Perancangan Sarung Tangan Untuk Pengenalan Sistem Isyarat Bahasa Indonesia Berbasis Sensor

Penelitian ini bertujuan untuk mengembangkan sarung tangan yang dilengkapi sensor (embedded system) yang digunakan dalam sistem pengenalan Sistem Isyarat Bahasa Indonesia (SIBI). Dengan pendekatan berbasis data sensor, sistem pengenalan SIBI diharapkan dapat memiliki akurasi yang lebih baik, yaitu dengan menggunakan sensor flex (untuk gerakan lekukan jari, dan menggunakan kombinasi sensor accelerometer-gyroscope untuk mengetahui kemiringan/orientasi tangan. Penelitian ini masih dalam tahap perancangan sarung tangan. Dalam tahap perancangan ini telah diselesaikan untuk desain rangkaian, desain PCB, pembuatan PCB, pemasangan sensor flex dan desain program mikrokontroler

A Real Time Hand Gesture Recognition System Based on DFT and SVM

[[abstract]]Vision based band gesture recognition provides a more nature and powerful means for human-computer interaction. A fast detection process of hand gesture and an effective feature extraction process are presented. The proposed a hand gesture recognition algorithm comprises four main steps. First use Cam-shift algorithm to track skin color after closing process. Second, in order to extract feature, we use BEA to extract the boundary of the hand. Third, the benefits of Fourier descriptor are invariance to the starting point of the boundary, deformation, and rotation, and therefore transform the starting point of the boundary by Fourier transformation. Finally, outline feature for the nonlinear non-separable type of data was classified by using SVM. Experimental results showed the accuracy is 93.4% in average and demonstrated the feasibility of proposed system.[[incitationindex]]EI[[booktype]]電子版[[booktype]]紙

RANCANG BANGUN APLIKASI MONITORING DAN REKAM DATA SISTEM PENGENALAN SISTEM ISYARAT BAHASA INDONESIA BERBASIS SENSOR

ABSTRAK Dalam penelitian ini dikembangkan pengenalan sistem isyarat bahasa Indonesia (SIBI) berbasis sensor yang diharapkan dapat memperbaiki akurasi, yaitu dengan menggunakan sensor flex untuk gerakan lekukan jari, dan menggunakan sensor accelerometer-gyroscope untuk mengetahui kemiringan/orientasi tangan. Untuk mendapatkan ekstraksi ciri dan metode pengenalan yang optimal, maka diperlukan uji coba dan analisis terhadap perbandingan ekstraksi ciri dan metode pengenalan, sehingga dapat ditentukan yang terbaik. Dalam uji coba dan analisis tersebut, maka diperlukan sampel data offline atau data yang sudah disimpan/direkam sebelumnya, sehingga diperlukan aplikasi untuk dapat merekam data (recording) dan memonitoring data dari sensor-sensor yang dipasang pada sarung tangan. Dengan adanya data-data sensor tersebut, maka proses pemilihan ekstraksi ciri dan metode pengenalan yang optimal dapat dilakukan secara offline, menggunakan perangkat lunak komputasi. Capaian dapam Penelitian ini, adalah telah berhasil dikembangkan program aplikasi monitoring dan rekam data untuk sistem pengenalan SIBI. Data sensor yang dimonitoring dan direkam adalah data raw, sehingga perlu dilakukan pengolahan data untuk proses ekstraksi ciri sebelum diujicobakan pada metode pengenalan tertentu Kata kunci: SIBI, bahasa isyarat, sensor, flex, acclerometer, gyroscope, monitoring, recording

Thai Finger-Spelling Recognition Using a Cascaded Classifier Based on Histogram of Orientation Gradient Features

Hand posture recognition is an essential module in applications such as human-computer interaction (HCI), games, and sign language systems, in which performance and robustness are the primary requirements. In this paper, we proposed automatic classification to recognize 21 hand postures that represent letters in Thai finger-spelling based on Histogram of Orientation Gradient (HOG) feature (which is applied with more focus on the information within certain region of the image rather than each single pixel) and Adaptive Boost (i.e., AdaBoost) learning technique to select the best weak classifier and to construct a strong classifier that consists of several weak classifiers to be cascaded in detection architecture. We collected 21 static hand posture images from 10 subjects for testing and training in Thai letters finger-spelling. The parameters for the training process have been adjusted in three experiments, false positive rates (FPR), true positive rates (TPR), and number of training stages (N), to achieve the most suitable training model for each hand posture. All cascaded classifiers are loaded into the system simultaneously to classify different hand postures. A correlation coefficient is computed to distinguish the hand postures that are similar. The system achieves approximately 78% accuracy on average on all classifier experiments

