7,376 research outputs found
Eye movements in surgery: A literature review
With recent advances in eye tracking technology, it is now possible to track surgeons’ eye movements while engaged in a surgical task or when surgical residents practice their surgical skills. Several studies have compared eye movements of surgical experts and novices, developed techniques to assess surgical skill on the basis of eye movements, and examined the role of eye movements in surgical training. We here provide an overview of these studies with a focus on the methodological aspects. We conclude that the different studies of eye movements in surgery suggest that the
recording of eye movements may be beneficial both for skill assessment and training purposes, although more research will be needed in this field
Expected exponential loss for gaze-based video and volume ground truth annotation
Many recent machine learning approaches used in medical imaging are highly
reliant on large amounts of image and ground truth data. In the context of
object segmentation, pixel-wise annotations are extremely expensive to collect,
especially in video and 3D volumes. To reduce this annotation burden, we
propose a novel framework to allow annotators to simply observe the object to
segment and record where they have looked at with a \$200 eye gaze tracker. Our
method then estimates pixel-wise probabilities for the presence of the object
throughout the sequence from which we train a classifier in semi-supervised
setting using a novel Expected Exponential loss function. We show that our
framework provides superior performances on a wide range of medical image
settings compared to existing strategies and that our method can be combined
with current crowd-sourcing paradigms as well.Comment: 9 pages, 5 figues, MICCAI 2017 - LABELS Worksho
Looking Beyond a Clever Narrative: Visual Context and Attention are Primary Drivers of Affect in Video Advertisements
Emotion evoked by an advertisement plays a key role in influencing brand
recall and eventual consumer choices. Automatic ad affect recognition has
several useful applications. However, the use of content-based feature
representations does not give insights into how affect is modulated by aspects
such as the ad scene setting, salient object attributes and their interactions.
Neither do such approaches inform us on how humans prioritize visual
information for ad understanding. Our work addresses these lacunae by
decomposing video content into detected objects, coarse scene structure, object
statistics and actively attended objects identified via eye-gaze. We measure
the importance of each of these information channels by systematically
incorporating related information into ad affect prediction models. Contrary to
the popular notion that ad affect hinges on the narrative and the clever use of
linguistic and social cues, we find that actively attended objects and the
coarse scene structure better encode affective information as compared to
individual scene objects or conspicuous background elements.Comment: Accepted for publication in the Proceedings of 20th ACM International
Conference on Multimodal Interaction, Boulder, CO, US
Contextual cropping and scaling of TV productions
This is the author's accepted manuscript. The final publication is available at Springer via http://dx.doi.org/10.1007/s11042-011-0804-3. Copyright @ Springer Science+Business Media, LLC 2011.In this paper, an application is presented which automatically adapts SDTV (Standard Definition Television) sports productions to smaller displays through intelligent cropping and scaling. It crops regions of interest of sports productions based on a smart combination of production metadata and systematic video analysis methods. This approach allows a context-based composition of cropped images. It provides a differentiation between the original SD version of the production and the processed one adapted to the requirements for mobile TV. The system has been comprehensively evaluated by comparing the outcome of the proposed method with manually and statically cropped versions, as well as with non-cropped versions. Envisaged is the integration of the tool in post-production and live workflows
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