scholarly journals A possible linear filtering model to explain White’s illusion at different grating widths

2017 ◽  
Author(s):  
Soma Mitra ◽  
Deabasis Mazumdar ◽  
Kuntal Ghosh ◽  
Kamales Bhaumik
Keyword(s):  
High Frequency ◽  
The Other ◽  
Linear Filtering ◽  
Multi Scale ◽  
Difference Of Gaussians ◽  
Frequency Components ◽  
Lightness Induction ◽  
Simulation Results ◽  
Model Support ◽  
Mach Band

The perceived lightness of a stimulus depends on its background, a phenomenon known as lightness induction. For instance, the same gray stimulus can look light in one background and dark in another. Moreover, such induction can take place in two directions; in one case, it occurs in the direction of the background lightness known as lightness assimilation, while in the other it occurs opposite to that, known as lightness contrast. The White’s illusion is a typical one which does not completely conform to any of these two processes. In this paper, we have quantified the perceptual strength of the White’s illusion as a function of the width of the background square grating. Based on our results which also corroborate some earlier studies, we propose a linear filtering model inspired from an earlier work dealing with varying Mach band widths. Our model assumes that the for the White’s illusion, where the edges are strong and many in number, and as such the spectrum is rich in high frequency components, the inhibitory surround in the classical Difference-of-Gaussians (DoG) filter gets suppressed, so that the filter essentially reduces to a multi-scale Gaussian one. The simulation results with this model support the present as well as earlier experimental results.

scholarly journals A possible linear filtering model to explain White’s illusion at different grating widths

2017 ◽  
Author(s):  
Soma Mitra ◽  
Deabasis Mazumdar ◽  
Kuntal Ghosh ◽  
Kamales Bhaumik
Keyword(s):  
High Frequency ◽  
The Other ◽  
Linear Filtering ◽  
Multi Scale ◽  
Difference Of Gaussians ◽  
Frequency Components ◽  
Lightness Induction ◽  
Simulation Results ◽  
Model Support ◽  
Mach Band

The perceived lightness of a stimulus depends on its background, a phenomenon known as lightness induction. For instance, the same gray stimulus can look light in one background and dark in another. Moreover, such induction can take place in two directions; in one case, it occurs in the direction of the background lightness known as lightness assimilation, while in the other it occurs opposite to that, known as lightness contrast. The White’s illusion is a typical one which does not completely conform to any of these two processes. In this paper, we have quantified the perceptual strength of the White’s illusion as a function of the width of the background square grating. Based on our results which also corroborate some earlier studies, we propose a linear filtering model inspired from an earlier work dealing with varying Mach band widths. Our model assumes that the for the White’s illusion, where the edges are strong and many in number, and as such the spectrum is rich in high frequency components, the inhibitory surround in the classical Difference-of-Gaussians (DoG) filter gets suppressed, so that the filter essentially reduces to a multi-scale Gaussian one. The simulation results with this model support the present as well as earlier experimental results.

scholarly journals A multi-scale Gaussian filtering model to explain the White’s illusion from the viewpoint of lightness assimilation for widely varying grating width and patch length

2018 ◽  
Author(s):  
Soma Mitra ◽  
Debasis Mazumdar ◽  
Kuntal Ghosh ◽  
Kamales Bhaumik
Keyword(s):  
Gaussian Kernel ◽  
Linear Filter ◽  
Filter Model ◽  
Visual Neuroscience ◽  
Multi Scale ◽  
Difference Of Gaussians ◽  
Simultaneous Brightness Contrast ◽  
Frequency Components ◽  
Lightness Induction ◽  
Patch Length

The variation between the actual and perceived lightness of a stimulus has strong dependency on its background, a phenomena commonly known as lightness induction in the literature of visual neuroscience and psychology. For instance, a gray patch may perceptually appear to be darker in a background while it looks brighter when the background is reversed. In the literature it is further reported that such variation can take place in two possible ways. In case of stimulus like the Simultaneous Brightness Contrast (SBC), the apparent lightness changes in the direction opposite to that of the background lightness, a phenomenon often referred to as lightness contrast, while in the others like pincushion or checkerboard illusion it occurs opposite to that, and known as lightness assimilation. The White’s illusion is a typical one which according to many, does not completely conform to any of these two processes. This paper presents the result of quantification of the perceptual strength of the White’s illusion as a function of the width of the background square grating as well as the length of the gray patch. A linear filter model is further proposed to simulate the possible neurophysiological phenomena responsible for this particular visual experience. The model assumes that for the White’s illusion, where the edges are strong and quite a few, i.e. the spectrum is rich in high frequency components, the inhibitory surround in the classical Difference-of-Gaussians (DoG) filter gets suppressed, and the filter essentially reduces to a multi-scale Gaussian kernel that brings about lightness assimilation. The linear filter model with a Gaussian kernel is used to simulate the White’s illusion phenomena with wide variation of spatial frequency of the background grating as well as the length of the gray patch. The appropriateness of the model is presented through simulation results, which are highly tuned to the present as well as earlier psychometric results.

