38 research outputs found
Vision based 3D Gesture Tracking using Augmented Reality and Virtual Reality for Improved Learning Applications
3D gesture recognition and tracking based augmented reality and virtual reality have become a big interest of research because of advanced technology in smartphones. By interacting with 3D objects in augmented reality and virtual reality, users get better understanding of the subject matter where there have been requirements of customized hardware support and overall experimental performance needs to be satisfactory. This research investigates currently various vision based 3D gestural architectures for augmented reality and virtual reality. The core goal of this research is to present analysis on methods, frameworks followed by experimental performance on recognition and tracking of hand gestures and interaction with virtual objects in smartphones. This research categorized experimental evaluation for existing methods in three categories, i.e. hardware requirement, documentation before actual experiment and datasets. These categories are expected to ensure robust validation for practical usage of 3D gesture tracking based on augmented reality and virtual reality. Hardware set up includes types of gloves, fingerprint and types of sensors. Documentation includes classroom setup manuals, questionaries, recordings for improvement and stress test application. Last part of experimental section includes usage of various datasets by existing research. The overall comprehensive illustration of various methods, frameworks and experimental aspects can significantly contribute to 3D gesture recognition and tracking based augmented reality and virtual reality.Peer reviewe
Augmented Reality based 3D Human Hands Tracking from Monocular True Images Using Convolutional Neural Network
Precise modeling of hand tracking from monocular moving camera calibration parameters using semantic cues is an active area of research concern for the researchers due to lack of accuracy and computational overheads. In this context, deep learning based framework, i.e. convolutional neural network based human hands tracking as well as recognizing pose of hands in the current camera frame become active research problem. In addition, tracking based on monocular camera needs to be addressed due to updated technology such as Unity3D engine and other related augmented reality plugins. This research aims to track human hands in continuous frame by using the tracked points to draw 3D model of the hands as an overlay in the original tracked image. In the proposed methodology, Unity3D environment was used for localizing hand object in augmented reality (AR). Later, convolutional neural network was used to detect hand palm and hand keypoints based on cropped region of interest (ROI). Proposed method by this research achieved accuracy rate of 99.2% where single monocular true images were used for tracking. Experimental validation shows the efficiency of the proposed methodology.Peer reviewe
A comprehensive review towards segmentation and detection of cancer cell and tumor for dynamic 3D reconstruction
Automated cancer cell and tumor segmentation and detection for 3D modeling are still an unsolved research
problem in computer vision, image processing and pattern recognition research domains. Human body is
complex three-dimensional structure where numerous types of cancer and tumor may exist regardless of shape
or position. A three-dimensional (3D) reconstruction of cancer cell and tumor from body parts does not lead to
loss of information like 2D shape visualization. Various research methodologies for segmentation and detection
for 3D reconstruction of cancer cell and tumor were performed by previous research. However, the pursuit for
better methodology for segmentation and detection for 3D reconstruction of cancer cell and tumor are still
unsolved research problem due to lack of efficient feature extraction for details surface information,
misclassification during training phases and low tissue contrast which causes low detection and precision rate
with high computational complexity during detection and segmentation. This research addresses comprehensive
and critical review of various segmentation and detection research methodologies for cancer affected cell and
tumor in human body in the basis of three-dimensional reconstruction from MRI or CT images. At first, core
research background is illustrated highlighting various aspects addressed by this research. After that, various
previous methods with advantages and disadvantages followed by various phases used as frameworks exist in
the previous research demonstrated by this research. Then, extensive experimental evaluations done by previous
research are demonstrated by this research with various performance metrics. At last, this research summarized
overall observation on previous research categorized into two aspects, i.e. observation on common research
methodologies and recommended area for further research. Overall reviews proposed in this paper have been
extensively studied in various research papers which can significantly contribute to computer vision research
and can be potential for future development and direction for future research
Serial Cases of Combining ESWL and ERCP Procedures in Management Chronic Pancreatitis and Difficult Bile Duct Stones
Extracorporeal shock wave lithotripsy (ESWL) has an established role in the management of pancreaticductalcalculiandasfurthertreatmentmodalityforlargeordifficultcomplicatedcommon bile duct(CBD)stones. Combinedwithminimally invasive endoscopic procedure suchasendoscopic retrogradecholangio-pancreatography(ERCP),it has replaced open surgeryas theinitial form of therapy. ESWL has also proved to bean effective therapyin treating intrahepaticstones that are refractory toroutine endoscopic extraction.Inthese serialcaseswe presentthree caseswhichusing combinedESWLandERCP procedurestotreatchronicpancreatitispainandgiantCBDstonethat presentinggoodresultsonfollowupwithoutanycomplication
Kapasitas Lembaga dan Dinamika Pencegahan Konflik: Studi Kasus Kalimantan Barat dan Nusa Tenggara Barat
Publikasi ini mengenai studi tentang kapasitas lembaga dalam pencegahan konflik dengan mengambil kasus di Kalimantan Barat dan Nusa Tenggara Barat. Mengingat semakin kompleksnya penanganan konflik di Indonesia, peran lembaga menjadi strategis untuk melaksanakan hal tersebut. Undang-Undang No. 7 Tahun 2012 tentang Penanganan Konflik Sosial sendiri menyatakan bahwa penanganan konflik meliputi upaya-upaya pencegahan, penghentian, dan pemulihan. Mengingat pencegahan konflik merupakan bagian terpenting dari penanganan konflik, kami dari Tim Sistem Nasional Pemantauan Kekerasan-The Habibie Center (SNPK-THC) memandang perlu diadakan studi tentang bagaimana lembaga-lembaga tersebut menjalankan kapasitas mereka dalam pencegahan konflik.
