Tree leaf feature extraction and recognition based on geometric features and Haar wavelet theory

2019 ◽  
Vol 12 (4) ◽  
pp. 477-483 ◽  
Author(s):  
Hongbo Mu ◽  
Haiming Ni ◽  
Miaomiao Zhang ◽  
Yang Yang ◽  
Dawei Qi
Keyword(s):  
Feature Extraction ◽  
Haar Wavelet ◽  
Geometric Features ◽  
Wavelet Theory ◽  
Tree Leaf

OFFLINE YORÙBÁ HANDWRITTEN WORD RECOGNITION USING GEOMETRIC FEATURE EXTRACTION AND SUPPORT VECTOR MACHINE CLASSIFIER

2020 ◽  
Vol 5 (2) ◽  
pp. 504
Author(s):  
Matthias Omotayo Oladele ◽  
Temilola Morufat Adepoju ◽  
Olaide ` Abiodun Olatoke ◽  
Oluwaseun Adewale Ojo
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Word Recognition ◽  
Support Vector Machine Classifier ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Support Vector ◽  
Geometric Features ◽  
Total Length ◽  
Yoruba Language

Yorùbá language is one of the three main languages that is been spoken in Nigeria. It is a tonal language that carries an accent on the vowel alphabets. There are twenty-five (25) alphabets in Yorùbá language with one of the alphabets a digraph (GB). Due to the difficulty in typing handwritten Yorùbá documents, there is a need to develop a handwritten recognition system that can convert the handwritten texts to digital format. This study discusses the offline Yorùbá handwritten word recognition system (OYHWR) that recognizes Yorùbá uppercase alphabets. Handwritten characters and words were obtained from different writers using the paint application and M708 graphics tablets. The characters were used for training and the words were used for testing. Pre-processing was done on the images and the geometric features of the images were extracted using zoning and gradient-based feature extraction. Geometric features are the different line types that form a particular character such as the vertical, horizontal, and diagonal lines. The geometric features used are the number of horizontal lines, number of vertical lines, number of right diagonal lines, number of left diagonal lines, total length of all horizontal lines, total length of all vertical lines, total length of all right slanting lines, total length of all left-slanting lines and the area of the skeleton. The characters are divided into 9 zones and gradient feature extraction was used to extract the horizontal and vertical components and geometric features in each zone. The words were fed into the support vector machine classifier and the performance was evaluated based on recognition accuracy. Support vector machine is a two-class classifier, hence a multiclass SVM classifier least square support vector machine (LSSVM) was used for word recognition. The one vs one strategy and RBF kernel were used and the recognition accuracy obtained from the tested words ranges between 66.7%, 83.3%, 85.7%, 87.5%, and 100%. The low recognition rate for some of the words could be as a result of the similarity in the extracted features.

Feature Extraction I: Geometric Features and Transformations

2021 ◽  
pp. 165-194
Author(s):  
Abhijit S. Pandya ◽  
Robert B. Macy
Keyword(s):  
Feature Extraction ◽  
Geometric Features

scholarly journals Feature Extraction from 3D Point Cloud Data Based on Discrete Curves

2013 ◽  
Vol 2013 ◽  
pp. 1-19 ◽  
Author(s):  
Yi An ◽  
Zhuohan Li ◽  
Cheng Shao
Keyword(s):  
Feature Extraction ◽  
Autonomous Navigation ◽  
Point Cloud ◽  
Geometric Features ◽  
3D Point Cloud ◽  
Point Cloud Data ◽  
Principal Curvatures ◽  
Cloud Data ◽  
Discrete Curves ◽  
Threshold Setting

Reliable feature extraction from 3D point cloud data is an important problem in many application domains, such as reverse engineering, object recognition, industrial inspection, and autonomous navigation. In this paper, a novel method is proposed for extracting the geometric features from 3D point cloud data based on discrete curves. We extract the discrete curves from 3D point cloud data and research the behaviors of chord lengths, angle variations, and principal curvatures at the geometric features in the discrete curves. Then, the corresponding similarity indicators are defined. Based on the similarity indicators, the geometric features can be extracted from the discrete curves, which are also the geometric features of 3D point cloud data. The threshold values of the similarity indicators are taken from[0,1], which characterize the relative relationship and make the threshold setting easier and more reasonable. The experimental results demonstrate that the proposed method is efficient and reliable.

