Download or read book Image based Gesture Recognition with Support Vector Machines written by Yu Yuan and published by ProQuest. This book was released on 2008 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Face Detection and Gesture Recognition for Human Computer Interaction written by Ming-Hsuan Yang and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: Traditionally, scientific fields have defined boundaries, and scientists work on research problems within those boundaries. However, from time to time those boundaries get shifted or blurred to evolve new fields. For instance, the original goal of computer vision was to understand a single image of a scene, by identifying objects, their structure, and spatial arrangements. This has been referred to as image understanding. Recently, computer vision has gradually been making the transition away from understanding single images to analyzing image sequences, or video understanding. Video understanding deals with understanding of video sequences, e. g. , recognition of gestures, activities, facial expressions, etc. The main shift in the classic paradigm has been from the recognition of static objects in the scene to motion-based recognition of actions and events. Video understanding has overlapping research problems with other fields, therefore blurring the fixed boundaries. Computer graphics, image processing, and video databases have obvious overlap with computer vision. The main goal of computer graphics is to gener ate and animate realistic looking images, and videos. Researchers in computer graphics are increasingly employing techniques from computer vision to gen erate the synthetic imagery. A good example of this is image-based rendering and modeling techniques, in which geometry, appearance, and lighting is de rived from real images using computer vision techniques. Here the shift is from synthesis to analysis followed by synthesis.

Download or read book Gesture Recognition written by Sergio Escalera and published by Springer. This book was released on 2017-07-19 with total page 583 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Pattern Recognition with Support Vector Machines written by Seong-Whan Lee and published by Springer. This book was released on 2003-08-02 with total page 433 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines, SVM 2002, held in Niagara Falls, Canada in August 2002.The 16 revised full papers and 14 poster papers presented together with two invited contributions were carefully reviewed and selected from 57 full paper submissions. The papers presented span the whole range of topics in pattern recognition with support vector machines from computational theories to implementations and applications.

Download or read book Support Vector Machines Applications written by Yunqian Ma and published by Springer Science & Business Media. This book was released on 2014-02-12 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: Support vector machines (SVM) have both a solid mathematical background and practical applications. This book focuses on the recent advances and applications of the SVM, such as image processing, medical practice, computer vision, and pattern recognition, machine learning, applied statistics, and artificial intelligence. The aim of this book is to create a comprehensive source on support vector machine applications.

Download or read book Wireless Hand Gesture Recognition written by Vaheh Elyasi and published by . This book was released on 2018 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt: Conversion of hand gesture of American Sign Language (ASL) to text is very important issue in today’s deaf society, which helps deaf people to have better communication with the outside world. The gesture detection is based on implementation of the method of Histogram Oriented Gradient (HOG) and use of Support Vector Machine classifier (SVM) to translate the input captured image of the ASL gesture to its corresponding text. Afterward, the detected text needs to be sent to another device; therefore, with the help of wireless module, such as Zigbee, this task can be accomplished.

Download or read book Human Computer Interaction Using Hand Gestures written by Prashan Premaratne and published by Springer Science & Business Media. This book was released on 2014-03-20 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt: Human computer interaction (HCI) plays a vital role in bridging the 'Digital Divide', bringing people closer to consumer electronics control in the 'lounge'. Keyboards and mouse or remotes do alienate old and new generations alike from control interfaces. Hand Gesture Recognition systems bring hope of connecting people with machines in a natural way. This will lead to consumers being able to use their hands naturally to communicate with any electronic equipment in their 'lounge.' This monograph will include the state of the art hand gesture recognition approaches and how they evolved from their inception. The author would also detail his research in this area for the past 8 years and how the future might turn out to be using HCI. This monograph will serve as a valuable guide for researchers (who would endeavour into) in the world of HCI.

