Fundamental limits of linear filters in the visual processing of two-dimensional signals

Zetzsche, Christoph; Barth, Erhardt

doi:10.1016/0042-6989(90)90120-a

Cited by 168 publications

(124 citation statements)

References 16 publications

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“…In image processing, the intrinsic dimensionality was introduced by [42] and was used to formalise a discrete distinction between edge-like and junction-like structures. This corresponds to a classical interpretation of local image structures in computer vision.…”

Section: Intrinsic Dimensionalitymentioning

confidence: 99%

“…The intrinsic dimension (see, e.g., [42,10]) has proven to be a suitable descriptor that distinguishes such structures. Homogeneous image patches have an intrinsic dimension of zero (i0D); edge-like structures are intrinsically 1-dimensional (i1D) while junctions and most textures have an intrinsic dimension of two (i2D)z.…”

Section: Introductionmentioning

confidence: 99%

“…The association of intrinsic dimension to a local image structure has been done mostly by a discrete classification [42,10,18]. A continuous definition of intrinsic dimensionality has been recently given in [23,11].…”

Section: Introductionmentioning

confidence: 99%

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Local image structures and optic flow estimation

Kalkan

Calow

Wörgötter

et al. 2005

Network: Computation in Neural Systems

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Abstract.Different kinds of local image structures (such as homogeneous, edge-like and junctionlike patches) can be distinguished by the intrinsic dimensionality of the local signals. Intrinsic dimensionality makes use of variance from a point and a line in spectral representation of the signal in order to classify it as homogeneous, edge-like or junction-like. The concept of intrinsic dimensionality has been mostly exercised using discrete formulations; however, a recent work [23,11] has introduced a continuous definition. The current study analyzes the distribution of local patches in natural images according to this continuous understanding of intrinsic dimensionality. This distribution reveals specific patterns than can be also associated to local image structures established in computer vision and which can be related to orientation and optic flow features. In particular, we link quantitative and qualitative properties of opticflow error estimates to these patterns. In this way, we also introduce a new tool for better analysis of optic flow algorithms.

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Section: Intrinsic Dimensionalitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Local image structures and optic flow estimation

Kalkan

Calow

Wörgötter

et al. 2005

Network: Computation in Neural Systems

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“…But what type of changes? In our view, the most basic categorization of changes is based on the concept of intrinsic dimensionality that has been introduced for images in [13] and shown to be useful for modelling attention with static images [14].…”

Section: Predictions Based On Salient Featuresmentioning

confidence: 99%

Dynamic predictions of tracked gaze

Barth

Drewes

Martinetz

2003

Seventh International Symposium on Signal Processing and Its Applications, 2003. Proceedings.

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We present a model for predicting the eye-movements of observers who is viewing dynamic sequences of images. As an indicator for the degree of saliency we evaluate an invariant of the spatio-temporal structure tensor that indicates an intrinsic dimension of at least two. The saliency is used to derive a list of candidate locations. Out of this list, the currently attended location is selected according to a mapping found by supervised learning. The true locations used for learning are obtained with an eyetracker. In addition to the saliency-based candidates, the selection algorithm uses a limited history of locations attended in the past. The mapping is linear and can thus be quickly adapted to the individual observer. The mapping is optimal in the sense that it is obtained by minimizing, by gradient descent, the overall quadratic difference between the predicted and the actually attended location.

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“…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%

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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Fundamental limits of linear filters in the visual processing of two-dimensional signals

Cited by 168 publications

References 16 publications

Local image structures and optic flow estimation

Local image structures and optic flow estimation

Dynamic predictions of tracked gaze

Guiding Eye Movements for Better Communication and Augmented Vision

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