Spatial Frequency Bands Research Articles

Perception of Facial Cues to Trustworthiness in Infancy: Insights from the Spatial Frequency Filtering Approach

AbstractA crucial aspect of human social competence is the ability to spontaneously and rapidly infer from facial cues whether others are likely to approach us with friendliness or hostility—that is, trustworthiness. The rapid and automatic nature of these inferences has prompted the claim that they may originate from evolutionary pressures to detect potential threats, thus enhancing our chances of survival. However, the developmental origins of this social skill remain a topic of ongoing debate. Recent evidence shows that infants' brains and looking time behaviors differentiate between faces varying along the trustworthiness continuum, but the question about the nature of the facial cues they rely on remains open. In this review, we propose the spatial frequency (SF) filtering approach (i.e., the selective removal of spatial frequency bands from the image) as a useful tool for investigating this question, and specifically whether infants' discrimination of facial expressions of emotion share common visual and neural mechanisms with discrimination of facial cues associated with trustworthiness inferences. The SF filtering approach could shed light on the neural and perceptual mechanisms underlying trustworthiness perception in infancy, providing insights into whether and how these mechanisms change across development. The evidence gathered through this method would prove critical to the understanding of the developmental origins of trustworthiness perception.

Subjective Affective Responses to Natural Scenes Require Understanding, Not Spatial Frequency Bands.

It is debated whether emotional processing and response depend on semantic identification or are preferentially tied to specific information in natural scenes, such as global features or local details. The present study aimed to further examine the relationship between scene understanding and affective response while manipulating visual content. To this end, we presented affective and neutral natural scenes which were progressively band-filtered to contain global features (low spatial frequencies) or local details (high spatial frequencies) and assessed both affective response and scene understanding. We observed that, if scene content was correctly reported, subjective ratings of arousal and valence were modulated by the affective content of the scene, and this modulation was similar across spatial frequency bands. On the other hand, no affective modulation of subjective ratings was observed if picture content was not correctly reported. The present results indicate that subjective affective response requires content understanding, and it is not tied to a specific spatial frequency range.

The role of visual spatial frequencies in newborns' processing of dynamic facial expressions of emotion.

Evidence on newborns' discrimination of emotional facial expressions is scarce, and the question of what is the nature of the visual information that newborns rely on to perform such discrimination remains open. Here, we manipulated the spatial frequency (SF) content of the stimuli by selectively removing low spatial frequency (LSF) and high spatial frequency bands using newborn-appropriate cutoffs to investigate what information newborns use when preferring and discriminating between dynamic displays showing happy and fearful expressions unfolding over time. Using a preferential looking paradigm, in Study 1 (N = 63, 59% females, 92% White), we showed that newborns looked longer to happy over fearful expressions in unfiltered (broad spatial frequency) and high-pass (high spatial frequency > 0.6 cycles per degree [cpd]) faces but not in low-pass (LSF < 0.5 cpd) faces. In Study 2 (N = 22, 59% females, 91% White), newborns tested in a visual habituation paradigm showed successful discrimination of the two LSF emotions. Results show that newborns can discriminate between dynamic images of happy and fearful facial expressions containing either extreme low SF (< 0.5 cpd) information or higher SF (> 0.6 cpd) bandwidth. Their preference for the happy expression was present for intact and high-pass filtered faces but not for low-pass faces. This SF effect is tentatively driven by an enhancement of attentional response to the LSF fearful face, whereas the response to the happy face is unaffected by the SF manipulation. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The processing of spatial frequencies through time in visual word recognition

This study examined the temporal profile of spatial frequency processing in a word reading task in 16 normal adult readers. They had to report the word presented in a 200 ms display using a four-alternative forced-choice task (4AFC). The stimuli were made of an additive combination of the signal (i.e. the target word) and of a visual white noise patch wherein the signal-to-noise ratio varied randomly across stimulus duration. Four spatial frequency conditions were defined for the signal component of the stimulus (bandpass Butterworth filters with center frequencies of 1.2, 2.4, 4.8 and 9.6 cycles per degree). In contrast to the coarse-to-fine theory of visual recognition, the results show that the highest spatial frequency range dominates early processing, with a shift toward lower spatial frequencies at later points during stimulus exposure. This pattern interacted in a complex way with the temporal frequency content of signal-to-noise oscillations. The outcome of individual data patterns classification by a machine learning algorithm according to the corresponding spatial frequency band further shows that the most salient spatial frequency signature is obtained when the time dimension within data patterns is recoded into its Fourier transform.

