Eye movement predictions on natural videos

Böhme, Martin; Dörr, Michael; Krause, Christopher; Martinetz, Thomas; Barth, Erhardt

doi:10.1016/j.neucom.2005.11.019

Cited by 28 publications

(20 citation statements)

References 9 publications

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“…This fits well with our previous analysis of eye movements on high resolution natural videos, which shows that eye movements tend to cluster in rather few (10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20) locations [3]. We then reported on simple experiments that were meant to increase the saliency by a brief gazecontingent presentation of red dots in the periphery.…”

Section: Discussionsupporting

confidence: 85%

“…As described previously [5,6], our approach to saliency is based on the concept of intrinsic dimension that was introduced for still images in [7]. The intrinsic dimension of a signal at a particular location is the number of directions in which the signal is locally non-constant.…”

Section: Eye Movement Predictionsmentioning

confidence: 99%

“…To extract salient features on different spatial and temporal scales, we construct a 4-level spatio-temporal Gaussian pyramid from the image sequence and compute the saliency measures on each level. Details of how we select the candidate locations from the saliency map are given in [11]. We have used other invariants of the structure tensor (trace and sum of minors that indicate intrinsic dimensions of at least one or two respectively) and found prediction results that were slightly worse.…”

Section: Eye Movement Predictionsmentioning

confidence: 99%

“…The superposition of these Gaussians then yields the empirical saliency map. For a detailed description see [6].…”

Section: Eye Movement Predictionsmentioning

confidence: 99%

See 3 more Smart Citations

Guiding Eye Movements for Better Communication and Augmented Vision

Barth

Dörr

Böhme

et al. 2006

Perception and Interactive Technologies

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Abstract. This paper briefly summarises our results on gaze guidance such as to complement the demonstrations that we plan to present at the workshop. Our goal is to integrate gaze into visual communication systems by measuring and guiding eye movements. Our strategy is to predict a set of about ten salient locations and then change the probability for one of these candidates to be attended: for one candidate the probability is increased, for the others it is decreased. To increase saliency, in our current implementation, we show a natural-scene movie and overlay red dots very briefly such that they are hardly perceived consciously. To decrease the probability, for example, we locally reduce the temporal frequency content of the movie. We here present preliminary results, which show that the three steps of our above strategy are feasible. The long-term goal is to find the optimal real-time video transformation that minimises the difference between the actual and the desired eye movements without being obtrusive. Applications are in the area of vision-based communication, augmented vision, and learning.

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Section: Discussionsupporting

confidence: 85%

Section: Eye Movement Predictionsmentioning

confidence: 99%

Section: Eye Movement Predictionsmentioning

confidence: 99%

“…The superposition of these Gaussians then yields the empirical saliency map. For a detailed description see [6].…”

Section: Eye Movement Predictionsmentioning

confidence: 99%

See 2 more Smart Citations

Guiding Eye Movements for Better Communication and Augmented Vision

Barth

Dörr

Böhme

et al. 2006

Perception and Interactive Technologies

Self Cite

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“…Especially in Hollywood movies, the coherence was very high due to camera work and scene cuts. Most likely, target regions are of high saliency [21], [22]. As our approach reduces saliency in the areas outside the object of interest, it can be seen as an extension to those classic movie techniques to subtly draw attention to specific regions.…”

Section: Related Workmentioning

confidence: 99%

Temporal Video Filtering and Exposure Control for Perceptual Motion Blur

Stengel

Bauszat

Eisemann

et al. 2015

IEEE Trans. Visual. Comput. Graphics

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Abstract-We propose the computation of a perceptual motion blur in videos. Our technique takes the predicted eye motion into account when watching the video. Compared to traditional motion blur recorded by a video camera our approach results in a perceptual blur that is closer to reality. This postprocess can also be used to simulate different shutter effects or for other artistic purposes. It handles real and artificial video input, is easy to compute and has a low additional cost for rendered content. We illustrate its advantages in a user study using eye tracking.Index Terms-high frame rate, temporal filtering, perception, sharpening and blur ✦

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A Learned Saliency Predictor for Dynamic Natural Scenes

Vig

Dörr

Martinetz

et al. 2010

Artificial Neural Networks – ICANN 2010

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Abstract. We investigate the extent to which eye movements in natural dynamic scenes can be predicted with a simple model of bottom-up saliency, which learns on different visual representations to discriminate between salient and less salient movie regions. Our image representations, the geometrical invariants of the structure tensor, are computed on multiple scales of an anisotropic spatio-temporal multiresolution pyramid. Eye movement data is used to label video locations as salient. For each location, low-dimensional features are extracted on the multiscale representations and used to train a classifier. The quality of the predictor is tested on a large test set of eye movement data and compared with the performance of two state-of-the-art saliency models on this data set. The proposed model demonstrates significant improvement -mean ROC score of 0.665 -over the selected baseline models with ROC scores of 0.625 and 0.635.

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Eye movement predictions on natural videos

Cited by 28 publications

References 9 publications

Guiding Eye Movements for Better Communication and Augmented Vision

Guiding Eye Movements for Better Communication and Augmented Vision

Temporal Video Filtering and Exposure Control for Perceptual Motion Blur

A Learned Saliency Predictor for Dynamic Natural Scenes

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