Design of Digital Map based on Hand Gesture as a Preservation of West Java History Sites for Elementary School

Social science study of historical content at present does not seem to be directly proportional to the development of the industrial revolution 4.0. it is because of rote learning styles, text-based, teacher-centered teaching methods without technologicalaided modifications. With these problems, it is necessary to have design innovations and media for learning models, one of them is to design a hand gesture-based map equipped with a leap motion controller. This study aims to design a digital hand gesture-based map design as the preservation of West Java historical sites for elementary school children. The method used in the study is Design and Development (D&D). The results of this study are the design of an interactive map as a teaching media innovation and the configuration response of the tool with the results of 13 ms / FPS 33 ms / 60 FPS tap gesture responses

Web-based Campus Virtual Tour Application using ORB Image Stitching

Information disclosure in the digital age has demanded the public to obtain information easily and meaningful. In this paper, we propose the development of web-based campus virtual tour 360-degree information system application at the State University of Malang, Indonesia which aims to introduce the assets of the institution in an interesting view to public. This application receives a stitched or panoramic image generated through the ORB image stitching algorithm as an input and displays it in virtual tour manner. This paper realizes the image stitching algorithm to present the visualization of the 360-degree dynamic building and campus environment, so it looks real as if it were in the actual location. Virtual tour approach can produce a more immersive and attractive appearance than regular photos

Facial feature point fitting with combined color and depth information for interactive displays

Interactive displays are driven by natural interaction with the user, necessitating a computer system that recognizes body gestures and facial expressions. User inputs are not easily or reliably recognized for a satisfying user experience, as the complexities of human communication are difficult to interpret in real-time. Recognizing facial expressions in particular is a problem that requires high-accuracy and efficiency for stable interaction environments. The recent availability of the Kinect, a low cost, low resolution sensor that supplies simultaneous color and depth images, provides a breakthrough opportunity to enhance the interactive capabilities of displays and overall user experience. This new RGBD (RGB + depth) sensor generates an additional channel of depth information that can be used to improve the performance of existing state of the art technology and develop new techniques. The Active Shape Model (ASM) is a well-known deformable model that has been extensively studied for facial feature point placement. Previous shape model techniques have applied 3D reconstruction techniques using multiple cameras or other statistical methods for producing 3D information from 2D color images. These methods showed improved results compared to using only color data, but required an additional deformable model or expensive imaging equipment. In this thesis, an ASM model is trained using the RGBD image produced by the Kinect. The real-time information from the depth sensor is registered to the color image to create a pixel-for-pixel match. To improve the quality of the depth image, a temporal median filter is applied to reduce random noise produced by the sensor. The resulting combined model is designed to produce more robust fitting of facial feature points compared to a purely color based active shape model

Embodied interaction with visualization and spatial navigation in time-sensitive scenarios

Paraphrasing the theory of embodied cognition, all aspects of our cognition are determined primarily by the contextual information and the means of physical interaction with data and information. In hybrid human-machine systems involving complex decision making, continuously maintaining a high level of attention while employing a deep understanding concerning the task performed as well as its context are essential. Utilizing embodied interaction to interact with machines has the potential to promote thinking and learning according to the theory of embodied cognition proposed by Lakoff. Additionally, the hybrid human-machine system utilizing natural and intuitive communication channels (e.g., gestures, speech, and body stances) should afford an array of cognitive benefits outstripping the more static forms of interaction (e.g., computer keyboard). This research proposes such a computational framework based on a Bayesian approach; this framework infers operator\u27s focus of attention based on the physical expressions of the operators. Specifically, this work aims to assess the effect of embodied interaction on attention during the solution of complex, time-sensitive, spatial navigational problems. Toward the goal of assessing the level of operator\u27s attention, we present a method linking the operator\u27s interaction utility, inference, and reasoning. The level of attention was inferred through networks coined Bayesian Attentional Networks (BANs). BANs are structures describing cause-effect relationships between operator\u27s attention, physical actions and decision-making. The proposed framework also generated a representative BAN, called the Consensus (Majority) Model (CMM); the CMM consists of an iteratively derived and agreed graph among candidate BANs obtained by experts and by the automatic learning process. Finally, the best combinations of interaction modalities and feedback were determined by the use of particular utility functions. This methodology was applied to a spatial navigational scenario; wherein, the operators interacted with dynamic images through a series of decision making processes. Real-world experiments were conducted to assess the framework\u27s ability to infer the operator\u27s levels of attention. Users were instructed to complete a series of spatial-navigational tasks using an assigned pairing of an interaction modality out of five categories (vision-based gesture, glove-based gesture, speech, feet, or body balance) and a feedback modality out of two (visual-based or auditory-based). Experimental results have confirmed that physical expressions are a determining factor in the quality of the solutions in a spatial navigational problem. Moreover, it was found that the combination of foot gestures with visual feedback resulted in the best task performance (p\u3c .001). Results have also shown that embodied interaction-based multimodal interface decreased execution errors that occurred in the cyber-physical scenarios (p \u3c .001). Therefore we conclude that appropriate use of interaction and feedback modalities allows the operators maintain their focus of attention, reduce errors, and enhance task performance in solving the decision making problems