Elastoplastic contact model of pitch-based rough surface and its polishing characteristics

Fused silica glass is widely used in optical systems, including astronomical telescopes, laser systems, optical communications, and the semiconductor industry. At the same time, the surface quality of the fused silica directly determines the performance and precision of the system. In order to analyze the microscopic surface interaction based on the basis of tribology, a roughness contact model of pitch and fused silica glass surfaces was established. Analyze the performance parameters of contact materials, surface roughness, and the relationship between load and contact area. Pitch materials with a higher plasticity index have a larger elastoplastic contact area with the fused silica surface during the polishing process. The experimental results demonstrate that the surface quality of the polished fused silica improves as the plasticity index of the pitch material increases. At the same time, judging from the PSD curve results, the polished surface of the No. 55 pitch on the spatial-frequency band curve (100-101/mm) is significantly lower than the other two brands of pitch. Additionally, the Ra value of the workpiece surface roughness reaches 0.091 nm. The results of this study provide important theoretical guidance for achieving full-diameter, full-frequency ultra-smooth polishing of large-diameter complex curved surfaces.

The effects of spatial frequency on the decoding of emotional facial expressions.

The accurate identification of emotion is critical to effectively navigating our social lives. However, it is not clear how distinct types of visual information afford the accurate perception of others' emotion states. Here, we sought to examine the influence of different spatial frequency visual information on emotion categorization, and whether distinctive emotional dimensions (valence and arousal) are differentially influenced by specific spatial frequency content. Across one pilot and two experiments (N = 603), we tested whether emotional facial expressions that vary in valence, arousal, and functional significance differ in accuracy of categorization as a function of low, intact, and high spatial frequency band information. Overall, we found a general decrease in the breadth of emotional expressions for filtered images but did not see a decrease in accuracy of categorization for the positive emotion, joy. Together, these results suggest that spatial frequency information influences perception of emotional expressions that differ in valence and arousal. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Efficient multi-scale representation of visual objects using a biologically plausible spike-latency code and winner-take-all inhibition

Deep neural networks have surpassed human performance in key visual challenges such as object recognition, but require a large amount of energy, computation, and memory. In contrast, spiking neural networks (SNNs) have the potential to improve both the efficiency and biological plausibility of object recognition systems. Here we present a SNN model that uses spike-latency coding and winner-take-all inhibition (WTA-I) to efficiently represent visual stimuli using multi-scale parallel processing. Mimicking neuronal response properties in early visual cortex, images were preprocessed with three different spatial frequency (SF) channels, before they were fed to a layer of spiking neurons whose synaptic weights were updated using spike-timing-dependent-plasticity. We investigate how the quality of the represented objects changes under different SF bands and WTA-I schemes. We demonstrate that a network of 200 spiking neurons tuned to three SFs can efficiently represent objects with as little as 15 spikes per neuron. Studying how core object recognition may be implemented using biologically plausible learning rules in SNNs may not only further our understanding of the brain, but also lead to novel and efficient artificial vision systems.

Influence of nanoparticle dispersion in a transparent liquid on the spatial characteristics of a four-wave radiation converter

Abstract – The spatial characteristics of a four-wave radiation converter in a transparent heterogeneous polydisperse medium are studied in this paper considering the nanoparticle flow caused by the gravity action with a normal size distribution of particle. Three ranges of average nanoparticle radii (small, intermediate and large), for which different types of spatial spectra of the object waves are characteristic, are distinguished. Shown that in the range of small average nanoparticle radii an increase in the standard deviation leads to an increase in the half-width of the spatial frequency band cut out by a four-wave radiation converter from the spatial spectrum of the object wave. In the range of intermediate average nanoparticle radii an increase in the standard deviation can lead to both an increase and a decrease in the width of the «ring» cut out by a four-wave radiation converter. At large average nanoparticle radii a change in the standard deviation doesn’t affect the spatial selectivity of the four-wave radiation converter.

