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Author: Rapeepan Maitree
Publisher:
ISBN:
Category : Deaf
Languages : en
Pages : 140
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Book Description
This study aims to develop a real-time continuous gestures classification system. The approach does not rely on using any gloves, visual markings, or special camera to achieve the recognition task. Instead, it uses just real-time video streaming of bare hand from a webcam. The top priority of this work is to create a robust, accessible, and flexible system that capable of dealing with different kinds of gestures (those that are solely characterized by the hand shape but not the motion, and those that required a specific hand or fingers movement) and determining how and when the system should respond to each gesture. The framework for handling both gesture types is a novel approach, and the evaluation shows promising results at 91.77% average correct classification using all 26 American Sign Language (ASL) manual alphabets as the examples. The system also achieves real-time performance at an average of 25 FPS. Other novel approaches in this study include the use of adaptive background model with the combination of elliptical skin-color model, K-mean clustering, and local region Gaussian mixture to detect hand under complex background. The configuration that allows the system to respond promptly to the gesture when it starts or ends, and the use of fingers counting to help increase the accuracy during the detection and classification stages are also developed.
Author: Rapeepan Maitree
Publisher:
ISBN:
Category : Deaf
Languages : en
Pages : 140
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Book Description
This study aims to develop a real-time continuous gestures classification system. The approach does not rely on using any gloves, visual markings, or special camera to achieve the recognition task. Instead, it uses just real-time video streaming of bare hand from a webcam. The top priority of this work is to create a robust, accessible, and flexible system that capable of dealing with different kinds of gestures (those that are solely characterized by the hand shape but not the motion, and those that required a specific hand or fingers movement) and determining how and when the system should respond to each gesture. The framework for handling both gesture types is a novel approach, and the evaluation shows promising results at 91.77% average correct classification using all 26 American Sign Language (ASL) manual alphabets as the examples. The system also achieves real-time performance at an average of 25 FPS. Other novel approaches in this study include the use of adaptive background model with the combination of elliptical skin-color model, K-mean clustering, and local region Gaussian mixture to detect hand under complex background. The configuration that allows the system to respond promptly to the gesture when it starts or ends, and the use of fingers counting to help increase the accuracy during the detection and classification stages are also developed.
Author: Ying Yin (Ph. D.)
Publisher:
ISBN:
Category :
Languages : en
Pages : 154
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Book Description
I have developed a real-time continuous gesture recognition system capable of dealing with two important problems that have previously been neglected: (a) smoothly handling two different kinds of gestures: those characterized by distinct paths and those characterized by distinct hand poses; and (b) determining how and when the system should respond to gestures. The novel approaches in this thesis include: a probabilistic recognition framework based on a flattened hierarchical hidden Markov model (HHMM) that unifies the recognition of path and pose gestures; and a method of using information from the hidden states in the HMM to identify different gesture phases (the pre-stroke, the nucleus and the post-stroke phases), allowing the system to respond appropriately to both gestures that require a discrete response and those needing a continuous response. The system is extensible: new gestures can be added by recording 3-6 repetitions of the gesture; the system will train an HMM model for the gesture and integrate it into the existing HMM, in a process that takes only a few minutes. Our evaluation shows that even using only a small number of training examples (e.g. 6), the system can achieve an average F1 score of 0.805 for two forms of gestures. To evaluate the performance of my system I collected a new dataset (YANG dataset) that includes both path and pose gestures, offering a combination currently lacking in the community and providing the challenge of recognizing different types of gestures mixed together. I also developed a novel hybrid evaluation metric that is more relevant to real- time interaction with different gesture flows.
Author: Steven Daniel Lovell
Publisher:
ISBN:
Category :
Languages : en
Pages : 103
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Book Description
This thesis describes the design and implementation of a wireless 6 degree-of-freedom inertial sensor system to be used for multiple-user, real-time gesture recognition and coordinated activity detection. Analysis is presented that shows that the data streams captured can be readily processed to detect gestures and coordinated activity. Finally, some pertinent research that can be pursued with these nodes in the areas of biomotion analysis and interactive entertainment are introduced.
Author: Sergio Escalera
Publisher: Springer
ISBN: 3319570218
Category : Computers
Languages : en
Pages : 583
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Book Description
This book presents a selection of chapters, written by leading international researchers, related to the automatic analysis of gestures from still images and multi-modal RGB-Depth image sequences. It offers a comprehensive review of vision-based approaches for supervised gesture recognition methods that have been validated by various challenges. Several aspects of gesture recognition are reviewed, including data acquisition from different sources, feature extraction, learning, and recognition of gestures.
