scholarly journals Effects of spatial frequency bands on perceptual decision: It is not the stimuli but the comparison

2010 ◽  
Vol 9 (8) ◽  
pp. 806-806
Author(s):  
P. Rotshtein ◽  
A. Schofield ◽  
M. J. Funes ◽  
G. W. Humphreys
Keyword(s):  
Spatial Frequency ◽  
Frequency Bands ◽  
Perceptual Decision

scholarly journals Effects of spatial frequency bands on perceptual decision: It is not the stimuli but the comparison

2010 ◽  
Vol 10 (10) ◽  
pp. 25-25 ◽  
Author(s):  
P. Rotshtein ◽  
A. Schofield ◽  
M. J. Funes ◽  
G. W. Humphreys
Keyword(s):  
Spatial Frequency ◽  
Frequency Bands ◽  
Perceptual Decision

Extraction of phase profile at discrete spatial frequency bands using phase shifting interferometry

2019 ◽  
Author(s):  
Payel Ghosh ◽  
Sarad Subhra S. Bhakat ◽  
Ipsita Chakraborty ◽  
Sanjukta Sarkar ◽  
Kallol Bhattacharya
Keyword(s):  
Spatial Frequency ◽  
Phase Shifting ◽  
Frequency Bands ◽  
Phase Shifting Interferometry ◽  
Phase Profile

Discrimination of Changes in the Slopes of the Amplitude Spectra of Natural Images: Band-Limited Contrast and Psychometric Functions

Perception ◽  
1997 ◽  
Vol 26 (8) ◽  
pp. 1011-1025 ◽  
Author(s):  
David J Tolhurst ◽  
Yoav Tadmor
Keyword(s):  
Spatial Frequency ◽  
Discrimination Task ◽  
Spatial Scales ◽  
Natural Images ◽  
Natural Scenes ◽  
Local Contrast ◽  
Frequency Bands ◽  
Amplitude Spectra ◽  
Lower Frequency Band ◽  
Band Limited

Thresholds were measured for discriminating changes in the slopes of the amplitude spectra of stimuli derived from photographs of natural scenes and from random-luminance patterns. The variety and magnitudes of the thresholds could be explained by a model based on the discrimination of the changes in band-limited local contrast. Different spatial scales of local contrast (or different spatial-frequency bands of about 1 octave) were implicated for different reference spectral slopes; the model implicated a lower frequency-band for stimuli with shallower amplitude spectra. The implications of the model were tested experimentally by using stimuli in which the spectra were changed within restricted spatial-frequency bands. When the amplitude spectra of the test and reference stimuli differed only within the implicated frequency bands, thresholds were affected little. However, when the test and reference spectra differed at all frequencies except those in the implicated bands, the thresholds were elevated markedly. The forms of the psychometric functions for the discrimination task were entirely compatible with the hypothesis that the task relies upon the ability to discriminate changes of contrast. The Weibull functions fitted to the data had slope parameters (β) in the range 1 to 3, compatible with discrimination of low (but suprathreshold) contrasts.

Information reduction in holograms by superimposing spatial frequency bands

2003 ◽  
Author(s):  
Satoshi Nakazaki ◽  
Kunihiro Sato ◽  
Masakazu Morimoto ◽  
Kensaku Fujii
Keyword(s):  
Spatial Frequency ◽  
Frequency Bands ◽  
Information Reduction

Multi-Modal Medical Image Fusion Algorithm Based On Spatial Frequency Motivated PA-PCNN In NSST Domain

2020 ◽  
Vol 16 ◽  
Author(s):  
Vanitha Kamarthi ◽  
D. Satyanarayana ◽  
M.N. Giri Prasad
Keyword(s):  
Mutual Information ◽  
Spatial Frequency ◽  
Image Fusion ◽  
High Frequency ◽  
Medical Image ◽  
Medical Images ◽  
Low Frequency ◽  
Fusion Algorithm ◽  
Frequency Bands ◽  
Edge Strength

Background: Image fusion has been grown as an effectual method in diseases related diagnosis schemes. Methods: In this paper, a new method for combining multimodal medical images using spatial frequency motivated parameter-adaptive PCNN (SF-PAPCNN) is suggested. The multi-modal images are disintegrated into frequency bands by using decomposition NSST. The coefficients of low frequency bands are selected using maximum rule. The coefficients of high frequency bands are combined by SF-PAPCNN. Results: The fused medical images is obtained by applying INSST to above coefficients. Conclusion: The quality metrics such as entropy ENT, fusion symmetry FS, deviation STD, mutual information QMI and edge strength QAB/F are used to validate the efficacy of suggested scheme.

Discarding optimality: Throwing out the baby with the bathwater?

2018 ◽  
Vol 41 ◽  
Author(s):  
Patrick Simen ◽  
Fuat Balcı
Keyword(s):  
Decision Making ◽  
Perceptual Decision Making ◽  
Reward Rate ◽  
Perceptual Decision ◽  
Standard Observer ◽  
Rate Maximization ◽  
Reward Rate Maximization ◽  
Descriptive Standard ◽  
Observer Model

AbstractRahnev & Denison (R&D) argue against normative theories and in favor of a more descriptive “standard observer model” of perceptual decision making. We agree with the authors in many respects, but we argue that optimality (specifically, reward-rate maximization) has proved demonstrably useful as a hypothesis, contrary to the authors’ claims.

Determination of the Best Emulsion for the Microscopy of Crystals

1981 ◽  
Vol 39 ◽  
pp. 32-33
Author(s):  
David A. Grano ◽  
Kenneth H. Downing
Keyword(s):  
Spatial Frequency ◽  
Information Transfer ◽  
Signal To Noise Ratio ◽  
Experimental Situation ◽  
Photographic Emulsion ◽  
Modulation Transfer ◽  
Signal To Noise ◽  
Frequency Space ◽  
Noise Ratio

The retrieval of high-resolution information from images of biological crystals depends, in part, on the use of the correct photographic emulsion. We have been investigating the information transfer properties of twelve emulsions with a view toward 1) characterizing the emulsions by a few, measurable quantities, and 2) identifying the “best” emulsion of those we have studied for use in any given experimental situation. Because our interests lie in the examination of crystalline specimens, we've chosen to evaluate an emulsion's signal-to-noise ratio (SNR) as a function of spatial frequency and use this as our critereon for determining the best emulsion.The signal-to-noise ratio in frequency space depends on several factors. First, the signal depends on the speed of the emulsion and its modulation transfer function (MTF). By procedures outlined in, MTF's have been found for all the emulsions tested and can be fit by an analytic expression 1/(1+(S/S0)2). Figure 1 shows the experimental data and fitted curve for an emulsion with a better than average MTF. A single parameter, the spatial frequency at which the transfer falls to 50% (S0), characterizes this curve.

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