Joint optimization of manifold learning and sparse representations for face and gesture analysis

Face and gesture understanding algorithms are powerful enablers in intelligent vision systems for surveillance, security, entertainment, and smart spaces. In the future, complex networks of sensors and cameras may disperse directions to lost tourists, perform directory lookups in the office lobby, or contact the proper authorities in case of an emergency. To be effective, these systems will need to embrace human subtleties while interacting with people in their natural conditions. Computer vision and machine learning techniques have recently become adept at solving face and gesture tasks using posed datasets in controlled conditions. However, spontaneous human behavior under unconstrained conditions, or in the wild, is more complex and is subject to considerable variability from one person to the next. Uncontrolled conditions such as lighting, resolution, noise, occlusions, pose, and temporal variations complicate the matter further. This thesis advances the field of face and gesture analysis by introducing a new machine learning framework based upon dimensionality reduction and sparse representations that is shown to be robust in posed as well as natural conditions. Dimensionality reduction methods take complex objects, such as facial images, and attempt to learn lower dimensional representations embedded in the higher dimensional data. These alternate feature spaces are computationally more efficient and often more discriminative. The performance of various dimensionality reduction methods on geometric and appearance based facial attributes are studied leading to robust facial pose and expression recognition models. The parsimonious nature of sparse representations (SR) has successfully been exploited for the development of highly accurate classifiers for various applications. Despite the successes of SR techniques, large dictionaries and high dimensional data can make these classifiers computationally demanding. Further, sparse classifiers are subject to the adverse effects of a phenomenon known as coefficient contamination, where for example variations in pose may affect identity and expression recognition. This thesis analyzes the interaction between dimensionality reduction and sparse representations to present a unified sparse representation classification framework that addresses both issues of computational complexity and coefficient contamination. Semi-supervised dimensionality reduction is shown to mitigate the coefficient contamination problems associated with SR classifiers. The combination of semi-supervised dimensionality reduction with SR systems forms the cornerstone for a new face and gesture framework called Manifold based Sparse Representations (MSR). MSR is shown to deliver state-of-the-art facial understanding capabilities. To demonstrate the applicability of MSR to new domains, MSR is expanded to include temporal dynamics. The joint optimization of dimensionality reduction and SRs for classification purposes is a relatively new field. The combination of both concepts into a single objective function produce a relation that is neither convex, nor directly solvable. This thesis studies this problem to introduce a new jointly optimized framework. This framework, termed LGE-KSVD, utilizes variants of Linear extension of Graph Embedding (LGE) along with modified K-SVD dictionary learning to jointly learn the dimensionality reduction matrix, sparse representation dictionary, sparse coefficients, and sparsity-based classifier. By injecting LGE concepts directly into the K-SVD learning procedure, this research removes the support constraints K-SVD imparts on dictionary element discovery. Results are shown for facial recognition, facial expression recognition, human activity analysis, and with the addition of a concept called active difference signatures, delivers robust gesture recognition from Kinect or similar depth cameras

Multimodale Interaktion in Multi-Display-Umgebungen

Interaktive Umgebungen entwickeln sich mehr und mehr weg von Einzelarbeitsplätzen, hin zu Multi-Display-/Multi-User-Umgebungen. Diese stellen neue Anforderungen an Eingabegeräte und Interaktionstechniken. Im Rahmen dieser Arbeit werden neue Ansätze zur Interaktion auf Basis von Handgesten und Blick als neuartige Eingabemodalitäten entwickelt und untersucht

Multimodale Interaktion in Multi-Display-Umgebungen

Interaktive Umgebungen entwickeln sich mehr und mehr weg von Einzelarbeitsplätzen, hin zu Multi-Display-/Multi-User-Umgebungen. Diese stellen neue Anforderungen an Eingabegeräte und Interaktionstechniken. Im Rahmen dieser Arbeit werden neue Ansätze zur Interaktion auf Basis von Handgesten und Blick als neuartige Eingabemodalitäten entwickelt und untersucht