Author: Yu Yuan
Publisher: ProQuest
ISBN: 9780549812494
Category : Human activity recognition
Languages : en
Pages :
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Book Description
Recent advances in various display and virtual technologies, coupled with an explosion in available computing power, have given rise to a number of novel human-computer interaction (HCI) modalities, among which gesture recognition is undoubtedly the most grammatically structured and complex. However, despite the abundance of novel interaction devices, the naturalness and efficiency of HCI has remained low. This is due in particular to the lack of robust sensory data interpretation techniques. To address the task of gesture recognition, this dissertation establishes novel probabilistic approaches based on support vector machines (SVM). Of special concern in this dissertation are the shapes of contact images on a multi-touch input device for both 2D and 3D. Five main topics are covered in this work. The first topic deals with the hand pose recognition problem. To perform classification of different gestures, a recognition system must attempt to leverage between class variations (semantically varying gestures), while accommodating potentially large within-class variations (different hand poses to perform certain gestures). For recognition of gestures, a sequence of hand shapes should be recognized. We present a novel shape recognition approach using Active Shape Model (ASM) based matching and SVM based classification. Firstly, a set of correspondences between the reference shape and query image are identified through ASM. Next, a dissimilarity measure is created to measure how well any correspondence in the set aligns the reference shape and candidate shape in the query image. Finally, SVM classification is employed to search through the set to find the best match from the kernel defined by the dissimilarity measure above. Results presented show better recognition results than conventional segmentation and template matching methods. In the second topic, dynamic time alignment (DTA) based SVM gesture recognition is addressed. In particular, the proposed method combines DTA and SVM by establishing a new kernel. The gesture data is first projected into a common eigenspace formed by principal component analysis (PCA) and a distance measure is derived from the DTA. By incorporating DTA in the kernel function, general classification problems with variable-sized sequential data can be handled. In the third topic, a C++ based gesture recognition application for the multi-touchpad is implemented. It uses the proposed gesture classification method along with a recursive neural networks approach to recognize definable gestures in real time, then runs an associated command. This application can further enable users with different disabilities or preferences to custom define gestures and enhance the functionality of the multi-touchpad. Fourthly, an SVM-based classification method that uses the DTW to measure the similarity score is presented. The key contribution of this approach is the extension of trajectory based approaches to handle shape information, thereby enabling the expansion of the system's gesture vocabulary. It consists of two steps: converting a given set of frames into fixed-length vectors and training an SVM from the vectorized manifolds. Using shape information not only yields discrimination among various gestures, but also enables gestures that cannot be characterized solely based on their motion information to be classified, thus boosting overall recognition scores. Finally, a computer vision based gesture command and communication system is developed. This system performs two major tasks: the first is to utilize the 3D traces of laser pointing devices as input to perform common keyboard and mouse control; the second is supplement free continuous gesture recognition, i.e., data gloves or other assistive devices are not necessary for 3D gestures recognition. As a result, the gesture can be used as a text entry system in wearable computers or mobile communication devices, though the recognition rate is lower than the approaches with the assistive tools. The purpose of this system is to develop new perceptual interfaces for human computer interaction based on visual input captured by computer vision systems, and to investigate how such interfaces can complement or replace traditional interfaces. Original contributions of this work span the areas of SVMs and interpretation of computer sensory inputs, such as gestures for advanced HCI. In particular, we have addressed the following important issues: (1) ASM base kernels for shape recognition. (2) DTA based sequence kernels for gesture classification. (3) Recurrent neural networks (RNN). (4) Exploration of a customizable HCI. (5) Computer vision based 3D gesture recognition algorithms and system.
Author: Eugene Charniak
Publisher: MIT Press
ISBN: 9780262531412
Category : Computers
Languages : en
Pages : 196
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Book Description
This text introduces statistical language processing techniques--word tagging, parsing with probabilistic context free grammars, grammar induction, syntactic disambiguation, semantic word classes, word-sense disambiguation--along with the underlying mathematics and chapter exercises.
Author: Tomas Pajdla
Publisher: Springer
ISBN: 3540246703
Category : Computers
Languages : en
Pages : 659
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Book Description
Welcome to the proceedings of the 8th European Conference on Computer - sion! Following a very successful ECCV 2002, the response to our call for papers was almost equally strong – 555 papers were submitted. We accepted 41 papers for oral and 149 papers for poster presentation. Several innovations were introduced into the review process. First, the n- ber of program committee members was increased to reduce their review load. We managed to assign to program committee members no more than 12 papers. Second, we adopted a paper ranking system. Program committee members were asked to rank all the papers assigned to them, even those that were reviewed by additional reviewers. Third, we allowed authors to respond to the reviews consolidated in a discussion involving the area chair and the reviewers. Fourth, thereports,thereviews,andtheresponsesweremadeavailabletotheauthorsas well as to the program committee members. Our aim was to provide the authors with maximal feedback and to let the program committee members know how authors reacted to their reviews and how their reviews were or were not re?ected in the ?nal decision. Finally, we reduced the length of reviewed papers from 15 to 12 pages. ThepreparationofECCV2004wentsmoothlythankstothee?ortsofthe- ganizing committee, the area chairs, the program committee, and the reviewers. We are indebted to Anders Heyden, Mads Nielsen, and Henrik J. Nielsen for passing on ECCV traditions and to Dominique Asselineau from ENST/TSI who kindly provided his GestRFIA conference software. We thank Jan-Olof Eklundh and Andrew Zisserman for encouraging us to organize ECCV 2004 in Prague.