Influence of Large-Aperture Output Wavefront Distribution on Focal Spot in High-Power Laser Facility

To improve the focal spot quality, the output wavefront of the Shenguang-II Upgrade facility is divided into four types based on the spatial frequency and division band of power spectral density. The influence of each on the focal spot was quantitatively studied, and the results indicate that the spatial profile, energy concentration, and peak intensity of the focal spot are mainly affected by low-spatial-frequency, and the relative intensity of the sidelobes is greatly affected by both the low- and mid-spatial counterparts. The peak-to-valley value of the wavefront of the Shenguang-II Upgrade should not exceed 2.27λ under the requirement that 50% of the energy is enclosed within 3 times the diffraction limit (DL), and it should be less than 2.45λ under the requirement that 95% is within 10DL. Meanwhile, the Strehl ratio dropped to within 0.2 under these conditions. These results can be applied to focal quality improvement in designing an adaptive optical system, optical element processing, as well as focal spot prediction in high-power laser facilities.

Multiscale Analysis for Improving Texture Classification

Information from an image occurs over multiple and distinct spatial scales. Image pyramid multiresolution representations are a useful data structure for image analysis and manipulation over a spectrum of spatial scales. This paper employs the Gaussian–Laplacian pyramid to separately treat different spatial frequency bands of a texture. First, we generate three images corresponding to three levels of the Gaussian–Laplacian pyramid for an input image to capture intrinsic details. Then, we aggregate features extracted from gray and color texture images using bioinspired texture descriptors, information-theoretic measures, gray-level co-occurrence matrix feature descriptors, and Haralick statistical feature descriptors into a single feature vector. Such an aggregation aims at producing features that characterize textures to their maximum extent, unlike employing each descriptor separately, which may lose some relevant textural information and reduce the classification performance. The experimental results on texture and histopathologic image datasets have shown the advantages of the proposed method compared to state-of-the-art approaches. Such findings emphasize the importance of multiscale image analysis and corroborate that the descriptors mentioned above are complementary.

Medical image fusion using convolutional neural network

Medical image fusion methods combine medical pictures from many morphologies to improve the accuracy and reliability of medical diagnoses, and they are becoming more significant in a variety of clinical applications. This research introduces a convolutional neural network (CNN) based medical image fusion approach to create a fused picture with good visual quality and clear structural details. To generate a weight map, the proposed technique employs a trained Siamese convolutional network to fuse the pixel activity information of source pictures. Meanwhile, the original picture is decomposed using a contrast pyramid. Source pictures are combined using distinct spatial frequency bands and a weighted fusion operator. The suggested fusion method can successfully maintain the exact structural information of source pictures and generate excellent human visual effects, according to the findings of comparison trials.

A Multiscale Approach to Deep Blind Image Quality Assessment.

Faithful measurement of perceptual quality is of significant importance to various multimedia applications. By fully utilizing reference images, full-reference image quality assessment (FR-IQA) methods usually achieves better prediction performance. On the other hand, no-reference image quality assessment (NR-IQA), also known as blind image quality assessment (BIQA), which does not consider the reference image, makes it a challenging but important task. Previous NR-IQA methods have focused on spatial measures at the expense of information in the available frequency bands. In this paper, we present a multiscale deep blind image quality assessment method (BIQA, M.D.) with spatial optimal-scale filtering analysis. Motivated by the multi-channel behavior of the human visual system and contrast sensitivity function, we decompose an image into a number of spatial frequency bands by multiscale filtering and extract features for mapping an image to its subjective quality score by applying convolutional neural network. Experimental results show that BIQA, M.D. compares well with existing NR-IQA methods and generalizes well across datasets.

Spatial and temporal frequency band changes during infarct induction, infarct progression, and spreading depolarizations in the gyrencephalic brain.

