scholarly journals Image Compressing Method for Checked Textile Designs by Using Distributions in the Spatial Frequency Domains

2000 ◽  
Vol 46 (1) ◽  
pp. 1-4
Author(s):  
Ken'ichi Ohta ◽  
Toshihiko Tanaka ◽  
Fujio Miyawaki
Keyword(s):  
Spatial Frequency ◽  
Frequency Domains ◽  
Image Compressing

Role of the color-opponent and broad-band channels in vision

1990 ◽  
Vol 5 (04) ◽  
pp. 321-346 ◽  
Author(s):  
Peter H. Schiller ◽  
Nikos K. Logothetis ◽  
Eliot R. Charles
Keyword(s):  
Spatial Frequency ◽  
Temporal Frequency ◽  
Broad Band ◽  
High Spatial Frequency ◽  
Brightness Perception ◽  
Rods And Cones ◽  
Form Vision ◽  
Temporal Domain ◽  
Frequency Domains

AbstractThe functions of the primate color-opponent and broad-band channels were assessed by examining the visual capacities of rhesus monkeys following selective lesions of parvocellular and magnocellular lateral geniculate nucleus, which respectively relay these two channels to the cortex. Parvocellular lesions impaired color vision, high spatial-frequency form vision, and fine stereopsis. Magnocellular lesions impaired high temporal- frequency flicker and motion perception but produced no deficits in stereopsis. Low spatial-frequency form vision, stereopsis, and brightness perception were unaffected by either lesion. Much as the rods and cones of the retina can be thought of as extending the range of vision in the intensity domain, we propose that the color-opponent channel extends visual capacities in the wavelength and spatial-frequency domains whereas the broad-band channel extends them in the temporal domain.

scholarly journals Joint Entropy for Space and Spatial Frequency Domains Estimated from Psychometric Functions of Achromatic Discrimination

PLoS ONE ◽  
2014 ◽  
Vol 9 (1) ◽  
pp. e86579 ◽  
Author(s):  
Vladímir de Aquino Silveira ◽  
Givago da Silva Souza ◽  
Bruno Duarte Gomes ◽  
Anderson Raiol Rodrigues ◽  
Luiz Carlos de Lima Silveira
Keyword(s):  
Spatial Frequency ◽  
Joint Entropy ◽  
Frequency Domains

scholarly journals A Framework of Combining Short-Term Spatial/Frequency Feature Extraction and Long-Term IndRNN for Activity Recognition

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6984
Author(s):  
Beidi Zhao ◽  
Shuai Li ◽  
Yanbo Gao ◽  
Chuankun Li ◽  
Wanqing Li
Keyword(s):  
Feature Extraction ◽  
Spatial Frequency ◽  
Activity Recognition ◽  
Sensor Data ◽  
Short Term ◽  
Temporal Features ◽  
Frequency Domains ◽  
Smartphone Sensors ◽  
Frequency Feature

Smartphone-sensors-based human activity recognition is attracting increasing interest due to the popularization of smartphones. It is a difficult long-range temporal recognition problem, especially with large intraclass distances such as carrying smartphones at different locations and small interclass distances such as taking a train or subway. To address this problem, we propose a new framework of combining short-term spatial/frequency feature extraction and a long-term independently recurrent neural network (IndRNN) for activity recognition. Considering the periodic characteristics of the sensor data, short-term temporal features are first extracted in the spatial and frequency domains. Then, the IndRNN, which can capture long-term patterns, is used to further obtain the long-term features for classification. Given the large differences when the smartphone is carried at different locations, a group-based location recognition is first developed to pinpoint the location of the smartphone. The Sussex-Huawei Locomotion (SHL) dataset from the SHL Challenge is used for evaluation. An earlier version of the proposed method won the second place award in the SHL Challenge 2020 (first place if not considering the multiple models fusion approach). The proposed method is further improved in this paper and achieves 80.72% accuracy, better than the existing methods using a single model.

