scholarly journals Food Intake Vision-Based Recognition System via Histogram of Oriented Gradients and Support Vector Machine for Persons With Alzheimer’s Disease

2021 ◽  
Vol 16 (4) ◽  
pp. 0-0
Keyword(s):  
Support Vector Machine ◽  
False Positive ◽  
Recognition System ◽  
Experimental Simulation ◽  
Upper Body ◽  
Body Region ◽  
Support Vector ◽  
Histogram Of Oriented Gradients ◽  
Template Image ◽  
The Subject

Due to cognitive decline, individuals with Alzheimer’s often suffer from malnutrition, forgetting to eat, even if food is presented. Therefore, assistance with feeding is needed. In this paper a vision-based system for monitoring of eating patterns is presented. Upper Body Region (UBR) is detected using Viola-Jones method, a histogram of oriented gradients (HOG) is generated for feature extraction, and a support vector machine (SVM) is used to distinguish eating versus non-eating. To reduce false positive results, Haar-like features are used to detect hands while moving between served food and mouth within the identified upper body region (UBR). A combined template image (CTI) method is proposed in this work to eliminate false positive hand detections where 30 hand eating posture images have been selected and combined into one template image. Matching implemented using CTI is 2.86 times faster than matching the subject to the 30 images separately. Experimental simulation used 33 videos of 163840 frames indicates that the proposed method achieves a high accuracy of 90.65%.

scholarly journals Deteksi Pemain Basket Terklasifikasi Berbasis Histogram of Oriented Gradients

Jurnal INFORM ◽  
2018 ◽  
Vol 3 (1) ◽  
pp. 6-11
Author(s):  
Nisa ul Hafidhoh ◽  
Septian Enggar Sukmana
Keyword(s):  
Support Vector Machine ◽  
False Positive ◽  
False Positive Rate ◽  
True Positive Rate ◽  
Support Vector ◽  
Histogram Of Oriented Gradients ◽  
True Positive ◽  
Positive Rate

Pada olahraga basket jaman modern ini, kebutuhan analisis pergerakan pemain pada calon tim lawan olahraga basket perlu didukung oleh teknologi informasi yang mampu mengupayakan sistem yang otomatis. Analisis pergerakan pemain yang otomatis perlu didukung oleh sistem deteksi pemain yang handal dan akurat sehingga pemetaan pergerakan dapat dilakukan secara optimal. Tujuan dari penelitian ini adalah untuk mengembangkan metode Histogram of Oriented Gradients (HOG) menjadi sebuah metode deteksi yang handal untuk kasus deteksi pemain basket pada media. Tantangan pada penelitian ini adalah deteksi pemain tidak hanya pada saat berjalan dan berlari namun juga pada saat melompat. Untuk memperkuat fokus dan konsistensi terhadap objek yang terdeteksi, pemanfaatan metode klasifikasi Support Vector Machine (SVM) digunakan melalui kolaborasi terhadap HOG descriptor serta warna kostum pemain sehingga pembeda tim dari masing-masing pemain juga dapat dikenali. Tingkat akurasi dari evaluasi yang dihasilkan adalah 92% untuk true positive rate dan 40% untuk false positive rate.

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.

GESTURE RECOGNITION SYSTEM FOR NIGERIAN TRIBAL GREETING POSTURES USING SUPPORT VECTOR MACHINE

2020 ◽  
Vol 5 (2) ◽  
pp. 609
Author(s):  
Segun Aina ◽  
Kofoworola V. Sholesi ◽  
Aderonke R. Lawal ◽  
Samuel D. Okegbile ◽  
Adeniran I. Oluwaranti
Keyword(s):  
Support Vector Machine ◽  
Gesture Recognition ◽  
Recognition Rate ◽  
Recognition Task ◽  
Recognition System ◽  
Human Interaction ◽  
Support Vector ◽  
System A ◽  
Extraction Algorithm ◽  
Gaussian Blur

