it is robust to noise. It is promising for application to the real-time condition evaluation of civil structures installed with SHMSs.

Particulate matter characterization by gray level co-occurrence matrix based support vector machines

2012 ◽  
Vol 223-224 ◽  
pp. 94-103 ◽  
Author(s):  
K. Manivannan ◽  
P. Aggarwal ◽  
V. Devabhaktuni ◽  
A. Kumar ◽  
D. Nims ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Support Vector Machines ◽  
Support Vector ◽  
Gray Level ◽  
Vector Machines ◽  
Occurrence Matrix

scholarly journals Denoising and SAR Image Classification with K-SVD Algorithm

2018 ◽  
Vol 7 (3.3) ◽  
pp. 36 ◽  
Author(s):  
D V.R Mohan ◽  
I Rambabu ◽  
B Harish
Keyword(s):  
Support Vector ◽  
Gray Level ◽  
Infrared Sensors ◽  
Sar Images ◽  
Feature Spaces ◽  
Vector Machines ◽  
And Performance ◽  
Occurrence Matrix ◽  
Value Decomposition

Synthetic Aperture Radar (SAR) is not only having the characteristic of obtaining images during all-day, all-weather, but also provides object information which is distinctive from visible and infrared sensors. but, SAR images have more speckles noise and fewer bands. This paper propose a method for denoising, feature extraction and classification of SAR images. Initially the image was denoised using K-Singular Value Decomposition (K-SVD) algorithm. Then the Gray Level Histogram (GLH) and Gray Level Co-occurrence Matrix (GLCM) are used for extraction of features. Secondly, the extracted feature vectors from the first step were combined using the correlation analysis to decrease the dimensionality of the feature spaces. Thirdly, Classification of SAR images was done in Sparse Representations Classification (SRC) and Support Vector Machines (SVMs). The results indicate that the performance of the introduce SAR classification method is good. The above mentioned classifications techniques are enhanced and performance parameters are computed using MATLAB 2014a software.  

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