Feature Extraction Methods for Character Recognition

Chatterji, B. N.

doi:10.1080/02564602.1986.11437879

Cited by 28 publications

(4 citation statements)

References 59 publications

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“…Many studies [ 27 , 28 ] have proposed various feature extraction methods, such as Gabor filters, local binary pattern, Haar, and histograms of oriented gradients (HOG). HOG is a feature descriptor used in computer vision and image processing for the purpose of object detection.…”

Section: Methodsmentioning

confidence: 99%

Detection of Adulteration in Canola Oil by Using GC-IMS and Chemometric Analysis

Chen

et al. 2018

International Journal of Analytical Chemistry

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The aim of the present study was to detect adulteration of canola oil with other vegetable oils such as sunflower, soybean, and peanut oils and to build models for predicting the content of adulterant oil in canola oil. In this work, 147 adulterated samples were detected by gas chromatography-ion mobility spectrometry (GC-IMS) and chemometric analysis, and two methods of feature extraction, histogram of oriented gradient (HOG) and multiway principal component analysis (MPCA), were combined to pretreat the data set. The results evaluated by canonical discriminant analysis (CDA) algorithm indicated that the HOG-MPCA-CDA model was feasible to discriminate the canola oil adulterated with other oils and to precisely classify different levels of each adulterant oil. Partial least square analysis (PLS) was used to build prediction models for adulterant oil level in canola oil. The model built by PLS was proven to be effective and precise for predicting adulteration with good regression (R2>0.95) and low errors (RMSE ≤ 3.23).

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Section: Methodsmentioning

confidence: 99%

Detection of Adulteration in Canola Oil by Using GC-IMS and Chemometric Analysis

Chen

et al. 2018

International Journal of Analytical Chemistry

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“…One-way analysis of variance (ANOVA), using for example a Tukey's test, is often applied to identify volatile compounds which exhibit significant differences, commonly quantified at a 5% significance level (p ≤ 0.05) [59]. Various other methods, such as Gabor filters, local binary pattern, Haar, and histograms of oriented gradients (HOG), have been proposed for feature extraction [136,137]. Chen and coworkers applied MPCA and HOG for feature extraction and data reduction of MCC-IMS data, with subsequent canonical discriminant analysis for the generation of nonlinear boundaries, for the successful quantification of the adulteration degree of canola oil.…”

Section: Comparison Of Nts and Targeted Strategiesmentioning

confidence: 99%

(Mass Spectrometric) Non Target Screening–Techniques and Strategies

2023

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“…One-way analysis of variance (ANOVA), using for example a Tukey’s test, is often applied to identify volatile compounds which exhibit significant differences, commonly quantified at a 5% significance level ( p ≤ 0.05) [ 59 ]. Various other methods, such as Gabor filters, local binary pattern, Haar, and histograms of oriented gradients (HOG), have been proposed for feature extraction [ 136 , 137 ]. Chen and coworkers applied MPCA and HOG for feature extraction and data reduction of MCC-IMS data, with subsequent canonical discriminant analysis for the generation of nonlinear boundaries, for the successful quantification of the adulteration degree of canola oil.…”

Section: Comparison Of Nts and Targeted Strategiesmentioning

confidence: 99%

Non-Targeted Screening Approaches for Profiling of Volatile Organic Compounds Based on Gas Chromatography-Ion Mobility Spectroscopy (GC-IMS) and Machine Learning

Capitain

Weller

2021

Molecules

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Due to its high sensitivity and resolving power, gas chromatography-ion mobility spectrometry (GC-IMS) is a powerful technique for the separation and sensitive detection of volatile organic compounds. It is a robust and easy-to-handle technique, which has recently gained attention for non-targeted screening (NTS) approaches. In this article, the general working principles of GC-IMS are presented. Next, the workflow for NTS using GC-IMS is described, including data acquisition, data processing and model building, model interpretation and complementary data analysis. A detailed overview of recent studies for NTS using GC-IMS is included, including several examples which have demonstrated GC-IMS to be an effective technique for various classification and quantification tasks. Lastly, a comparison of targeted and non-targeted strategies using GC-IMS are provided, highlighting the potential of GC-IMS in combination with NTS.

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Feature Extraction Methods for Character Recognition

Cited by 28 publications

References 59 publications

Detection of Adulteration in Canola Oil by Using GC-IMS and Chemometric Analysis

Detection of Adulteration in Canola Oil by Using GC-IMS and Chemometric Analysis

(Mass Spectrometric) Non Target Screening–Techniques and Strategies

Non-Targeted Screening Approaches for Profiling of Volatile Organic Compounds Based on Gas Chromatography-Ion Mobility Spectroscopy (GC-IMS) and Machine Learning

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