Download or read book Real time Hand Gesture Detection and Recognition for Human Computer Interaction written by Nasser Hasan Abdel-Qader Dardas and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis focuses on bare hand gesture recognition by proposing a new architecture to solve the problem of real-time vision-based hand detection, tracking, and gesture recognition for interaction with an application via hand gestures. The first stage of our system allows detecting and tracking a bare hand in a cluttered background using face subtraction, skin detection and contour comparison. The second stage allows recognizing hand gestures using bag-of-features and multi-class Support Vector Machine (SVM) algorithms. Finally, a grammar has been developed to generate gesture commands for application control. Our hand gesture recognition system consists of two steps: offline training and online testing. In the training stage, after extracting the keypoints for every training image using the Scale Invariance Feature Transform (SIFT), a vector quantization technique will map keypoints from every training image into a unified dimensional histogram vector (bag-of-words) after K-means clustering. This histogram is treated as an input vector for a multi-class SVM to build the classifier. In the testing stage, for every frame captured from a webcam, the hand is detected using my algorithm. Then, the keypoints are extracted for every small image that contains the detected hand posture and fed into the cluster model to map them into a bag-of-words vector, which is fed into the multi-class SVM classifier to recognize the hand gesture. Another hand gesture recognition system was proposed using Principle Components Analysis (PCA). The most eigenvectors and weights of training images are determined. In the testing stage, the hand posture is detected for every frame using my algorithm. Then, the small image that contains the detected hand is projected onto the most eigenvectors of training images to form its test weights. Finally, the minimum Euclidean distance is determined among the test weights and the training weights of each training image to recognize the hand gesture. Two application of gesture-based interaction with a 3D gaming virtual environment were implemented. The exertion videogame makes use of a stationary bicycle as one of the main inputs for game playing. The user can control and direct left-right movement and shooting actions in the game by a set of hand gesture commands, while in the second game, the user can control and direct a helicopter over the city by a set of hand gesture commands.

Download or read book Motion History Images for Action Recognition and Understanding written by Md. Atiqur Rahman Ahad and published by Springer Science & Business Media. This book was released on 2012-12-28 with total page 132 pages. Available in PDF, EPUB and Kindle. Book excerpt: Human action analysis and recognition is a relatively mature field, yet one which is often not well understood by students and researchers. The large number of possible variations in human motion and appearance, camera viewpoint, and environment, present considerable challenges. Some important and common problems remain unsolved by the computer vision community. However, many valuable approaches have been proposed over the past decade, including the motion history image (MHI) method. This method has received significant attention, as it offers greater robustness and performance than other techniques. This work presents a comprehensive review of these state-of-the-art approaches and their applications, with a particular focus on the MHI method and its variants.

Download or read book Proceedings written by and published by . This book was released on 2002 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Visual Affect Recognition written by Ioanna-Ourania Stathopoulou and published by IOS Press. This book was released on 2010 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: It is generally known that human faces, as well as body motions and gestures, provide a wealth of information about a person, such as age, race, sex and emotional state. This monograph primarily studies the perception of facial expression of emotion, and secondarily of motion and gestures, with the purpose of developing a fully automated visual affect recognition system for use in modes of human/computer interaction. The book begins with a survey of the literature on emotion perception, followed by a decription of empirical studies conducted with human participants and the construction of a face image database . On the basis of this work, a visual affect recognition system was developed, consisting of two modules: a face detection subsystem and a facia expression recognition subsystem. Details of this system are demonstrated and analyzed, and extensive performance evaluations and test results are provided. Finally, current research avenues leading to visual affect recognition via analysis of body motin and gestures are also discussed."

Download or read book Gesture Recognition written by Amit Konar and published by Springer. This book was released on 2017-07-04 with total page 289 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a thorough analysis of gestural data extracted from raw images and/or range data with an aim to recognize the gestures conveyed by the data. It covers image morphological analysis, type-2 fuzzy logic, neural networks and evolutionary computation for classification of gestural data. The application areas include the recognition of primitive postures in ballet/classical Indian dances, detection of pathological disorders from gestural data of elderly people, controlling motion of cars in gesture-driven gaming and gesture-commanded robot control for people with neuro-motor disability. The book is unique in terms of its content, originality and lucid writing style. Primarily intended for graduate students and researchers in the field of electrical/computer engineering, the book will prove equally useful to computer hobbyists and professionals engaged in building firmware for human-computer interfaces. A prerequisite of high school level mathematics is sufficient to understand most of the chapters in the book. A basic background in image processing, although not mandatory, would be an added advantage for certain sections.

