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| Author | : |
| Publisher | : |
| Total Pages | : 55 |
| Release | : 2015 |
| Genre | : |
| ISBN | : |
| Author | : Kane, Lalit |
| Publisher | : IGI Global |
| Total Pages | : 249 |
| Release | : 2022-03-25 |
| Genre | : Computers |
| ISBN | : 1799894363 |
Due to the rise of new applications in electronic appliances and pervasive devices, automated hand gesture recognition (HGR) has become an area of increasing interest. HGR developments have come a long way from the traditional sign language recognition (SLR) systems to depth and wearable sensor-based electronic devices. Where the former are more laboratory-oriented frameworks, the latter are comparatively realistic and practical systems. Based on various gestural traits, such as hand postures, gesture recognition takes different forms. Consequently, different interpretations can be associated with gestures in various application contexts. A considerable amount of research is still needed to introduce more practical gesture recognition systems and associated algorithms. Challenges and Applications for Hand Gesture Recognition highlights the state-of-the-art practices of HGR research and discusses key areas such as challenges, opportunities, and future directions. Covering a range of topics such as wearable sensors and hand kinematics, this critical reference source is ideal for researchers, academicians, scholars, industry professionals, engineers, instructors, and students.
| Author | : Michael Dimartino |
| Publisher | : |
| Total Pages | : 44 |
| Release | : 2016 |
| Genre | : |
| ISBN | : |
As technology continues to improve, hand gesture recognition as a form of humancomputer interaction is becoming more and more feasible. One such piece of technology, the Leap Motion Controller, provides 3D tracking data of the hands through an easy-to-use API. This thesis presents an application that uses Leap Motion tracking data to learn and recognize static and dynamic hand gestures. Gestures are recognized using statistical pattern recognition. Each gesture is defined by a set of features including fingertip positions, hand orientation, and movement. Given sufficient training data, the similarity between two gestures is measured by comparing each of their corresponding features. Two separate implementations are presented for dealing with the temporal aspect of dynamic gestures. Users are able to interact with the system using a convenient graphical user interface. The accuracy of the system was experimentally tested with the help of two separate test participants: one for the training phase and one for the recognition phase. All test gestures (both static and dynamic) were successfully recognized with minimal training data. In some cases, additional gestures were mistakenly recognized.
| Author | : Jan Erik Solem |
| Publisher | : "O'Reilly Media, Inc." |
| Total Pages | : 264 |
| Release | : 2012-06-19 |
| Genre | : Computers |
| ISBN | : 1449341934 |
If you want a basic understanding of computer vision’s underlying theory and algorithms, this hands-on introduction is the ideal place to start. You’ll learn techniques for object recognition, 3D reconstruction, stereo imaging, augmented reality, and other computer vision applications as you follow clear examples written in Python. Programming Computer Vision with Python explains computer vision in broad terms that won’t bog you down in theory. You get complete code samples with explanations on how to reproduce and build upon each example, along with exercises to help you apply what you’ve learned. This book is ideal for students, researchers, and enthusiasts with basic programming and standard mathematical skills. Learn techniques used in robot navigation, medical image analysis, and other computer vision applications Work with image mappings and transforms, such as texture warping and panorama creation Compute 3D reconstructions from several images of the same scene Organize images based on similarity or content, using clustering methods Build efficient image retrieval techniques to search for images based on visual content Use algorithms to classify image content and recognize objects Access the popular OpenCV library through a Python interface
| Author | : Ankit Chaudhary |
| Publisher | : Springer |
| Total Pages | : 108 |
| Release | : 2017-06-05 |
| Genre | : Technology & Engineering |
| ISBN | : 9811047987 |
This book focuses on light invariant bare hand gesture recognition while there is no restriction on the types of gestures. Observations and results have confirmed that this research work can be used to remotely control a robotic hand using hand gestures. The system developed here is also able to recognize hand gestures in different lighting conditions. The pre-processing is performed by developing an image-cropping algorithm that ensures only the area of interest is included in the segmented image. The segmented image is compared with a predefined gesture set which must be installed in the recognition system. These images are stored and feature vectors are extracted from them. These feature vectors are subsequently presented using an orientation histogram, which provides a view of the edges in the form of frequency. Thereby, if the same gesture is shown twice in different lighting intensities, both repetitions will map to the same gesture in the stored data. The mapping of the segmented image's orientation histogram is firstly done using the Euclidian distance method. Secondly, the supervised neural network is trained for the same, producing better recognition results. An approach to controlling electro-mechanical robotic hands using dynamic hand gestures is also presented using a robot simulator. Such robotic hands have applications in commercial, military or emergency operations where human life cannot be risked. For such applications, an artificial robotic hand is required to perform real-time operations. This robotic hand should be able to move its fingers in the same manner as a human hand. For this purpose, hand geometry parameters are obtained using a webcam and also using KINECT. The parameter detection is direction invariant in both methods. Once the hand parameters are obtained, the fingers’ angle information is obtained by performing a geometrical analysis. An artificial neural network is also implemented to calculate the angles. These two methods can be used with only one hand, either right or left. A separate method that is applicable to both hands simultaneously is also developed and fingers angles are calculated. The contents of this book will be useful for researchers and professional engineers working on robotic arm/hand systems.
