Picture Processing

Rosenfeld, Azriel; Weszka, Joan S.

doi:10.1007/978-94-011-9688-8_11

Cited by 55 publications

(82 citation statements)

References 4 publications

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“…Moreover, for group analyses, maps are often smoothed with Gaussian kernels of 5-15 mm FWHM. This is in line with the matched filter theorem according to which optimal smoothing kernel should match the spatial extent of the signal to be detected (Rosenfeld and Kak, 1982). Summing up all the sources of variability, the effective smoothness of group-averaged fMRI or PET maps is expected to be characterized by FWHMs of~10-20 mm.…”

Section: Contrast Maps In Other Imaging Modalitiessupporting

confidence: 66%

Contrasting functional imaging parametric maps: The mislocation problem and alternative solutions

2018

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A B S T R A C TIn the field of neuroimaging, researchers often resort to contrasting parametric maps to identify differences between conditions or populations. Unfortunately, contrast patterns mix effects related to amplitude and location differences and tend to peak away from sources of genuine brain activity to an extent that scales with the smoothness of the maps. Here, we illustrate this mislocation problem on source maps reconstructed from magnetoencephalographic recordings and propose a novel, dedicated location-comparison method. In realistic simulations, contrast mislocation was on average~10 mm when genuine sources were placed at the same location, and was still above 5 mm when sources were 20 mm apart. The dedicated location-comparison method achieved a sensitivity of~90% when inter-source distance was 12 mm. Its benefit is also illustrated on real brain-speech entrainment data. In conclusion, contrasts of parametric maps provide precarious information for source location. To specifically address the question of location difference, one should turn to dedicated methods as the one proposed here.

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Section: Contrast Maps In Other Imaging Modalitiessupporting

confidence: 66%

Contrasting functional imaging parametric maps: The mislocation problem and alternative solutions

2018

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“…tion (see Rosenfeld & Kak, 1982) and thresholding operation. The two most common edge-decision criteria are the simple thresholding operation illustrated in Figures 5 and 6 and the use of zero-crossings in the V 1 G images.…”

Section: Image-feature Extractionmentioning

confidence: 99%

“…Here, the prior information consists of at least some signal and background specifications, and the question is what filter (of g) best reveals the presence of ! in the image (the "matched filter problem"; see Rosenfeld & Kak, 1982).…”

Section: Pattern Recognition: Detecting Signals In Imagesmentioning

confidence: 99%

“…The simplest example of pattern recognition involves The solution to this problem is determined from what is termed the Cauchy-Schwarz inequality (see Hall, 1979, p. 481); that is, the cross-eorrelation between/and g is upper-bounded by: (Rosenfeld & Kak, 1982).…”

mentioning

confidence: 99%

“…A cross-eorrelator determines the likelihood of the signal's presence over the full image: both the what and the where. When n (x,y) in Equation 9 is white-noise, having (1) a flat power spectrum, (2) random phase spectrum, and (3) a Gaussian pixel histogram, then E, is constant and the crosscorrelator is optimal for detection (Rosenfeld & Kak, 1982).…”

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confidence: 99%

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Digital image-processing techniques for the display of images and modeling of visual perception

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Ranging a blooming picture by using partial autocorrelation

Sugiura

Aizawa

Harashima

1999

Electron. Comm. Jpn. Pt. II

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A new method of designing control sensors for intelligent transport systems (ITS) is proposed in which blooming is estimated by partial autocorrelation analysis, the distance from the focal point to the object is derived, and the distance to the object is measured. In partial autocorrelation analysis, forward prediction and backward prediction are carried out simultaneously so that the point spread function convoluted with the blooming image is linearly predicted and the amount of blooming is estimated. The two‐dimensional analysis requires a significant amount of processing and highly accurate operation. Therefore, in this method, by preprocessing in the frequency dimension, only the horizontal components are extracted for analysis. From the experimental results, it is found that distance estimation from a single image is possible near the focal length (2.5 m–1.7 m). © 1999 Scripta Technica, Electron Comm Jpn Pt 2, 82(1): 23–33, 1999

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Picture Processing

Cited by 55 publications

References 4 publications

Contrasting functional imaging parametric maps: The mislocation problem and alternative solutions

Contrasting functional imaging parametric maps: The mislocation problem and alternative solutions

Digital image-processing techniques for the display of images and modeling of visual perception

Ranging a blooming picture by using partial autocorrelation

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