Real-time user independent hand gesture recognition from time-of-flight camera video using static and dynamic models

The use of hand gestures offers an alternative to the commonly used human computer interfaces, providing a more intuitive way of navigating among menus and multimedia applications. This paper presents a system for hand gesture recognition devoted to control windows applications. Starting from the images captured by a time-of-flight camera (a camera that produces images with an intensity level inversely proportional to the depth of the objects observed) the system performs hand segmentation as well as a low-level extraction of potentially relevant features which are related to the morphological representation of the hand silhouette. Classification based on these features discriminates between a set of possible static hand postures which results, combined with the estimated motion pattern of the hand, in the recognition of dynamic hand gestures. The whole system works in real-time, allowing practical interaction between user and application.Peer ReviewedPostprint (published version

Tensor-based Hyperspectral Image Processing Methodology and its Applications in Impervious Surface and Land Cover Mapping

The emergence of hyperspectral imaging provides a new perspective for Earth observation, in addition to previously available orthophoto and multispectral imagery. This thesis focused on both the new data and new methodology in the field of hyperspectral imaging. First, the application of the future hyperspectral satellite EnMAP in impervious surface area (ISA) mapping was studied. During the search for the appropriate ISA mapping procedure for the new data, the subpixel classification based on nonnegative matrix factorization (NMF) achieved the best success. The simulated EnMAP image shows great potential in urban ISA mapping with over 85% accuracy. Unfortunately, the NMF based on the linear algebra only considers the spectral information and neglects the spatial information in the original image. The recent wide interest of applying the multilinear algebra in computer vision sheds light on this problem and raised the idea of nonnegative tensor factorization (NTF). This thesis found that the NTF has more advantages over the NMF when work with medium- rather than the high-spatial-resolution hyperspectral image. Furthermore, this thesis proposed to equip the NTF-based subpixel classification methods with the variations adopted from the NMF. By adopting the variations from the NMF, the urban ISA mapping results from the NTF were improved by ~2%. Lastly, the problem known as the curse of dimensionality is an obstacle in hyperspectral image applications. The majority of current dimension reduction (DR) methods are restricted to using only the spectral information, when the spatial information is neglected. To overcome this defect, two spectral-spatial methods: patch-based and tensor-patch-based, were thoroughly studied and compared in this thesis. To date, the popularity of the two solutions remains in computer vision studies and their applications in hyperspectral DR are limited. The patch-based and tensor-patch-based variations greatly improved the quality of dimension-reduced hyperspectral images, which then improved the land cover mapping results from them. In addition, this thesis proposed to use an improved method to produce an important intermediate result in the patch-based and tensor-patch-based DR process, which further improved the land cover mapping results

Intuitive Interaktion durch videobasierte Gestenerkennung

Hinter der Forschung an videobasierter Handgestenerkennung steht die Vision, Interaktion zwischen Mensch und Computer losgelöst von klassischen Eingabegeräten wie Maus und Tastatur zu realisieren. Das Ziel dieser Arbeit ist die Entwicklung von echtzeitfähigen Verfahren, die eine robuste und fehlerarme Erkennung menschlicher Handgesten realisieren und so die Bedienung eines Computersystems auch für technisch unerfahrene Anwender nutzbar machen. In dieser Arbeit werden vier Verfahren entwickelt, die unterschiedliche Arten der Interaktion durch videobasierte Handgestenerkennung realisieren.The vision behind research on video based hand gesture recognition is to realise a new kind of interaction between humans and computer beyond the classical input devices such as mouse and keyboard. The aim of this thesis is to develop new video based realtime algorithms, which enable a robust and accurate recognition of human hand gestures and allow interaction with the computer even for technically unversed users. In this thesis four different algorithms are developed that can be used for intuitive interaction purposes depending on the demands and needs of different scenario applications