scholarly journals Wavelet Theory: Applications of the Wavelet

2021 ◽  
Author(s):  
Mohammed S. Mechee ◽  
Zahir M. Hussain ◽  
Zahrah Ismael Salman
Keyword(s):  
Wavelet Transform ◽  
Differential Equations ◽  
Numerical Solutions ◽  
Haar Wavelet ◽  
Computational Time ◽  
Frequency Content ◽  
Wavelet Base ◽  
Wavelet Theory ◽  
Emden Equation ◽  
Fractional Differential

In this Chapter, continuous Haar wavelet functions base and spline base have been discussed. Haar wavelet approximations are used for solving of differential equations (DEs). The numerical solutions of ordinary differential equations (ODEs) and fractional differential equations (FrDEs) using Haar wavelet base and spline base have been discussed. Also, Haar wavelet base and collocation techniques are used to approximate the solution of Lane-Emden equation of fractional-order showing that the applicability and efficacy of Haar wavelet method. The numerical results have clearly shown the advantage and the efficiency of the techniques in terms of accuracy and computational time. Wavelet transform studied as a mathematical approach and the applications of wavelet transform in signal processing field have been discussed. The frequency content extracted by wavelet transform (WT) has been effectively used in revealing important features of 1D and 2D signals. This property proved very useful in speech and image recognition. Wavelet transform has been used for signal and image compression.

scholarly journals CLASSIFICATION OF AIRBORNE LASER SCANNING DATA USING GEOMETRIC MULTI-SCALE FEATURES AND DIFFERENT NEIGHBOURHOOD TYPES

2016 ◽  
Vol III-3 ◽  
pp. 169-176 ◽  
Author(s):  
R. Blomley ◽  
B. Jutzi ◽  
M. Weinmann
Keyword(s):  
Feature Extraction ◽  
Laser Scanning ◽  
Benchmark Dataset ◽  
Airborne Laser Scanning ◽  
Geometric Features ◽  
Multi Scale ◽  
Airborne Laser ◽  
Single Scale ◽  
Detailed Evaluation

In this paper, we address the classification of airborne laser scanning data. We present a novel methodology relying on the use of complementary types of geometric features extracted from multiple local neighbourhoods of different scale and type. To demonstrate the performance of our methodology, we present results of a detailed evaluation on a standard benchmark dataset and we show that the consideration of multi-scale, multi-type neighbourhoods as the basis for feature extraction leads to improved classification results in comparison to single-scale neighbourhoods as well as in comparison to multi-scale neighbourhoods of the same type.

scholarly journals Klasifikasi Rambu Lalu Lintas Menggunakan Ekstraksi Ciri Wavelet Dan Jarak Euclidean

2019 ◽  
Vol 3 (1) ◽  
pp. 26-35
Author(s):  
Vincentius Abdi Gunawan ◽  
Ignatia Imelda Fitriani ◽  
Leonardus Sandy Ade Putra
Keyword(s):  
Feature Extraction ◽  
Euclidean Distance ◽  
Daily Life ◽  
Human Mobility ◽  
Haar Wavelet ◽  
Surrounding Area ◽  
Traffic Signs ◽  
Road Users ◽  
Traffic Area ◽  
Very High

Driving is one of the human activities in which daily life is often done.  Driving can be done by land, air, and sea.  Human mobility in driving is very high on land routes using various means of transportation.  For the sake of smooth driving, roads are often equipped with traffic signs in each traffic area.  Traffic signs are a means for road users to provide information and guidance for motorists about the situation in the surrounding area.  The number of motorists who lack awareness of the knowledge of reading traffic signs is one of the biggest causes of accidents in Indonesia.  So that a system is needed that can help in recognizing traffic signs, especially prohibited signs.  The system designed using Haar Wavelet feature extraction and Euclidean distance as a classification.  From the data that has been tested, the level of recognition in reading traffic signs is prohibited by 92%.

scholarly journals Dendritic Cells Feature Extraction using Geometric Features and 1D Fourier Descriptors

2017 ◽  
Vol 7 (4) ◽  
pp. 1334
Author(s):  
Anis Azwani Muhd Suberi ◽  
Wan Nurshazwani Wan Zakaria ◽  
Razali Tomari ◽  
Nurmiza Othman ◽  
Nik Farhan Nik Fuad
Keyword(s):  
Dendritic Cells ◽  
Feature Extraction ◽  
Fourier Descriptors ◽  
Geometric Features
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