To describe the spatial and temporal electrocorticographic (ECoG) changes after middle cerebral artery occlusion (MCAo), including those caused by spreading depolarization (SD) in the pig brain. The left middle cerebral arteries (MCAs) were clipped in six pigs. The clipping procedure lasted between 8 and 12 min, achieving a permanent occlusion (MCAo). Five-contact ECoG stripes were placed bilaterally over the frontoparietal cortices corresponding to the irrigation territory of the MCA and anterior cerebral artery (ACA). ECoG recordings were performed around 24 h: 1 h before and 23 h after the MCAo, and SDs were quantified. Five-minute ECoG signal segments were sampled before, 5 min, and 4, 8, and 12 h after cerebral artery occlusion and before, during, and after the negative direct current shift of the SDs. The power spectrum of the signals was decomposed into delta, theta, alpha, beta, and gamma bands. Descriptive statistics, Wilcoxon matched-pairs signed-rank tests, and Friedman tests were performed. Electrodes close to the MCAo showed instant decay in all frequency bands and SD onset during the first 5 h. Electrodes far from the MCAo exhibited immediate loss of fast frequencies and progressive decline of slow frequencies with an increased SD incidence between 6 and 14 h. After 8 h, the ACA electrode reported a secondary reduction of all frequency bands except gamma and high SD incidence within 12-17 h. During the SD, all electrodes showed a decline in all frequency bands. After SD passage, frequency band recovery was impaired only in MCA electrodes. ECoG can identify infarct progression and secondary brain injury. Severe disturbances in all the frequency bands are generated in the cortices where the SDs are passing by.

A Novel Method of Emotion Recognition from Multi-Band EEG Topology Maps Based on ERENet

EEG-based emotion recognition research has become a hot research topic. However, many studies focus on identifying emotional states from time domain features, frequency domain features, and time-frequency domain features of EEG signals, ignoring the spatial information and frequency band characteristics of the EEG signals. In this paper, an emotion recognition method based on multi-band EEG topology maps is proposed by combining the frequency domain features, spatial information, and frequency band characteristics of multi-channel EEG signals. In this method, multi-band EEG topology maps are introduced to present EEG signals, and a novel emotion recognition network, ERENet, is proposed to recognize emotional states from multi-band EEG topology maps. The results on the DEAP dataset show that the performance of ERENet outperforms that of most of the current methods.

Four-wave mixing on thermal nonlinearity with feedback for signal or object waves

The coincidence of the amplitude reflection coefficients, half-widths of the spatial frequencies bands of a four-wave radiation converter on thermal nonlinearity with feedback for signal or object waves has been shown at equal pump waves intensity. The dependence of the amplitude reflection coefficient, spatial selectivity of the four-wave radiation converter on thermal nonlinearity on the pump waves intensity has been analyzed. An increase in the half-width of the spatial frequency band is observed with an increase in the pump waves intensity at the compensation for the phase shift arising as a result of the propagation of the pump waves in the medium. There is a threshold value of the pump waves intensity, above which the value of the amplitude reflection coefficient of the four-wave converter with feedback for signal or object waves, is less than in the absence of feedback.

Combining Spatial and Temporal Data to Create a Fine-Resolution Daily Urban Air Temperature Product from Remote Sensing Land Surface Temperature (LST) Data

Remotely sensed land surface temperature (LST) is often used as a proxy for air temperature in urban heat island studies, particularly to illustrate relative temperature differences between locations. Two sensors are used predominantly in the literature, Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS). However, each has shortcomings that currently limit its utility for many urban applications. Landsat has high spatial resolution but low temporal resolution, and may miss hot days, while MODIS has high temporal resolution but low spatial resolution, which is inadequate to represent the fine grain heterogeneity in cities. In this paper, we overcome this inadequacy by combining high spatial frequency Environmental Services (ES), Landsat-driven Normalized Difference Vegetation Index (NDVI), and MODIS low spatial frequency background LST at different spatial frequency bands (spatial spectral composition). The method is able to provide fine scale LST four times daily on any day of the year. Using data from Paris in 2019 we show that (1) daytime cooling by vegetation reaches a maximum of 30 °C, above which there is no further increase in cooling. In addition, (2) the cooling is relatively local and does not extend further than 200 m beyond the boundary of the NBS. This model can be used to quantify the benefits of NBS in providing cooling in cities.