Gaze estimation and interaction in real-world environments

Human eye gaze has been widely used in human-computer interaction, as it is a promising modality for natural, fast, pervasive, and non-verbal interaction between humans and computers. As the foundation of gaze-related interactions, gaze estimation has been a hot research topic in recent decades. In this thesis, we focus on developing appearance-based gaze estimation methods and corresponding attentive user interfaces with a single webcam for challenging real-world environments. First, we collect a large-scale gaze estimation dataset, MPIIGaze, the first of its kind, outside of controlled laboratory conditions. Second, we propose an appearance-based method that, in stark contrast to a long-standing tradition in gaze estimation, only takes the full face image as input. Second, we propose an appearance-based method that, in stark contrast to a long-standing tradition in gaze estimation, only takes the full face image as input. Third, we study data normalisation for the first time in a principled way, and propose a modification that yields significant performance improvements. Fourth, we contribute an unsupervised detector for human-human and human-object eye contact. Finally, we study personal gaze estimation with multiple personal devices, such as mobile phones, tablets, and laptops.Der Blick des menschlichen Auges wird in Mensch-Computer-Interaktionen verbreitet eingesetzt, da dies eine vielversprechende Möglichkeit für natürliche, schnelle, allgegenwärtige und nonverbale Interaktion zwischen Mensch und Computer ist. Als Grundlage von blickbezogenen Interaktionen ist die Blickschätzung in den letzten Jahrzehnten ein wichtiges Forschungsthema geworden. In dieser Arbeit konzentrieren wir uns auf die Entwicklung Erscheinungsbild-basierter Methoden zur Blickschätzung und entsprechender “attentive user interfaces” (die Aufmerksamkeit des Benutzers einbeziehende Benutzerschnittstellen) mit nur einer Webcam für anspruchsvolle natürliche Umgebungen. Zunächst sammeln wir einen umfangreichen Datensatz zur Blickschätzung, MPIIGaze, der erste, der außerhalb von kontrollierten Laborbedingungen erstellt wurde. Zweitens schlagen wir eine Erscheinungsbild-basierte Methode vor, die im Gegensatz zur langjährigen Tradition in der Blickschätzung nur eine vollständige Aufnahme des Gesichtes als Eingabe verwendet. Drittens untersuchen wir die Datennormalisierung erstmals grundsätzlich und schlagen eine Modifizierung vor, die zu signifikanten Leistungsverbesserungen führt. Viertens stellen wir einen unüberwachten Detektor für Augenkontakte zwischen Mensch und Mensch und zwischen Mensch und Objekt vor. Abschließend untersuchen wir die persönliche Blickschätzung mit mehreren persönlichen Geräten wie Handy, Tablet und Laptop

From head to toe:body movement for human-computer interaction

Our bodies are the medium through which we experience the world around us, so human-computer interaction can highly benefit from the richness of body movements and postures as an input modality. In recent years, the widespread availability of inertial measurement units and depth sensors led to the development of a plethora of applications for the body in human-computer interaction. However, the main focus of these works has been on using the upper body for explicit input. This thesis investigates the research space of full-body human-computer interaction through three propositions. The first proposition is that there is more to be inferred by natural users’ movements and postures, such as the quality of activities and psychological states. We develop this proposition in two domains. First, we explore how to support users in performing weight lifting activities. We propose a system that classifies different ways of performing the same activity; an object-oriented model-based framework for formally specifying activities; and a system that automatically extracts an activity model by demonstration. Second, we explore how to automatically capture nonverbal cues for affective computing. We developed a system that annotates motion and gaze data according to the Body Action and Posture coding system. We show that quality analysis can add another layer of information to activity recognition, and that systems that support the communication of quality information should strive to support how we implicitly communicate movement through nonverbal communication. Further, we argue that working at a higher level of abstraction, affect recognition systems can more directly translate findings from other areas into their algorithms, but also contribute new knowledge to these fields. The second proposition is that the lower limbs can provide an effective means of interacting with computers beyond assistive technology To address the problem of the dispersed literature on the topic, we conducted a comprehensive survey on the lower body in HCI, under the lenses of users, systems and interactions. To address the lack of a fundamental understanding of foot-based interactions, we conducted a series of studies that quantitatively characterises several aspects of foot-based interaction, including Fitts’s Law performance models, the effects of movement direction, foot dominance and visual feedback, and the overhead incurred by using the feet together with the hand. To enable all these studies, we developed a foot tracker based on a Kinect mounted under the desk. We show that the lower body can be used as a valuable complementary modality for computing input. Our third proposition is that by treating body movements as multiple modalities, rather than a single one, we can enable novel user experiences. We develop this proposition in the domain of 3D user interfaces, as it requires input with multiple degrees of freedom and offers a rich set of complex tasks. We propose an approach for tracking the whole body up close, by splitting the sensing of different body parts across multiple sensors. Our setup allows tracking gaze, head, mid-air gestures, multi-touch gestures, and foot movements. We investigate specific applications for multimodal combinations in the domain of 3DUI, specifically how gaze and mid-air gestures can be combined to improve selection and manipulation tasks; how the feet can support the canonical 3DUI tasks; and how a multimodal sensing platform can inspire new 3D game mechanics. We show that the combination of multiple modalities can lead to enhanced task performance, that offloading certain tasks to alternative modalities not only frees the hands, but also allows simultaneous control of multiple degrees of freedom, and that by sensing different modalities separately, we achieve a more detailed and precise full body tracking