Author: Jiadi Yu
Publisher: Springer Nature
ISBN: 9819959144
Category : Computers
Languages : en
Pages : 105
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Book Description
As a privacy-preserving and illumination-robust manner, WiFi signal-based user authentication has become a new direction for ubiquitous user authentication to protect user privacy and security. It gradually turns into an important option for addressing the security concern of IoT environment. However, due to the limited sensing capability of WiFi signals and wide application scenarios, WiFi signal-based user authentication suffers from practical issues of diversified behaviors and complex scenarios. Therefore, it is necessary to address the issues and build integrated systems for user authentication using WiFi signals. In this book, the development and progress of WiFi signal-based user authentication systems in extensive scenarios are presented, which provides a new direction and solution for ubiquitous security and privacy protection. This book gives strong motivation of leveraging WiFi signals to sense human activities for user authentication, and presents the key issues of WiFi-based user authentication in diversified behaviors and complex scenarios. This book provides the approaches for digging WiFi signals to sense human activities and extract features, realizing user authentication under fine-grained finger gestures, undefined body gestures, and multi-user scenarios. State-of-the-art researches and future directions involved with WiFi signal-based user authentication are presented and discussed as well. This book will benefit researchers and practitioners in the related field.
Author: Saeid Sanei
Publisher: John Wiley & Sons
ISBN: 1119390028
Category : Technology & Engineering
Languages : en
Pages : 417
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Book Description
A complete guide to the state of the art theoretical and manufacturing developments of body sensor network, design, and algorithms In Body Sensor Networking, Design, and Algorithms, professionals in the field of Biomedical Engineering and e-health get an in-depth look at advancements, changes, and developments. When it comes to advances in the industry, the text looks at cooperative networks, noninvasive and implantable sensor microelectronics, wireless sensor networks, platforms, and optimization—to name a few. Each chapter provides essential information needed to understand the current landscape of technology and mechanical developments. It covers subjects including Physiological Sensors, Sleep Stage Classification, Contactless Monitoring, and much more. Among the many topics covered, the text also includes additions such as: ● Over 120 figures, charts, and tables to assist with the understanding of complex topics ● Design examples and detailed experimental works ● A companion website featuring MATLAB and selected data sets Additionally, readers will learn about wearable and implantable devices, invasive and noninvasive monitoring, biocompatibility, and the tools and platforms for long-term, low-power deployment of wireless communications. It’s an essential resource for understanding the applications and practical implementation of BSN when it comes to elderly care, how to manage patients with chronic illnesses and diseases, and use cases for rehabilitation.
Author: Dipali Bansal
Publisher: Academic Press
ISBN: 0128221569
Category : Science
Languages : en
Pages : 208
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Book Description
Real-Time Data Acquisition in Human Physiology: Real-Time Acquisition, Processing, and Interpretation—A MATLAB-Based Approach focuses on the design and development of a computer-based system to detect and digitally process human ECG, EMG, and carotid pulse waveforms in real time. The indigenous system developed and described in this book allows for an easy-to-interface, simple hardware arrangement for bio-signal detection. The computational functionality of MATLAB is verified for viewing, digital filtration, and feature extraction of acquired bio-signals. This book demonstrates a method of providing a relatively cost-effective solution to human physiology real-time monitoring, processing, and interpretation that is more realizable and would directly benefit a larger population of patients. Presents an application-driven, interdisciplinary, and experimental approach to bio-signal processing with a focus on acquiring, processing, and understanding human ECG, EMG, carotid pulse data and HRV. Covers instrumentation and digital signal processing techniques useful for detecting and interpreting human physiology in real time, including experimental layout and methodology in an easy-to-understand manner. Discusses development of a computer-based system that is capable of direct interface through the sound port of a PC and does not require proprietary DAQ units and ADC units. Covers a MATLAB-based algorithm for online noise reduction, features extraction techniques, and infers diagnostic features in real time. Provides proof of concept of a PC-based twin channel acquisition system for the recognition of multiple physiological parameters. Establishes the use of Digital Signal Controller to enhance features of acquired human physiology. Presents the use of carotid pulse waveforms for HRV analysis in critical situations using a very simple hardware/software arrangement.