Spatial frequency bands used by patients with glaucoma to recognize facial expressions

ABSTRACT The authors investigated the influence of spatial frequencies in foveal vision in glaucomatous patients in a recognition task of facial expressions. Nineteen patients, 16 age-matched and 14 young controls saw centrally presented photographs of faces. Participants categorized the facial expressions as happy, angry or neutral. Two versions were tested: filtered faces of either low (LSF) or high (HSF) spatial frequency content and hybrid faces constructed from a face with LSF content superimposed on a face with HSF content with differing facial expressions. Compared to age-matched controls a significant deficit was observed both on HSF and on LSF filtered faces for patients. Controls, but not patients, were biased towards the HSF component of the hybrid faces. Different spatial frequencies are normally used to recognize different facial expressions. Impaired processing of a spatial frequency bandwidth due to an ocular pathology can therefore lead to misinterpretation of emotions, and impact on social interactions.

A Spatio-Temporal Method for Extracting Gamma-Band Features to Enhance Classification in a Rapid Serial Visual Presentation Task.

Rapid serial visual presentation (RSVP) is a type of electroencephalogram (EEG) pattern commonly used for target recognition. Besides delta- and theta-band responses already used for classification, RSVP task also evokes gamma-band responses having low amplitude and large individual difference. This paper proposes a filter bank spatio-temporal component analysis (FBSCA) method, extracting spatio-temporal features of the gamma-band responses for the first time, to enhance the RSVP classification performance. Considering the individual difference in time latency and responsive frequency, the proposed FBSCA method decomposes the gamma-band EEG data into sub-components in different time-frequency-space domains and seeks the weight coefficients to optimize the combinations of electrodes, common spatial pattern (CSP) components, time windows and frequency bands. Two state-of-the-art methods, i.e. hierarchical discriminant principal component analysis (HDPCA) and discriminative canonical pattern matching (DCPM), were used for comparison. The performance was evaluated in [Formula: see text] cross validations using a public dataset. Study results showed that the FBSCA method outperformed the other methods regardless of number of training trials. These results suggest that the proposed FBSCA method can enhance the RSVP classification.

A classification method for EEG motor imagery signals based on parallel convolutional neural network

Deep learning has been used popularly and successfully in state of art researches to classify different types of images. However, so far, the applications of deep learning methods for the electroencephalography (EEG) motor imagery classification is very limited. In this study, a pre-processing algorithm is proposed for the EEG signals representation. Then, a parallel convolutional neural network (PCNN) architecture is proposed to classify motor imagery signals. For the raw EEG signals representation, a new form of the images is created to combine spatial filtering and frequency bands extracting together. By feeding the represented images into the PCNN, it stacks three unique sub-models together aiming to optimize the performance of classification. The average accuracy of the proposed method achieves 83.0 ± 3.4% on BCI Competition IV dataset 2b, which outperforms the compared methods at least 5.2%. The average Kappa value of the proposed method achieves 0.659 ± 0.067 on dataset 2b, providing at least 20.5% improvement with respect to the compared algorithms. The results show that the proposed method performs better in EEG motor imagery signals classification.

Pixel MTF response effect on non-null interferometry.

The pixel modulation transfer function response degrades the contrast of non-null interferometric surface figure measurements. We experimentally quantify this effect for spatial frequencies ranging from 0 to 363 lp/mm (≈3.33 times the Nyquist limit). Our results show a low SNR spatial frequency band that behaves like a low-pass filter for sub-Nyquist interferometry and a stop-band filter for multiple-wavelength phase-shifting interferometry. We also introduce a multiple-mode, multiple-wavelength interferometry approach to measure optical surfaces with slope departure angles mapping to spatial frequencies in this low SNR band. The extended measurement range of this approach is achieved without using a sparse-array detector.