Moment Invariant Features Extraction for Hand Gesture Recognition of Sign Language based on SIBI

Rahagiyanto, Angga; Basuki, Achmad; Sigit, Riyanto

doi:10.24003/emitter.v5i1.173

Cited by 9 publications

(6 citation statements)

References 21 publications

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“…This movement is slightly different from BISINDO [27] and considerably different from SIBI [28]. The main difference is that the Quantum Maki movement does not only use hand movements, but also other limb movements in particular circumstances.…”

Section: A the Sign Movement Of Quantum Makimentioning

confidence: 81%

A Hybrid Background Subtraction and Artificial Neural Networks for Movement Recognition in Memorizing Quran

Prabuwono¹,

Maula²,

Usino³

et al. 2018

ijacsa

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Movement change beyond the duration of time and the variations of object appearance becomes an interesting topic for research in computer vision. Object behavior can be recognized through movement change on video. During the recognition of object behavior, the target and the trace of an object in a video must be determined in the sequence of frames. To date, the existence of object on a video has been widely used in different areas such as supervision, robotics, agriculture, health, sports, education, and traffic. This research focuses on the field of education by recognizing the movement of Quantum Maki Quran memorization through a video. The purpose of this study is to enhance the existing computer vision technique in detecting the Quantum Maki Quran memorization movement on a video. It combines the Background Subtraction method and Artificial Neural Networks; and evaluates the combination to optimize the system accuracy. Background Subtraction is used as object detection method and Back propagation in Artificial Neural Networks is used as object classification. Nine videos are obtained by three different volunteers. These nine videos are divided into six training and three testing data. The experimental result shows that the percentage of accuracy system is 91.67%. It can be concluded that there are several factors influencing the accuracy, such as video capturing factors, video improvements, the models, feature extraction and parameter definitions during the Artificial Neural Networks training.

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Section: A the Sign Movement Of Quantum Makimentioning

confidence: 81%

A Hybrid Background Subtraction and Artificial Neural Networks for Movement Recognition in Memorizing Quran

Prabuwono¹,

Maula²,

Usino³

et al. 2018

ijacsa

View full text Add to dashboard Cite

show abstract

“…The raw signals from the inertial sensors were inserted into a convolutional neural network (CNN), achieving a 98% accuracy level. Other works presented the recognition for Thai Sign Language (TSL) [ 30 ] and Indonesian Sign Language (ISL) [ 31 , 32 ]; however, in their methodologies, some aspects were not explained and described, such as the number of subjects from which the data were collected.…”

Section: Related Workmentioning

confidence: 99%

Analysis of Influence of Segmentation, Features, and Classification in sEMG Processing: A Case Study of Recognition of Brazilian Sign Language Alphabet

Mendes

Freitas

Campos

et al. 2020

Sensors

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Sign Language recognition systems aid communication among deaf people, hearing impaired people, and speakers. One of the types of signals that has seen increased studies and that can be used as input for these systems is surface electromyography (sEMG). This work presents the recognition of a set of alphabet gestures from Brazilian Sign Language (Libras) using sEMG acquired from an armband. Only sEMG signals were used as input. Signals from 12 subjects were acquired using a MyoTM armband for the 26 signs of the Libras alphabet. Additionally, as the sEMG has several signal processing parameters, the influence of segmentation, feature extraction, and classification was considered at each step of the pattern recognition. In segmentation, window length and the presence of four levels of overlap rates were analyzed, as well as the contribution of each feature, the literature feature sets, and new feature sets proposed for different classifiers. We found that the overlap rate had a high influence on this task. Accuracies in the order of 99% were achieved for the following factors: segments of 1.75 s with a 12.5% overlap rate; the proposed set of four features; and random forest (RF) classifiers.

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“…Metode yang digunakan dalam penelitian ini terdiri dari preprocessing suara, yaitu ekstraksi fitur menggunakan Prosodic Features, lalu untuk mengidentifikasi pola dari suatu sampel suara tangisan bayi menggunakan Moments of Distribution dan menerapkan algoritma klasifikasi suara tangisan bayi menggunakan metode K-Nearest Neighbour dimana validation sampling yang digunakan adalah Percentage Rate dan Leave One Out [7].…”

Section: Metodologi Penelitianunclassified

Klasifikasi Suara Tangisan Bayi Berdasarkan Prosodic Features Menggunakan Metode Moments of Distribution dan K-Nearest Neighbours

Prayogi

Rizqi

Fahrudin

2019

teknika

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Bagi orang dewasa suara tangisan bayi terdengar sangat mengganggu, apalagi jika tangisannya berlarut-larut. Karena tidak ada yang mengerti arti dari suara tangisan bayi. Dijaman sekarang yang serba otomatis pengenalan suara tangisan bayi dapat dilakukan secara otomatis menggunakan komputer. Jika suara tangisan bayi dapat diartikan secara otomatis oleh komputer maka dapat membantu orang dewasa mengenali kebutuhan si bayi dan si bayi bisa tenang dengan mudah. Pengenalan suara tangisan bayi secara otomatis dapat dilakukan dengan menggunakan sistem perangkat lunak komputer. Sistem tersebut dapat mengidentifikasi suara tangisan bayi menggunakan algoritma klasifikasi. Algoritma yang dapat digunakan untuk melakukan klasifikasi adalah K-Nearest Neighbour. Untuk melakukan klasifikasi suara bayi perlu diubah menjadi data numerik yang dapat digunakan pada proses klasifikasi proses tersebut dinamakan ekstraksi fitur. Ekstraksi fitur yang digunakan pada penelitian ini adalah Prosodic Features. Setelah melewati proses ekstraksi fitur perlu dilakukan pengenalan pola untuk mendapatkan perbedaan pola antara satu data suara tangisan bayi dengan data suara tangisan bayi yang lain menggunakan Metode Moment of Dsitribution. Akurasi terbaik pada proses klasifikasi menggunakan data sampling Percentage Rate yaitu 76% dimana nilai K yang digunakan adalah 9. Sedangkan akurasi terbaik pada proses klasifikasi menggunakan data sampling Leave One Out yaitu 42% dengan nilai K yang digunakan adalah 5.

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Moment Invariant Features Extraction for Hand Gesture Recognition of Sign Language based on SIBI

Cited by 9 publications

References 21 publications

A Hybrid Background Subtraction and Artificial Neural Networks for Movement Recognition in Memorizing Quran

A Hybrid Background Subtraction and Artificial Neural Networks for Movement Recognition in Memorizing Quran

Analysis of Influence of Segmentation, Features, and Classification in sEMG Processing: A Case Study of Recognition of Brazilian Sign Language Alphabet

Klasifikasi Suara Tangisan Bayi Berdasarkan Prosodic Features Menggunakan Metode Moments of Distribution dan K-Nearest Neighbours

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