Separability of spatiotemporal spectra of image sequences

Eckert, Michael P.; Buchsbaum, Gershon; Watson, Andrew B.

doi:10.1109/34.177387

Cited by 23 publications

(6 citation statements)

References 12 publications

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“…It has also been shown that the pattern of change over time follows a power law. That is, if the contrast modulation over time for a given pixel is examined, the power spectra can also be modelled with P = 1/ f α , where P is the power as function of frequency f , and α is temporal spectral slope [BGK01, DA95, EBW92]. Temporal spectral slopes between 1.2 and 2.0 have been reported for natural image sequences.…”

Section: Second‐order Statisticsmentioning

confidence: 99%

A Survey of Image Statistics Relevant to Computer Graphics

Pouli

Cunningham

Reinhard

2011

Computer Graphics Forum

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The statistics of natural images have attracted the attention of researchers in a variety of fields and have been used as a means to better understand the human visual system and its processes. A number of algorithms in computer graphics, vision and image processing take advantage of such statistical findings to create visually more plausible results. With this report we aim to review the state of the art in image statistics and discuss existing and potential applications within computer graphics and related areas.

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Section: Second‐order Statisticsmentioning

confidence: 99%

A Survey of Image Statistics Relevant to Computer Graphics

Pouli

Cunningham

Reinhard

2011

Computer Graphics Forum

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“…Figure 7 shows how tracking modifies the instanta neous temporal frequency bandwidth in the region of tracking. The instantaneous temporal frequency band width was computed from the frame to frame correla tion, px, using a correlation model of the form = e -"M (Jayant & Noll 1984;Eckert et al 1992). For a frame rate of 30 frames per second, the temporal bandwidth can be computed as a = -3 0 log(pT).…”

Section: The Effect Of Tracking On Digitized Image Sequencesmentioning

confidence: 99%

Efficient coding of natural time varying images in the early visual system

Eckert

Buchsbaum

1993

Phil. Trans. R. Soc. Lond. B

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We investigate the hypothesis that the early visual system efficiently codes natural time varying images, first by tracking part of the image, then by matching the spatiotemporal properties of the neural pathway to those of the tracked image. A representation for the time varying image is formulated which consists of two spatiotemporal components, a velocity field component and a stationary component. We show, using digitized sequences of natural images, that the spatiotemporal spectrum and other attributes of the image markedly differ before and after tracking. The temporal frequency bandwidth and velocity distribution of the velocity field component are diminished in the region of tracking and broaden with increasing eccentricity from this region. On the other hand, the spectrum of the stationary component is unaffected by tracking. Comparison of the properties of the tracked image to those of the M and P pathways suggests that each pathway transmits different attributes of the tracked image. A retinal architecture which varies with eccentricity also matches the properties of the tracked image.

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“…A spatial and temporal response factorization was also performed similar to the method described by Mourão-Miranda et al [11]. Some previous studies have assessed formally the issue of separability [11,23]. Spatiotemporal factorization is an assumption implicit in conventional unsupervised approaches like ICA.…”

Section: Discussionmentioning

confidence: 99%

Dynamic Spatial Discrimination Maps of Discriminative Activation between Different Tasks Based on Support Vector Machines

Huang¹,

Chen²,

Yin³

2011

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As a set of supervised pattern recognition methods, support vector machines (SVMs) have been successfully applied to functional magnetic resonance imaging (fMRI) field, but few studies have focused on visualizing discriminative regions of whole brain between different cognitive tasks dynamically. This paper presents a SVM-based method for visualizing dynamically discriminative activation of whole-brain voxels between two kinds of tasks without any contrast. Our method provides a series of dynamic spatial discrimination maps (DSDMs), representing the temporal evolution of discriminative brain activation during a duty cycle and describing how the discriminating information changes over the duty cycle. The proposed method was applied to investigate discriminative brain functional activations of whole brain voxels dynamically based on a hand-motor task experiment. A set of DSDMs between left hand movement and right hand movement were reached. Our results demonstrated not only where but also when the discriminative activations of whole brain voxels occurred between left hand movement and right hand movement during one duty cycle

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Separability of spatiotemporal spectra of image sequences

Cited by 23 publications

References 12 publications

A Survey of Image Statistics Relevant to Computer Graphics

A Survey of Image Statistics Relevant to Computer Graphics

Efficient coding of natural time varying images in the early visual system

Dynamic Spatial Discrimination Maps of Discriminative Activation between Different Tasks Based on Support Vector Machines

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