scholarly journals A multi-scale Gaussian filtering model to explain the White’s illusion from the viewpoint of lightness assimilation for widely varying grating width and patch length

2018 ◽  
Author(s):  
Soma Mitra ◽  
Debasis Mazumdar ◽  
Kuntal Ghosh ◽  
Kamales Bhaumik
Keyword(s):  
Gaussian Kernel ◽  
Linear Filter ◽  
Filter Model ◽  
Visual Neuroscience ◽  
Multi Scale ◽  
Difference Of Gaussians ◽  
Simultaneous Brightness Contrast ◽  
Frequency Components ◽  
Lightness Induction ◽  
Patch Length

The variation between the actual and perceived lightness of a stimulus has strong dependency on its background, a phenomena commonly known as lightness induction in the literature of visual neuroscience and psychology. For instance, a gray patch may perceptually appear to be darker in a background while it looks brighter when the background is reversed. In the literature it is further reported that such variation can take place in two possible ways. In case of stimulus like the Simultaneous Brightness Contrast (SBC), the apparent lightness changes in the direction opposite to that of the background lightness, a phenomenon often referred to as lightness contrast, while in the others like pincushion or checkerboard illusion it occurs opposite to that, and known as lightness assimilation. The White’s illusion is a typical one which according to many, does not completely conform to any of these two processes. This paper presents the result of quantification of the perceptual strength of the White’s illusion as a function of the width of the background square grating as well as the length of the gray patch. A linear filter model is further proposed to simulate the possible neurophysiological phenomena responsible for this particular visual experience. The model assumes that for the White’s illusion, where the edges are strong and quite a few, i.e. the spectrum is rich in high frequency components, the inhibitory surround in the classical Difference-of-Gaussians (DoG) filter gets suppressed, and the filter essentially reduces to a multi-scale Gaussian kernel that brings about lightness assimilation. The linear filter model with a Gaussian kernel is used to simulate the White’s illusion phenomena with wide variation of spatial frequency of the background grating as well as the length of the gray patch. The appropriateness of the model is presented through simulation results, which are highly tuned to the present as well as earlier psychometric results.

scholarly journals Seismic section image detail enhancement method based on bilateral texture filtering and adaptive enhancement of texture details

2020 ◽  
Vol 27 (2) ◽  
pp. 253-260
Author(s):  
Xiang-Yu Jia ◽  
Chang-Lei DongYe
Keyword(s):  
High Frequency ◽  
Structural Information ◽  
Low Frequency ◽  
Frequency Noise ◽  
Seismic Section ◽  
Multi Scale ◽  
Adaptive Enhancement ◽  
Enhancement Method ◽  
Frequency Components ◽  
Texture Filtering

Abstract. The seismic section image contains a wealth of texture detail information, which is important for the interpretation of the formation profile information. In order to enhance the texture detail of the image while keeping the structural information of the image intact, a multi-scale enhancement method based on wavelet transform is proposed. Firstly, the image is wavelet decomposed to obtain a low-frequency structural component and a series of high-frequency texture detail components. Secondly, bilateral texture filtering is performed on the low-frequency structural components to filter out high-frequency noise while maintaining the edges of the image; adaptive enhancement is performed on the high-frequency detail components to filter out low-frequency noise while enhancing detail. Finally, the processed high- and low-frequency components reconstructed by wavelets can obtain a seismic section image with enhanced detail. The method of this paper enhances the texture detail information in the image while preserving the edge of the image.

scholarly journals Multi-Focus Image Fusion Method Based on Multi-Scale Decomposition of Information Complementary

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1362
Author(s):  
Hui Wan ◽  
Xianlun Tang ◽  
Zhiqin Zhu ◽  
Weisheng Li
Keyword(s):  
Image Fusion ◽  
High Frequency ◽  
Low Frequency ◽  
Fusion Method ◽  
Complementary Information ◽  
Multi Scale ◽  
Initial Decision ◽  
Focus Image ◽  
Frequency Components ◽  
Image Fusion Method