Sesuai dengan apa yang digariskan UU di atas, pencegahan konflik sebagai salah satu tahapan dalam penanganan konflik diimplementasikan melalui peran lembaga dan strategi pencegahan yang dilakukannya. Dengan demikian, penelitian tentang pencegahan konflik ini dipilih sebagai bagian awal dari rangkaian penelitian tentang penanganan konflik. Selain itu, studi tentang kapasitas lembaga dalam pencegahan konflik di Indonesia masih belum banyak dilakukan, sehingga penelitian ini diharapkan dapat berkontribusi untuk mengisi kesenjangan literatur yang ada.
Studi tentang kapasitas lembaga ini terlaksana sebagai bagian dari program SNPK-THC. Program ini merupakan kerja sama antara The Habibie Center dan Kedeputian I Bidang Koordinasi Lingkungan Hidup dan Kerawanan Sosial, Kementerian Koordinator Bidang Pembangunan Manusia dan Kebudayaan (Kemenko PMK), didukung oleh The World Bank serta kemitraan antara Australian Department of Foreign Affairs and Trade (DFAT) dan The Asia Foundation (TAF)
Crowd Density Estimation from Autonomous Drones Using Deep Learning : Challenges and Applications
Crowd flow estimation from Drones or normally referred as Unmanned Aerial Vehicle (UAV ) for crowd management and monitoring is an essential research problem for adaptive monitoring and controlling dynamic crowd gatherings. Various challenges exist in this context, i.e. variation in density, scale, brightness, height from UAV platform, occlusion and inefficient pose estimation. Currently, gathering of crowd is mostly monitored by Close Circuit Television (CCTV) cameras where various problems exist, i.e. coverage in little area and constant involvement of human to monitor crowd which encourage researchers to move towards deep learning and computer vision techniques to minimize the need of human operator and thus develop intelligent crowd counting techniques. Deep learning frameworks are promising for intelligent crowd analysis from frames of video despite the fact of various challenges for detecting humans from unstable UAV camera platforms. This research presents rigorous investigation and analysis in existing methods with their applications for crowd flow estimation from UAV. Besides, comprehensive performance evaluation for existing methods using recent deep learning frameworks is illustrated for crowd counting purposes. In addition, strong foundation for future direction is given by elaborating observations on existing research frameworks.Peer reviewe
AN EFFICIENT METHOD FOR HAND GESTURE RECOGNITION USING ROBUST FEATURES VECTOR
Integration of technology for the Fourth Industrial Revolution (IR 4.0) has increased the need for efficient methods for developing dynamic human computer interfaces and virtual environments. In this context, hand gesture recognition can play a vital role to serve as a natural mode of interactive human machine interaction. Unfixed brightness, complex backgrounds, color constraints, dependency on hand shape, rotation, and scale variance are the challenging issues which have an impact on robust performance for the existing methods as per outlined in previous researches. This research presents an efficient method for hand gesture recognition by constructing a robust features vector. The proposed method is performed in two phases, where in the first phase the features vector is constructed by selecting interest points at distinctive locations using a blob detector based on Hessian matrix approximation. After detecting the area of the hand from the features vector, edge detection is applied in the isolated hand followed by edge orientation computation. After this, templates are generated using one and two dimensional mapping to compare candidate and prototype images using adaptive threshold. The proposed research performed extensive experimentation, where a recognition accuracy rate of 98.33% was achieved by it, which is higher as compared to previous research results. Experimental results reveal the effectiveness of the proposed methodology in real time.Peer reviewe
Employee Work Achievement of Indonesian Regional Company: Impact of Leadership and Organizational Commitment
This research was carried out to know the influence of leadership style on employee work achievements, to know the effect of organizational commitment on employee work achievements, and to know the influence of leadership style and organizational commitment on the work achievements of Regional Company employees. This research is an associative study with a quantitative approach. Data analysis techniques use multiple linear regression. The data used are primary, i.e. data obtained directly from research objects through the distribution of questionnaires to 77 respondents. The results showed that leadership styles had a positive and significant effect on employee work achievements. Organizational commitment has a positive and significant effect on employee work achievements. Leadership style and organizational commitment have a positive and significant effect on employee work achievements
Moving Object Detection Using Dynamic Motion Modelling from UAV Aerial Images
Motion analysis based moving object detection from UAV aerial image is still an unsolved issue due to inconsideration of proper motion estimation. Existing moving object detection approaches from UAV aerial images did not deal with motion based pixel intensity measurement to detect moving object robustly. Besides current research on moving object detection from UAV aerial images mostly depends on either frame difference or segmentation approach separately. There are two main purposes for this research: firstly to develop a new motion model called DMM (dynamic motion model) and secondly to apply the proposed segmentation approach SUED (segmentation using edge based dilation) using frame difference embedded together with DMM model. The proposed DMM model provides effective search windows based on the highest pixel intensity to segment only specific area for moving object rather than searching the whole area of the frame using SUED. At each stage of the proposed scheme, experimental fusion of the DMM and SUED produces extracted moving objects faithfully. Experimental result reveals that the proposed DMM and SUED have successfully demonstrated the validity of the proposed methodology