13,451 research outputs found

    Navigation and interaction in a real-scale digital mock-up using natural language and user gesture

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    This paper tries to demonstrate a very new real-scale 3D system and sum up some firsthand and cutting edge results concerning multi-modal navigation and interaction interfaces. This work is part of the CALLISTO-SARI collaborative project. It aims at constructing an immersive room, developing a set of software tools and some navigation/interaction interfaces. Two sets of interfaces will be introduced here: 1) interaction devices, 2) natural language (speech processing) and user gesture. The survey on this system using subjective observation (Simulator Sickness Questionnaire, SSQ) and objective measurements (Center of Gravity, COG) shows that using natural languages and gesture-based interfaces induced less cyber-sickness comparing to device-based interfaces. Therefore, gesture-based is more efficient than device-based interfaces.FUI CALLISTO-SAR

    Deep Feature-based Face Detection on Mobile Devices

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    We propose a deep feature-based face detector for mobile devices to detect user's face acquired by the front facing camera. The proposed method is able to detect faces in images containing extreme pose and illumination variations as well as partial faces. The main challenge in developing deep feature-based algorithms for mobile devices is the constrained nature of the mobile platform and the non-availability of CUDA enabled GPUs on such devices. Our implementation takes into account the special nature of the images captured by the front-facing camera of mobile devices and exploits the GPUs present in mobile devices without CUDA-based frameorks, to meet these challenges.Comment: ISBA 201

    Intimate interfaces in action: assessing the usability and subtlety of emg-based motionless gestures

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    Mobile communication devices, such as mobile phones and networked personal digital assistants (PDAs), allow users to be constantly connected and communicate anywhere and at any time, often resulting in personal and private communication taking place in public spaces. This private -- public contrast can be problematic. As a remedy, we promote intimate interfaces: interfaces that allow subtle and minimal mobile interaction, without disruption of the surrounding environment. In particular, motionless gestures sensed through the electromyographic (EMG) signal have been proposed as a solution to allow subtle input in a mobile context. In this paper we present an expansion of the work on EMG-based motionless gestures including (1) a novel study of their usability in a mobile context for controlling a realistic, multimodal interface and (2) a formal assessment of how noticeable they are to informed observers. Experimental results confirm that subtle gestures can be profitably used within a multimodal interface and that it is difficult for observers to guess when someone is performing a gesture, confirming the hypothesis of subtlety

    SymbolDesign: A User-centered Method to Design Pen-based Interfaces and Extend the Functionality of Pointer Input Devices

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    A method called "SymbolDesign" is proposed that can be used to design user-centered interfaces for pen-based input devices. It can also extend the functionality of pointer input devices such as the traditional computer mouse or the Camera Mouse, a camera-based computer interface. Users can create their own interfaces by choosing single-stroke movement patterns that are convenient to draw with the selected input device and by mapping them to a desired set of commands. A pattern could be the trace of a moving finger detected with the Camera Mouse or a symbol drawn with an optical pen. The core of the SymbolDesign system is a dynamically created classifier, in the current implementation an artificial neural network. The architecture of the neural network automatically adjusts according to the complexity of the classification task. In experiments, subjects used the SymbolDesign method to design and test the interfaces they created, for example, to browse the web. The experiments demonstrated good recognition accuracy and responsiveness of the user interfaces. The method provided an easily-designed and easily-used computer input mechanism for people without physical limitations, and, with some modifications, has the potential to become a computer access tool for people with severe paralysis.National Science Foundation (IIS-0093367, IIS-0308213, IIS-0329009, EIA-0202067

    The Evolution of First Person Vision Methods: A Survey

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    The emergence of new wearable technologies such as action cameras and smart-glasses has increased the interest of computer vision scientists in the First Person perspective. Nowadays, this field is attracting attention and investments of companies aiming to develop commercial devices with First Person Vision recording capabilities. Due to this interest, an increasing demand of methods to process these videos, possibly in real-time, is expected. Current approaches present a particular combinations of different image features and quantitative methods to accomplish specific objectives like object detection, activity recognition, user machine interaction and so on. This paper summarizes the evolution of the state of the art in First Person Vision video analysis between 1997 and 2014, highlighting, among others, most commonly used features, methods, challenges and opportunities within the field.Comment: First Person Vision, Egocentric Vision, Wearable Devices, Smart Glasses, Computer Vision, Video Analytics, Human-machine Interactio

    Touch Screen Avatar English Learning System For University Students Learning Simplicity

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    This paper discusses on touch screen avatar for an English language learning application system. The system would be a combination of avatar as Animated Pedagogical Agent (APA) and a touch screen application that adapt the up to date gesture-based computing which is found as having potential to change the way how we learn as it could reduce the amount of Information Communication Technology (ICT) devices used during teaching and learning process. The key here is interaction between university students and touch screen avatar intelligent application system as well as learning resources that could be learned anytime anywhere twenty four hours in seven days 24/7 based on their study time preference where they could learn at their own comfort out of the tradition. The students would be provided with a learning tool that could help them learn interactively with the current trend which they might be interested with based on their own personalization. Apart from that, their performance shall be monitored from a distance and evaluated to avoid disturbing their learning process from working smoothly and getting rid of feeling of being controlled. Thus, the students are expected to have lower affective filter level that may enhance the way they learn unconsciously. Keywords: Gesture-Based Computing, Avatar, Portable Learning Tool, Interactivity, Language Learnin

    Understanding face and eye visibility in front-facing cameras of smartphones used in the wild

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    Commodity mobile devices are now equipped with high-resolution front-facing cameras, allowing applications in biometrics (e.g., FaceID in the iPhone X), facial expression analysis, or gaze interaction. However, it is unknown how often users hold devices in a way that allows capturing their face or eyes, and how this impacts detection accuracy. We collected 25,726 in-the-wild photos, taken from the front-facing camera of smartphones as well as associated application usage logs. We found that the full face is visible about 29% of the time, and that in most cases the face is only partially visible. Furthermore, we identified an influence of users' current activity; for example, when watching videos, the eyes but not the entire face are visible 75% of the time in our dataset. We found that a state-of-the-art face detection algorithm performs poorly against photos taken from front-facing cameras. We discuss how these findings impact mobile applications that leverage face and eye detection, and derive practical implications to address state-of-the art's limitations
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