| Author | : Qiguang Miao |
| Publisher | : Elsevier |
| Total Pages | : 225 |
| Release | : 2024-07-26 |
| Genre | : Computers |
| ISBN | : 0443289603 |
Gesture Recognition: Theory and Applications covers this important topic in computer science and language technology that has a goal of interpreting human gestures via mathematical algorithms. The book begins by examining the computer vision-based gesture recognition method, focusing on the theory and related research results of various recent gesture recognition technologies. The book takes the evolutions of gesture recognition technology as a clue, systematically introducing gesture recognition methods based on handcrafted features, convolutional neural networks, recurrent neural networks, multimodal data fusion, and visual attention mechanisms. Three gesture recognition-based HCI (Human Computer Interaction) practical cases are introduced. Finally, the book looks at emerging research trends and application. Focuses on the theory and application of gesture recognition, providing a systematic introduction to commonly used datasets in the field as well as algorithms based on handcrafted features, convolutional neural networks, multimodal fusion, and attention mechanisms Introduces the practical applications of gesture recognition in real-world scenarios, enabling readers to enhance their practical application skills while learning about relevant technologies Demonstrates four main categories of gesture recognition methods and analyzes their associated challenges
| Author | : |
| Publisher | : |
| Total Pages | : 36 |
| Release | : 1996 |
| Genre | : |
| ISBN | : |
Gestural interfaces have the potential of enhancing control operations in numerous applications. For Air Force systems, machine-recognition of whole-hand gestures may be useful as an alternative controller, especially when conventional controls are less accessible. The objective of this effort was to explore the utility of a neural network-based approach to the recognition of whole-hand gestures. Using a fiber-optic instrumented glove, gesture data were collected for a set of static gestures drawn from the manual alphabet used by the deaf. Two types of neural networks (multilayer perceptron and Kohonen self-organizing feature map) were explored. Both showed promise, but the perceptron model was quicker to implement and classification is inherent in the model. The high gesture recognition rates and quick network retraining times found in the present study suggest that a neural network approach to gesture recognition be further evaluated.
| Author | : Carlos Travieso-Gonzalez |
| Publisher | : BoD – Books on Demand |
| Total Pages | : 175 |
| Release | : 2017-07-12 |
| Genre | : Computers |
| ISBN | : 9535133772 |
Nowadays, the technological advances allow developing many applications on different fields. In this book Motion Tracking and Gesture Recognition, two important fields are shown. Motion tracking is observed by a hand-tracking system for surgical training, an approach based on detection of dangerous situation by the prediction of moving objects, an approach based on human motion detection results and preliminary environmental information to build a long-term context model to describe and predict human activities, and a review about multispeaker tracking on different modalities. On the other hand, gesture recognition is shown by a gait recognition approach using Kinect sensor, a study of different methodologies for studying gesture recognition on depth images, and a review about human action recognition and the details about a particular technique based on a sensor of visible range and with depth information.
| Author | : Giovanni Saggio |
| Publisher | : Mdpi AG |
| Total Pages | : 0 |
| Release | : 2023-12-29 |
| Genre | : Computers |
| ISBN | : 9783036586946 |
Gesture recognition (GR) aims to interpret human gestures, having an impact on a number of different application fields. This Special Issue is devoted to describing and examining up-to-date technologies to measure gestures, algorithms to interpret data, and applications related to GR. These technologies involve camera-based systems (e.g., ground truth system, GTS; Azura Kinect), wearable sensors (e.g., inertial measurement units, IMUs; micro electro-mechanical systems, MEMS; angular displacement sensors, ADS; resistive flex sensors, RFSs), electromagnetic field measurements (e.g., leap motion sensor), acoustic-based inputs (e.g., microphone, stethoscope), radar systems (e.g., continuous wave), and tactile sensors (e.g., pressure sensitive transistors). Data interpretations are detailed by means of classifiers (e.g., neural networks, NN; convolutional neural network, CNN; hidden Markov models, HMM; and k-nearest neighbors, kNN). The applications are for medical purposes (e.g., to provide physiotherapy solutions, to assess Parkinson's disease, and to electrocardiogram detection), for social inclusion (e.g., sign language recognition: British, American, and Italian ones), for sport activity scoring (e.g., taekwondo), for machine interaction (e.g., to control a holographic display), and for safety purposes (e.g., to drowsiness recognition). This Special Issue is addressed to all the researchers, professionals, and designers interested in GR and to all the users driven by curiosity and passion. The Guest Editors would like to acknowledge and express their gratitude to all of the authors involved.
| Author | : Mohd Amrallah Mustafa |
| Publisher | : |
| Total Pages | : 128 |
| Release | : 2007 |
| Genre | : Gesture |
| ISBN | : |
Hand gesture has been part of human communication, where, young children usually communicate by using gesture before they can talk. Adults may have also gesture if they need to or they are indeed mute or deaf. Thus the idea of teaching a machine to also learn gesture is very appealing due to its unique mode of communications. A reliable hand gesture recognition system will make the remote control become obsolete. However, ,any of the a new techniques proposed are complicated to be implemented in real time, especially as a human machine interface. This thesis focuses on recognizing hand gesture in static posture. Since static hand postures not only can express some concepts, but also can act as special transition states in temporal gestures recognition, thus estimating static hand postures is in fact a big topics in gesture recognition. A database consists of 200 gesture images have been built, where five volunteers had help in the making of the database. The images were captured in a controlled enviroment and the postures are free from occulation where the background is uncluttered and the hand is assumed to have been localized. A system was then built to recognize the hand gesture. The captured image will be first preprocessed in order to binarize the palm region, where Sobel edge detection technique has been employed, with later followed by morphological operation. A new feature extraction technique has been developed, based on horizontal and vertical states transition count, and the ration of hand area with the respect to whole area of image. These set of features have been proven to have high intra class dissimilarity attributes. In order to have a system that can be easily trained, artificial neural networks has been chosen in the classification stage. A multilayer perceptron with back-propagation algorithm has been developed, thus the system is actually in-built to be used as a human machine interface. The gesture recognition system has been built and tested in Matlab. Where simulations have shown promising results. The performance of recognition rate in this research is 95% which shows a major improvement in comparison to the available methods.