Multi-focus image fusion is an important method used to combine the focused parts from source multi-focus images into a single full-focus image. Currently, to address the problem of multi-focus image fusion, the key is on how to accurately detect the focus regions, especially when the source images captured by cameras produce anisotropic blur and unregistration. This paper proposes a new multi-focus image fusion method based on the multi-scale decomposition of complementary information. Firstly, this method uses two groups of large-scale and small-scale decomposition schemes that are structurally complementary, to perform two-scale double-layer singular value decomposition of the image separately and obtain low-frequency and high-frequency components. Then, the low-frequency components are fused by a rule that integrates image local energy with edge energy. The high-frequency components are fused by the parameter-adaptive pulse-coupled neural network model (PA-PCNN), and according to the feature information contained in each decomposition layer of the high-frequency components, different detailed features are selected as the external stimulus input of the PA-PCNN. Finally, according to the two-scale decomposition of the source image that is structure complementary, and the fusion of high and low frequency components, two initial decision maps with complementary information are obtained. By refining the initial decision graph, the final fusion decision map is obtained to complete the image fusion. In addition, the proposed method is compared with 10 state-of-the-art approaches to verify its effectiveness. The experimental results show that the proposed method can more accurately distinguish the focused and non-focused areas in the case of image pre-registration and unregistration, and the subjective and objective evaluation indicators are slightly better than those of the existing methods.

Evaluation of PCB Shielding Characteristic in Near Field

2017 ◽  
Vol 5 (3) ◽  
pp. 542
Author(s):  
Yih Jian Chuah ◽  
Mohd Tafir Mustaffa
Keyword(s):  
Electromagnetic Interference ◽  
High Frequency ◽  
Near Field ◽  
Electronic Devices ◽  
The Other ◽  
Shielding Effectiveness ◽  
Electronic Industry ◽  
Common Solution ◽  
The Common ◽  
Simulation Results

Wireless electronic devices nowadays always operate in high frequency while having small and compact form factor which led to electromagnetic interference among traces and components. PCB shielding is the common solution applied in electronic industry to mitigate electromagnetic interference. In this paper, PCB shielding characteristics such as shield’s thickness, height, and ground via spacing in PCB boards were evaluated in near field. Test boards with various ground via spacing were fabricated and evaluated by using 3D Electromagnetic scanner. On the other hand, shields with various thickness and height were modeled and evaluated through simulation. Results suggested that shielding effectiveness could be improved by having greater shield’s height with smaller ground via spacing in shielding ground tracks. Shielding effectiveness can be improved by 1 dB with every step of 0.5 mm increase in shield’s height. Besides that, approximately 0.5 dB improvement in shielding effectiveness with every step of 1 mm decrease in ground via spacing. Furthermore, greater shield’s thickness can contribute better shielding effectiveness for operating frequency below 300 MHz.

scholarly journals An adaptive scale Gaussian filter to explain White’s illusion from the viewpoint of lightness assimilation for a large range of variation in spatial frequency of the grating and aspect ratio of the targets

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5626 ◽  
Author(s):  
Soma Mitra ◽  
Debasis Mazumdar ◽  
Kuntal Ghosh ◽  
Kamales Bhaumik
Keyword(s):  
Spatial Frequency ◽  
Gaussian Kernel ◽  
Linear Filter ◽  
Filter Model ◽  
Visual Neuroscience ◽  
Difference Of Gaussians ◽  
Simultaneous Brightness Contrast ◽  
Frequency Components ◽  
Lightness Induction ◽  
Range Of Variation

The variation between the actual and perceived lightness of a stimulus has strong dependency on its background, a phenomena commonly known as lightness induction in the literature of visual neuroscience and psychology. For instance, a gray patch may perceptually appear to be darker in a background while it looks brighter when the background is reversed. In the literature it is further reported that such variation can take place in two possible ways. In case of stimulus like the Simultaneous Brightness Contrast (SBC), the apparent lightness changes in the direction opposite to that of the background lightness, a phenomenon often referred to as lightness contrast, while in the others like neon colour spreading or checkerboard illusion it occurs opposite to that, and known as lightness assimilation. The White’s illusion is a typical one which according to many, does not completely conform to any of these two processes. This paper presents the result of quantification of the perceptual strength of the White’s illusion as a function of the width of the background square grating as well as the length of the gray patch. A linear filter model is further proposed to simulate the possible neurophysiological phenomena responsible for this particular visual experience. The model assumes that for the White’s illusion, where the edges are strong and quite a few, i.e., the spectrum is rich in high frequency components, the inhibitory surround in the classical Difference-of-Gaussians (DoG) filter gets suppressed, and the filter essentially reduces to an adaptive scale Gaussian kernel that brings about lightness assimilation. The linear filter model with a Gaussian kernel is used to simulate the White’s illusion phenomena with wide variation of spatial frequency of the background grating as well as the length of the gray patch. The appropriateness of the model is presented through simulation results, which are highly tuned to the present as well as earlier psychometric results.

