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 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.

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.

Evaluation and Analysis of Cardiovascular Function in Intensive Care Unit Patients by Ultrasound Image Segmentation Based on Deep Learning

2020 ◽  
Vol 10 (8) ◽  
pp. 1892-1898
Author(s):  
Jiaqi Shen ◽  
Fangfang Huang ◽  
Myers Ulrich
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Carotid Artery ◽  
Blood Vessel ◽  
Nerve Cell ◽  
Clinical Application ◽  
Learning Algorithm ◽  
Linear Filter ◽  
Multi Scale ◽  
Deep Learning Algorithm

Many studies have shown that cardiovascular disease has become one of the major diseases leading to death in the world. Therefore, it is a very meaningful topic to use image segmentation technology to segment blood vessels for clinical application. In order to automatically extract the features of blood vessel images in the process of segmentation, the deep learning algorithm is combined with image segmentation technology to segment the nerve cell membrane and carotid artery images of ICU patients, and to segment the blood vessel images from a multi-dimensional perspective. The relevant data are collected to observe the effect of this model. The results show that the three-dimensional multi-scale linear filter has a good effect on carotid artery segmentation in the image segmentation of nerve cell membranes and carotid artery. When analyzing the accuracy of vascular image segmentation from network parameters and training parameters, it is found that the accuracy of the threedimensional multi-scale linear filter can reach about 85%. Therefore, it can be found that the combination of deep learning algorithm and image segmentation technology has a good segmentation effect, and the segmentation accuracy is also high. The experiment achieves the desired effect, which provides experimental basis for the clinical application of the vascular image segmentation technology.

scholarly journals Towards reconstruction of the flow duration curve: development of a conceptual framework with a physical basis

2011 ◽  
Vol 8 (2) ◽  
pp. 3961-3992 ◽  
Author(s):  
Y. Yokoo ◽  
M. Sivapalan
Keyword(s):  
Conceptual Framework ◽  
Sensitivity Analyses ◽  
Linear Filter ◽  
Flow Duration Curve ◽  
Rainfall Runoff ◽  
Filter Model ◽  
Flow Component ◽  
Flow Duration ◽  
Rainfall Runoff Model ◽  
Runoff Model

Abstract. In this paper we investigate the climatic and landscape controls on the flow duration curve (FDC) with the use of a physically-based rainfall-runoff model. The FDC is a stochastic representation of within-year variability of runoff, which arises from the transformation, by the catchment, of within-year variability of precipitation that can itself be characterized by a corresponding duration curve for precipitation (PFDC). Numerical simulations are carried out with the rainfall-runoff model under a variety of combinations of climatic inputs (i.e., precipitation, potential evaporation, including their within-year variability) and landscape properties (i.e., soil type and depth). The simulations indicated that the FDC can be disaggregated into two components, with sharply differing characteristics and origins: the FDC for surface (fast) runoff (SFDC) and the FDC for subsurface (slow) runoff (SSFDC). SFDC closely tracked PFDC and can be approximated with the use of a simple, nonlinear (threshold) filter model. On the other hand, SSFDC tracked the FDC that is constructed from the regime curve (ensemble mean within-year variation of streamflow), which can be closely approximated by a linear filter model. Sensitivity analyses were carried out to understand the climate and landscape controls on each component, gaining useful physical insights into their respective shapes. In particular the results suggested that evaporation from dynamic saturated areas, especially in the dry season, can contribute to a sharp dip at the lower tail of the FDCs. Based on these results, we develop a conceptual framework for the reconstruction of FDCs in ungauged basins. This framework partitions the FDC into: (1) a fast flow component, governed by a filtered version of PFDC, (2) a slow flow component governed by the regime curve, and (3) a correction to SSFDC to capture the effects of high evapotranspiration at low flows.

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.

scholarly journals Towards reconstruction of the flow duration curve: development of a conceptual framework with a physical basis

2011 ◽  
Vol 15 (9) ◽  
pp. 2805-2819 ◽  
Author(s):  
Y. Yokoo ◽  
M. Sivapalan
Keyword(s):  
Conceptual Framework ◽  
Sensitivity Analyses ◽  
Linear Filter ◽  
Flow Duration Curve ◽  
Rainfall Runoff ◽  
Filter Model ◽  
Flow Component ◽  
Flow Duration ◽  
Rainfall Runoff Model ◽  
Runoff Model

Abstract. In this paper we investigate the climatic and landscape controls on the flow duration curve (FDC) with the use of a physically-based rainfall-runoff model. The FDC is a stochastic representation of the variability of runoff, which arises from the transformation, by the catchment, of within-year variability of precipitation that can itself be characterized by a corresponding duration curve for precipitation (PDC). Numerical simulations are carried out with the rainfall-runoff model under a variety of combinations of climatic inputs (i.e. precipitation, potential evaporation, including their within-year variability) and landscape properties (i.e. soil type and depth). The simulations indicated that the FDC can be disaggregated into two components, with sharply differing characteristics and origins: the FDC for surface (fast) runoff (SFDC) and the FDC for subsurface (slow) runoff (SSFDC), which included base flow in our analysis. SFDC closely tracked PDC and can be approximated with the use of a simple, nonlinear (threshold) filter model. On the other hand, SSFDC tracked the FDC that is constructed from the regime curve (i.e. mean monthly runoff), which can be closely approximated by a linear filter model. Sensitivity analyses were carried out to understand the climate and landscape controls on each component, gaining useful physical insights into their respective shapes. In particular the results suggested that evaporation from dynamic saturated areas, especially in the dry season, can contribute to a sharp dip at the lower tail of the FDCs. Based on these results, we develop a conceptual framework for the reconstruction of FDCs in ungauged basins. This framework partitions the FDC into: (1) a fast flow component, governed by a filtered version of PDC, (2) a slow flow component governed by the regime curve, and (3) a correction to SSFDC to capture the effects of high evapotranspiration (ET) at low flows.

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