Organisation and Interrelationships of Functional Maps in Cat and Monkey Striate Cortex

Perception ◽  
1997 ◽  
Vol 26 (1_suppl) ◽  
pp. 356-356
Author(s):  
D Shoham ◽  
M Hübener ◽  
T Bonhoeffer ◽  
A Grinvald
Keyword(s):  
Spatial Frequency ◽  
Cytochrome Oxidase ◽  
Striate Cortex ◽  
Temporal Frequency ◽  
Activity Patterns ◽  
Ocular Dominance ◽  
High Spatial Frequency ◽  
Spatial Frequencies ◽  
Frequency Domains

Optical imaging of intrinsic signals allows mapping of the cortical functional architecture in vivo at high spatial resolution. The ability to image activity patterns evoked by many different stimuli in the same piece of cortex can provide information on the spatial relationships between different functional maps. Our findings on the organisation of multiple functional maps in cat and monkey striate cortex are reviewed. The main focus is on the recent finding in cat of two subsystems differing in their response to spatiotemporal aspects of the stimulus. We used grating stimuli of different spatial frequencies in an attempt to verify the existence of spatial frequency columns in cat area 17. Rather than observing a map of continuously changing spatial frequency across the cortical surface we found two distinct sets of domains, one preferring low and one preferring high spatial frequencies. By using different drift velocities we also found that the low-spatial-frequency domains preferred higher speeds than the high-spatial-frequency domains. Comparison of these spatiotemporal frequency domains with the cytochrome oxidase staining pattern revealed that the cytochrome oxidase blobs in cat striate cortex coincide with domains devoted to the processing of the low-spatial-frequency and high-temporal-frequency contents of the visual scene. Together with recent anatomical results these data suggest that spatiotemporal frequency domains are the manifestation of parallel streams in cat visual cortex with distinct patterns of thalamic inputs and extrastriate projections. In the same experiments we also imaged the orientation preference and ocular dominance maps. We investigated the relationships between these three columnar systems, and compared them to an earlier study of orientation, ocular dominance, and blobs in macaque striate cortex. We found systematic relationships between the three systems. While some of these relationships were much weaker than those found in monkey, the organisational principles are similar.

scholarly journals The Representation of Complex Images in Spatial Frequency Domains of Primary Visual Cortex

2007 ◽  
Vol 27 (35) ◽  
pp. 9310-9318 ◽  
Author(s):  
J. X. Zhang ◽  
A. Rosenberg ◽  
A. K. Mallik ◽  
T. R. Husson ◽  
N. P. Issa
Keyword(s):  
Visual Cortex ◽  
Spatial Frequency ◽  
Primary Visual Cortex ◽  
Frequency Domains ◽  
Complex Images

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.

Density-based discrimination of protein and RNA in the ribosome

1992 ◽  
Vol 50 (1) ◽  
pp. 464-465
Author(s):  
Joachim Frank
Keyword(s):  
Transfer Function ◽  
Spatial Frequency ◽  
Three Dimensional ◽  
Wiener Filter ◽  
Dark Field Image ◽  
Dark Field ◽  
Frequency Range ◽  
Bright Field ◽  
Contrast Transfer Function ◽  
Pass Filter

Cryo-electron microscopy combined with single-particle reconstruction techniques has allowed us to form a three-dimensional image of the Escherichia coli ribosome.In the interior, we observe strong density variations which may be attributed to the difference in scattering density between ribosomal RNA (rRNA) and protein. This identification can only be tentative, and lacks quantitation at this stage, because of the nature of image formation by bright field phase contrast. Apart from limiting the resolution, the contrast transfer function acts as a high-pass filter which produces edge enhancement effects that can explain at least part of the observed variations. As a step toward a more quantitative analysis, it is necessary to correct the transfer function in the low-spatial-frequency range. Unfortunately, it is in that range where Fourier components unrelated to elastic bright-field imaging are found, and a Wiener-filter type restoration would lead to incorrect results. Depending upon the thickness of the ice layer, a varying contribution to the Fourier components in the low-spatial-frequency range originates from an “inelastic dark field” image. The only prospect to obtain quantitatively interpretable images (i.e., which would allow discrimination between rRNA and protein by application of a density threshold set to the average RNA scattering density may therefore lie in the use of energy-filtering microscopes.

Sign in / Sign up

Export Citation Format

Share Document