This paper presents the application of Gaussian blur filters and Support Vector Machine (SVM) techniques for greeting recognition among the Yoruba tribe of Nigeria. Existing efforts have considered different recognition gestures. However, tribal greeting postures or gestures recognition for the Nigerian geographical space has not been studied before. Some cultural gestures are not correctly identified by people of the same tribe, not to mention other people from different tribes, thereby posing a challenge of misinterpretation of meaning. Also, some cultural gestures are unknown to most people outside a tribe, which could also hinder human interaction; hence there is a need to automate the recognition of Nigerian tribal greeting gestures. This work hence develops a Gaussian Blur – SVM based system capable of recognizing the Yoruba tribe greeting postures for men and women. Videos of individuals performing various greeting gestures were collected and processed into image frames. The images were resized and a Gaussian blur filter was used to remove noise from them. This research used a moment-based feature extraction algorithm to extract shape features that were passed as input to SVM. SVM is exploited and trained to perform the greeting gesture recognition task to recognize two Nigerian tribe greeting postures. To confirm the robustness of the system, 20%, 25% and 30% of the dataset acquired from the preprocessed images were used to test the system. A recognition rate of 94% could be achieved when SVM is used, as shown by the result which invariably proves that the proposed method is efficient.

scholarly journals Design of Fractal Features-Based Partial Discharge Pattern Recognition using Multi Support Vector Machine Method

2018 ◽  
Vol 159 ◽  
pp. 02048
Author(s):  
Rahayu ◽  
G.T. Anuraga ◽  
H. Prasetia ◽  
Umar Khayam
Keyword(s):  
Pattern Recognition ◽  
Support Vector Machine ◽  
High Voltage ◽  
Partial Discharge ◽  
Diagnostic System ◽  
Diagnostic Technique ◽  
Recognition System ◽  
Support Vector ◽  
Machine Method ◽  
Pattern Recognition System

Partial Discharge (PD) is one of the causes of insulation deteriorisation mode and impacts on the reliability of high voltage equipment. Therefore, PD measurement is used for diagnostic technique of high voltage equipment. Diagnostic output of high voltage equipment contain information about PD type, PD cause, PD location and PD severity. after identification, a proper preventive maintenance pattern can be performed. Therefore PD pattern recognition system is very important on PD diagnostic system to recognize the PD pattern and determine the level of hazard that occurs in specimen object or high voltage equipment‥ In this paper, PD pattern recognition system is designed with fractal geometry approach and support vector machine (SVM) algorithm. The coding and programming of graphical user interface of the application is done. Each PD type and hazard level on various insulating materials (solid, liquid and gas) have the dimensions of the fractal and the lacunarity. The type of PD (void, corona) and its danger level (bad, fair and good) can be identified with the support vector machine (SVM)

Smart Affect Recognition System for Real-Time Biometric Surveillance Using Hybrid Features and Multilayered Binary Structured Support Vector Machine

2020 ◽  
Author(s):  
Thamba Meshach W ◽  
Hemajothi S ◽  
Mary Anita E A
Keyword(s):  
Support Vector Machine ◽  
Confusion Matrix ◽  
Recognition System ◽  
Learning Technologies ◽  
Support Vector ◽  
Affect Recognition ◽  
Svm Classifier ◽  
Ecg Signal ◽  
Hybrid Features ◽  
Structured Support Vector Machine

Abstract Human affect recognition (HAR) using images of facial expression and electrocardiogram (ECG) signal plays an important role in predicting human intention. This system improves the performance of the system in applications like the security system, learning technologies and health care systems. The primary goal of our work is to recognize individual affect states automatically using the multilayered binary structured support vector machine (MBSVM), which efficiently classify the input into one of the four affect classes, relax, happy, sad and angry. The classification is performed efficiently by designing an efficient support vector machine (SVM) classifier in multilayer mode operation. The classifier is trained using the 8-fold cross-validation method, which improves the learning of the classifier, thus increasing its efficiency. The classification and recognition accuracy is enhanced and also overcomes the drawback of ‘facial mimicry’ by using hybrid features that are extracted from both facial images (visual elements) and physiological signal ECG (signal features). The reliability of the input database is improved by acquiring the face images and ECG signals experimentally and by inducing emotions through image stimuli. The performance of the affect recognition system is evaluated using the confusion matrix, obtaining the classification accuracy of 96.88%.

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