96,739 research outputs found
FĂschlĂĄr-DiamondTouch: collaborative video searching on a table
In this paper we present the system we have developed for our participation in the annual TRECVid benchmarking activity, specically the system we have developed, FĂschlĂĄr-DT, for participation in the interactive search
task of TRECVid 2005. Our back-end search engine uses a combination of a text search which operates over the automatic speech recognised text, and an image search which uses low-level image features matched against video keyframes. The two novel aspects of our work are the fact that we are evaluating collaborative, team-based search among groups of users working together, and that we are using a novel touch-sensitive tabletop interface and interaction device known as the DiamondTouch to support this collaborative search. The paper summarises the backend search systems as well as presenting the interface we have developed, in detail
Issues in conducting expert validation and review and user evaluation of the technology enhanced interaction framework and method
A Technology Enhanced Interaction Framework has been developed to support designers and developers design and develop technology enhanced interactions for complex scenarios involving disabled people. Issues of motivation, time, and understanding when validating and evaluating the Technology Enhanced Interaction Framework were identified through a literature review and questionnaires and interviews with experts. Changes to content, system, and approach were made in order to address the identified issues. Future work will involve detailed analysis of the expert review and validation findings and the implementation of a motivating approach to user evaluation
Interaction platform-orientated perspective in designing novel applications
The lack of HCI offerings in the invention of novel software applications and the bias of design knowledge towards desktop GUI make it difficult for us to design for novel scenarios and applications that leverage emerging computational technologies. These include new media platforms such as mobiles, interactive TV, tabletops and large multi-touch walls on which many of our future applications will operate. We argue that novel application design should come not from user-centred requirements engineering as in developing a conventional application, but from understanding the interaction characteristics of the new platforms. Ensuring general usability for a particular interaction platform without rigorously specifying envisaged usage contexts helps us to design an artifact that does not restrict the possible application contexts and yet is usable enough to help brainstorm its more exact place for future exploitation
Adaptive User Perspective Rendering for Handheld Augmented Reality
Handheld Augmented Reality commonly implements some variant of magic lens
rendering, which turns only a fraction of the user's real environment into AR
while the rest of the environment remains unaffected. Since handheld AR devices
are commonly equipped with video see-through capabilities, AR magic lens
applications often suffer from spatial distortions, because the AR environment
is presented from the perspective of the camera of the mobile device. Recent
approaches counteract this distortion based on estimations of the user's head
position, rendering the scene from the user's perspective. To this end,
approaches usually apply face-tracking algorithms on the front camera of the
mobile device. However, this demands high computational resources and therefore
commonly affects the performance of the application beyond the already high
computational load of AR applications. In this paper, we present a method to
reduce the computational demands for user perspective rendering by applying
lightweight optical flow tracking and an estimation of the user's motion before
head tracking is started. We demonstrate the suitability of our approach for
computationally limited mobile devices and we compare it to device perspective
rendering, to head tracked user perspective rendering, as well as to fixed
point of view user perspective rendering
Vision systems with the human in the loop
The emerging cognitive vision paradigm deals with vision systems that apply machine learning and automatic reasoning in order to learn from what they perceive. Cognitive vision systems can rate the relevance and consistency of newly acquired knowledge, they can adapt to their environment and thus will exhibit high robustness. This contribution presents vision systems that aim at flexibility and robustness. One is tailored for content-based image retrieval, the others are cognitive vision systems that constitute prototypes of visual active memories which evaluate, gather, and integrate contextual knowledge for visual analysis. All three systems are designed to interact with human users. After we will have discussed adaptive content-based image retrieval and object and action recognition in an office environment, the issue of assessing cognitive systems will be raised. Experiences from psychologically evaluated human-machine interactions will be reported and the promising potential of psychologically-based usability experiments will be stressed
Translating Video Recordings of Mobile App Usages into Replayable Scenarios
Screen recordings of mobile applications are easy to obtain and capture a
wealth of information pertinent to software developers (e.g., bugs or feature
requests), making them a popular mechanism for crowdsourced app feedback. Thus,
these videos are becoming a common artifact that developers must manage. In
light of unique mobile development constraints, including swift release cycles
and rapidly evolving platforms, automated techniques for analyzing all types of
rich software artifacts provide benefit to mobile developers. Unfortunately,
automatically analyzing screen recordings presents serious challenges, due to
their graphical nature, compared to other types of (textual) artifacts. To
address these challenges, this paper introduces V2S, a lightweight, automated
approach for translating video recordings of Android app usages into replayable
scenarios. V2S is based primarily on computer vision techniques and adapts
recent solutions for object detection and image classification to detect and
classify user actions captured in a video, and convert these into a replayable
test scenario. We performed an extensive evaluation of V2S involving 175 videos
depicting 3,534 GUI-based actions collected from users exercising features and
reproducing bugs from over 80 popular Android apps. Our results illustrate that
V2S can accurately replay scenarios from screen recordings, and is capable of
reproducing 89% of our collected videos with minimal overhead. A case
study with three industrial partners illustrates the potential usefulness of
V2S from the viewpoint of developers.Comment: In proceedings of the 42nd International Conference on Software
Engineering (ICSE'20), 13 page
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