Download or read book Deep Learning for Image Processing Applications written by D.J. Hemanth and published by IOS Press. This book was released on 2017-12 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning and image processing are two areas of great interest to academics and industry professionals alike. The areas of application of these two disciplines range widely, encompassing fields such as medicine, robotics, and security and surveillance. The aim of this book, ‘Deep Learning for Image Processing Applications’, is to offer concepts from these two areas in the same platform, and the book brings together the shared ideas of professionals from academia and research about problems and solutions relating to the multifaceted aspects of the two disciplines. The first chapter provides an introduction to deep learning, and serves as the basis for much of what follows in the subsequent chapters, which cover subjects including: the application of deep neural networks for image classification; hand gesture recognition in robotics; deep learning techniques for image retrieval; disease detection using deep learning techniques; and the comparative analysis of deep data and big data. The book will be of interest to all those whose work involves the use of deep learning and image processing techniques.

Download or read book Challenges and Applications for Hand Gesture Recognition written by Kane, Lalit and published by IGI Global. This book was released on 2022-03-25 with total page 249 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Towards Chereme Based Dynamic Sign Language Gesture Recognition System written by Addmore Machanja and published by LAP Lambert Academic Publishing. This book was released on 2011-03 with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Hand gestures are a natural and intuitive way of human communication. Motivated by the achievements made towards automatic speech recognition, and by the ease with which people sign, many researchers started working on sign language recognition systems. Besides, technologies used to gesture recognition system pose as an alternative to the cumbersome and the failure prone mechanical devices that are currently used as human-machine interface devices. Most of the available gesture recognition systems represent each sign language gesture with an individual gesture model. Such systems can only recognize a limited number of dynamic sign language gestures. It is cumbersome to build and maintain a gesture recognition system that uses thousands and thousands of individual gesture models. Sign language linguists argue that all sign language gestures are derived from small sets of reusable components, the cheremes. However, computer vision is such as ill-posed problem to the extent that it very difficult to sufficiently detect the basic gesture components from image data during image processing. In most cases important gesture information is lost as a result of occlusion, image noise or during the process of transforming 3D world views into 2D projections. Gesture recognition systems that recognize a large vocabulary of sign language gestures can only be built if we devise image processing algorithms that achieve high quality hand segmentation and tracking. This research presents a multi-cue based segmentation method that helps to improve the extraction of the hand-shape chereme. A Support Vector Machine (SVM) is then used for verifying the hand-shapes that are associated with each input gesture. Hand sementation results directly affect the extraction of the hand position and hand movement cheremes. The hand movement patterns are learnt and recognized through the Hidden Markov Model (HMM). A sequence of cheremes that represent each geture is used to build an online gesture dictionary that helps the gesture recognition module to classify the input gestures. In this research, video footages of signing people are used as input gestures. Since the meaning of a gesture differs from society to society in this project we only focuses on dynamic gestures from the South African Sign Language (SASL). The technologies used in this project will find many applications in various fields of Human Computer Interaction (HCI)". Summary on page iv.

Download or read book Fast and Reliable Hand Action Recognition written by Jingxin Ou and published by . This book was released on 2014 with total page 38 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this work, we develop a hand action recognition method using a SVM (Support Vector Machine) classifier with HOG (Histogram of Oriented Gradients) features and Motion Vectors. Hand gesture recognition system analyzes the HOG feature using SVM. Hand action recognition system applies motion estimation to the input video, analyze the motion vectors, and then recognize the action using a SVM classifier. Our gesture recognition results show that this method is relatively insensitive to variations in illumination, camera perspective, and background variations. We tested our method on 10000 real life images, which captured on camera under different backgrounds and lighting conditions. We achieved a recognition rate of 94%. In the second part of this thesis, we focus on hand action recognition from videos. Background subtraction is used to obtain the foreground of moving objects. Conceptually, this recognition method is based on motion estimation, searches the block in the current frame, and finds the best match of it in the previous frame. Our hand action recognition results show that 74% of the actions can be successfully recognized.

Download or read book Advanced Computational Intelligence Paradigms in Healthcare 1 written by Hiroyuki Yoshida and published by Springer. This book was released on 2007-04-27 with total page 305 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents some of the most recent research results on the applications of computational intelligence in healthcare. The contents include: information model for management of clinical content; state-based model for management of type II diabetes; case-based reasoning in medicine; assessing the quality of care in AI environment; electronic medical record to examine physician decisions; multi-agent systems for the management of community healthcare; assistive wheelchair navigation; and more.