Nonlinearities of Near-Threshold Contrast Transduction

Perception ◽  
1997 ◽  
Vol 26 (1_suppl) ◽  
pp. 208-208
Author(s):  
L L Kontsevich ◽  
C W Tyler
Keyword(s):  
Spatial Frequency ◽  
Psychometric Function ◽  
High Frequency ◽  
Opposite Sign ◽  
The Other ◽  
Threshold Behaviour ◽  
Threshold Elevation ◽  
Difference Of Gaussians ◽  
Local Difference ◽  
Near Threshold

The existence of analytic threshold nonlinearities was probed with a variety of local and extended stimuli. Incremental thresholds were measured by the 2AFC procedure for same-sign and opposite-sign stationary pedestals. In contrast to the dipper effect for same-sign pedestals, subthreshold bumper effects were observed of threshold elevation (up to a factor of 4 in some conditions). The results for local difference of Gaussians (DoG) and 10 cycles deg−1 Gabors were consistent with accurate hard-threshold behaviour. The results for negative DoG (whose increment corresponds to the darkening of the central spot) and 2 cycles deg−1 Gabor profiles revealed a quadratic nonlinearity for one observer and linear transduction for the other. These results repudiate the uncertainty explanation for the steep psychometric function near threshold, and suggest that there are two different hard-wired nonlinearities: one for luminance increments and another for luminance decrements. According to our analysis, in low-spatial-frequency gratings, a contrast change is detected within those bars that become darker; in high-frequency gratings, transient changes are detected in the bars that become brighter.

Calculation Error's Correction of Three-Component Geomagnetic Field's Marine Survey

2013 ◽  
Vol 756-759 ◽  
pp. 323-326
Author(s):  
Xing Le Zhu ◽  
Chang Han Xiao ◽  
Zhen Ning Yao
Keyword(s):  
Wavelet Transform ◽  
High Frequency ◽  
Geomagnetic Field ◽  
Low Frequency ◽  
Frequency Component ◽  
Frequency Noise ◽  
Calculation Error ◽  
High Frequency Noise ◽  
Multi Scale ◽  
Simulation Results

In order to eliminate calculation error, wavelet transform is used to remove noise when navigational data is used to calculate truth-value of three-component geomagnetic field. By introducing Euler attitude rotation matrix, the computing value of geomagnetic vector is decomposed by multi-scale wavelet transform in each frequency. The high-frequency noise is removed and the accurate value of geomagnetic field can be got by rebuilding low-frequency component. Simulation results indicate that the calculated value is identical with setting value and has high precision, which means the method has great applied importance and instructional significance for practical measurement of marine three-component geomagnetic field.

IMPROVING FORECASTING ACCURACY OF THE S&P500 INTRA-DAY PRICE DIRECTION USING BOTH WAVELET LOW AND HIGH FREQUENCY COEFFICIENTS

2014 ◽  
Vol 13 (01) ◽  
pp. 1450008 ◽  
Author(s):  
S. LAHMIRI
Keyword(s):  
Time Resolution ◽  
Stock Prices ◽  
High Frequency ◽  
Short Interval ◽  
Low Frequency ◽  
Standard Approach ◽  
Discrete Wavelet ◽  
High Time Resolution ◽  
Frequency Components ◽  
Simulation Results

Numerous research works have successfully applied the wavelet analysis for the decomposition and forecasting of financial data. Particularly, using the discrete wavelet transform (DWT) stock price time series were analyzed following a fixed sub-band coding scheme, which provides a low time resolution for low frequencies and a high time resolution for high frequencies. Following the standard approach found in the literature, only low frequency components were considered as main features to predict stock prices. However, this approach lacks of details about the generative process of the original data. In this paper, we rely on DWT high frequency sub-band to extract short interval hidden information for better classification of future Standard and Poors (S&P500) one minute ahead direction using artificial neural network trained with backpropagation algorithm. The simulation results show that our approach that uses both low (approximation) and high (detail) frequency coefficients provides better classification rates than the standard one. In addition, simulation results show that low frequency components are appropriate to detect future downshifts in S&P500, whilst our approach is suitable to predict future upwards. Thus, the standard approach provides valuable information for risk averse investors trading S&P500, and our approach that combines low and high frequency coefficients is strongly useful for aggressive investors seeking short-term profits when trading S&P500.

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