18,728 research outputs found
A Comparison of Visualisation Methods for Disambiguating Verbal Requests in Human-Robot Interaction
Picking up objects requested by a human user is a common task in human-robot
interaction. When multiple objects match the user's verbal description, the
robot needs to clarify which object the user is referring to before executing
the action. Previous research has focused on perceiving user's multimodal
behaviour to complement verbal commands or minimising the number of follow up
questions to reduce task time. In this paper, we propose a system for reference
disambiguation based on visualisation and compare three methods to disambiguate
natural language instructions. In a controlled experiment with a YuMi robot, we
investigated real-time augmentations of the workspace in three conditions --
mixed reality, augmented reality, and a monitor as the baseline -- using
objective measures such as time and accuracy, and subjective measures like
engagement, immersion, and display interference. Significant differences were
found in accuracy and engagement between the conditions, but no differences
were found in task time. Despite the higher error rates in the mixed reality
condition, participants found that modality more engaging than the other two,
but overall showed preference for the augmented reality condition over the
monitor and mixed reality conditions
Sketch-based 3D Shape Retrieval using Convolutional Neural Networks
Retrieving 3D models from 2D human sketches has received considerable
attention in the areas of graphics, image retrieval, and computer vision.
Almost always in state of the art approaches a large amount of "best views" are
computed for 3D models, with the hope that the query sketch matches one of
these 2D projections of 3D models using predefined features.
We argue that this two stage approach (view selection -- matching) is
pragmatic but also problematic because the "best views" are subjective and
ambiguous, which makes the matching inputs obscure. This imprecise nature of
matching further makes it challenging to choose features manually. Instead of
relying on the elusive concept of "best views" and the hand-crafted features,
we propose to define our views using a minimalism approach and learn features
for both sketches and views. Specifically, we drastically reduce the number of
views to only two predefined directions for the whole dataset. Then, we learn
two Siamese Convolutional Neural Networks (CNNs), one for the views and one for
the sketches. The loss function is defined on the within-domain as well as the
cross-domain similarities. Our experiments on three benchmark datasets
demonstrate that our method is significantly better than state of the art
approaches, and outperforms them in all conventional metrics.Comment: CVPR 201
Interact: A Mixed Reality Virtual Survivor for Holocaust Testimonies
In this paper we present Interact---a mixed reality virtual survivor for Holocaust education. It was created to preserve the powerful and engaging experience of listening to, and interacting with, Holocaust survivors, allowing future generations of audience access to their unique stories. Interact demonstrates how advanced filming techniques, 3D graphics and natural language processing can be integrated and applied to specially-recorded testimonies to enable users to ask questions and receive answers from that virtualised individuals. This provides a new and rich interactive narratives of remembrance to engage with primary testimony. We discuss the design and development of Interact, and argue that this new form of mixed reality is promising media to overcome the uncanny valley
A comparative evaluation of interactive segmentation algorithms
In this paper we present a comparative evaluation of four popular interactive segmentation algorithms. The evaluation was carried out as a series of user-experiments, in which participants were tasked with extracting 100 objects from a common dataset: 25 with each algorithm, constrained within a time limit of 2 min for each object. To facilitate the experiments, a “scribble-driven” segmentation tool was developed to enable interactive image segmentation by simply marking areas of foreground and background with the mouse. As the participants refined and improved their respective segmentations, the corresponding updated segmentation mask was stored along with the elapsed time. We then collected and evaluated each recorded mask against a manually segmented ground truth, thus allowing us to gauge segmentation accuracy over time. Two benchmarks were used for the evaluation: the well-known Jaccard index for measuring object accuracy, and a new fuzzy metric, proposed in this paper, designed for measuring boundary accuracy. Analysis of the experimental results demonstrates the effectiveness of the suggested measures and provides valuable insights into the performance and characteristics of the evaluated algorithms
About the nature of Kansei information, from abstract to concrete
Designer’s expertise refers to the scientific fields of emotional design and kansei information. This paper aims to answer to a scientific major issue which is, how to formalize designer’s knowledge, rules, skills into kansei information systems. Kansei can be considered as a psycho-physiologic, perceptive, cognitive and affective process through a particular experience. Kansei oriented methods include various approaches which deal with semantics and emotions, and show the correlation with some design properties. Kansei words may include semantic, sensory, emotional descriptors, and also objects names and product attributes. Kansei levels of information can be seen on an axis going from abstract to concrete dimensions. Sociological value is the most abstract information positioned on this axis. Previous studies demonstrate the values the people aspire to drive their emotional reactions in front of particular semantics. This means that the value dimension should be considered in kansei studies. Through a chain of value-function-product attributes it is possible to enrich design generation and design evaluation processes. This paper describes some knowledge structures and formalisms we established according to this chain, which can be further used for implementing computer aided design tools dedicated to early design. These structures open to new formalisms which enable to integrate design information in a non-hierarchical way. The foreseen algorithmic implementation may be based on the association of ontologies and bag-of-words.AN
Fidelity metrics for virtual environment simulations based on spatial memory awareness states
This paper describes a methodology based on human judgments of memory awareness
states for assessing the simulation fidelity of a virtual environment (VE) in relation
to its real scene counterpart. To demonstrate the distinction between task
performance-based approaches and additional human evaluation of cognitive awareness
states, a photorealistic VE was created. Resulting scenes displayed on a headmounted
display (HMD) with or without head tracking and desktop monitor were
then compared to the real-world task situation they represented, investigating spatial
memory after exposure. Participants described how they completed their spatial
recollections by selecting one of four choices of awareness states after retrieval in
an initial test and a retention test a week after exposure to the environment. These
reflected the level of visual mental imagery involved during retrieval, the familiarity
of the recollection and also included guesses, even if informed. Experimental results
revealed variations in the distribution of participants’ awareness states across conditions
while, in certain cases, task performance failed to reveal any. Experimental
conditions that incorporated head tracking were not associated with visually induced
recollections. Generally, simulation of task performance does not necessarily
lead to simulation of the awareness states involved when completing a memory
task. The general premise of this research focuses on how tasks are achieved,
rather than only on what is achieved. The extent to which judgments of human
memory recall, memory awareness states, and presence in the physical and VE are
similar provides a fidelity metric of the simulation in question
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