Manipulation of glossiness perception by contrast enhancement of high spatial frequency components

Neurons in the mammalian visual cortex have been found to respond to second-order features which are not defined by changes in luminance over the retina (Albright, 1992; Zhou & Baker, 1993, 1994, 1996; Mareschal & Baker, 1998a,b). The detection of these stimuli is most often accounted for by a separate nonlinear processing stream, acting in parallel to the linear stream in the visual system. Here we examine the two-dimensional spatial properties of these nonlinear neurons in area 18 using envelope stimuli, which consist of a high spatial-frequency carrier whose contrast is modulated by a low spatial-frequency envelope. These stimuli would fail to elicit a response in a conventional linear neuron because they are designed to contain no spatial-frequency components overlapping the neuron's luminance defined passband. We measured neurons' responses to these stimuli as a function of both the relative spatial frequencies and relative orientations of the carrier and envelope. Neurons' responses to envelope stimuli were narrowband to the carrier spatial frequency, with optimal values ranging from 8- to 30-fold higher than the envelope spatial frequencies. Neurons' responses to the envelope stimuli were strongly dependent on the orientation of the envelope and less so on the orientation of the carrier. Although the selectivity to the carrier orientation was broader, neurons' responses were clearly tuned, suggesting that the source of nonlinear input is cortical. There was no fixed relationship between the optimal carrier and envelope spatial frequencies or orientations, such that nonlinear neurons responding to these stimuli could perhaps respond to a variety of stimuli defined by changes in scale or orientation.

Download Full-text

Effects of Spatial Frequency Filtering Choices on the Perception of Filtered Images

Vision ◽

10.3390/vision4020029 ◽

2020 ◽

Vol 4 (2) ◽

pp. 29

Author(s):

Sabrina Perfetto ◽

John Wilder ◽

Dirk B. Walther

Keyword(s):

Spatial Frequency ◽

Behavioral Outcomes ◽

High Spatial Frequency ◽

Frequency Content ◽

Frequency Filtering ◽

Image Content ◽

Specific Choice ◽

Spatial Frequencies ◽

Spatial Frequency Filtering ◽

Frequency Components

The early visual system is composed of spatial frequency-tuned channels that break an image into its individual frequency components. Therefore, researchers commonly filter images for spatial frequencies to arrive at conclusions about the differential importance of high versus and low spatial frequency image content. Here, we show how simple decisions about the filtering of the images, and how they are displayed on the screen, can result in drastically different behavioral outcomes. We show that jointly normalizing the contrast of the stimuli is critical in order to draw accurate conclusions about the influence of the different spatial frequencies, as images of the real world naturally have higher contrast energy at low than high spatial frequencies. Furthermore, the specific choice of filter shape can result in contradictory results about whether high or low spatial frequencies are more useful for understanding image content. Finally, we show that the manner in which the high spatial frequency content is displayed on the screen influences how recognizable an image is. Previous findings that make claims about the visual system’s use of certain spatial frequency bands should be revisited, especially if their methods sections do not make clear what filtering choices were made.

Download Full-text

The importance of static phase-aligned, high spatial frequency components for continuous flash suppression

Journal of Vision ◽

10.1167/8.6.241 ◽

2010 ◽

Vol 8 (6) ◽

pp. 241-241 ◽

Cited By ~ 2

Author(s):

G. Maehara ◽

P.-C. Huang ◽

R. Hess

Keyword(s):

Spatial Frequency ◽

High Spatial Frequency ◽

Continuous Flash Suppression ◽

Static Phase ◽

Frequency Components

Download Full-text

Recognition of Positive and Negative Bandpass-Filtered Images

Perception ◽

10.1068/p150595 ◽

1986 ◽

Vol 15 (5) ◽

pp. 595-602 ◽

Cited By ~ 117

Author(s):

Tony Hayes ◽

M Concetta Morrone ◽

David C Burr

Keyword(s):

Spatial Frequency ◽

Visual System ◽

Visual Information ◽

Recognition Performance ◽

Low Frequency ◽

High Spatial Frequency ◽

Spatial Frequencies ◽

Negative Images ◽

Frequency Components

A study is reported in which the significance for vision of low- and high-spatial-frequency components of photographic positive and negative images was investigated by measuring recognition of bandpass-filtered photographs of faces. The results show that a 1.5 octave bandpass-filtered image contains sufficient visual information for good recognition performance, provided the filter is centred close to 20 cycles facewidth−1. At low spatial frequencies negatives are more difficult to recognize than positives, but at high spatial frequencies there is no difference in recognition, implying that it is the low-frequency components of negatives which present difficulties for the visual system.

Download Full-text

Spatial Frequency Effective for Increasing Perceived Glossiness by Contrast Enhancement

Frontiers in Psychology ◽

10.3389/fpsyg.2021.625135 ◽

2021 ◽

Vol 12 ◽

Author(s):

Hiroaki Kiyokawa ◽

Tomonori Tashiro ◽

Yasuki Yamauchi ◽

Takehiro Nagai

Keyword(s):

Surface Roughness ◽

Spatial Frequency ◽

Contrast Enhancement ◽

High Spatial Frequency ◽

Dark Condition ◽

Statistical Correlations ◽

Related Information ◽

Spatial Frequencies ◽

Band Contrast ◽

The Impact

It has been suggested that luminance edges in retinal images are potential cues for glossiness perception, particularly when the perception relies on low-luminance specular regions. However, a previous study has shown only statistical correlations between luminance edges and perceived glossiness, not their causal relations. Additionally, although specular components should be embedded at various spatial frequencies depending on the micro-roughness on the object surface, it is not well understood what spatial frequencies are essential for glossiness perception on objects with different micro-roughness. To address these issues, we examined the impact of a sub-band contrast enhancement on the perceived glossiness in the two conditions of stimuli: the Full condition where the stimulus had natural specular components and the Dark condition where it had specular components only in dark regions. Object images with various degrees of surface roughness were generated as stimuli, and their contrast was increased in various spatial-frequency sub-bands. The results indicate that the enhancement of the sub-band contrast can significantly increase perceived glossiness as expected. Furthermore, the effectiveness of each spatial frequency band depends on the surface roughness in the Full condition. However, effective spatial frequencies are constant at a middle spatial frequency regardless of the stimulus surface roughness in the Dark condition. These results suggest that, for glossiness perception, our visual system depends on specular-related information embedded in high spatial frequency components but may change the dependency on spatial frequency based on the surface luminance to be judged.

Download Full-text

Multisensensor Multitemporal Data Fusion Using Wavelet Transform

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-1-121-2014 ◽

2014 ◽

Vol XL-1 ◽

pp. 121-128

Author(s):

S. Ghannam ◽

M. Awadallah ◽

A. L. Abbott ◽

R. H. Wynne

Keyword(s):

Data Fusion ◽

Spatial Frequency ◽

High Spatial Frequency ◽

Weighted Average ◽

Linear Interpolation ◽

Fusion Model ◽

Landsat Images ◽

Sensing Applications ◽

Frequency Components ◽

Recovery Assessment

Interest in data fusion, for remote-sensing applications, continues to grow due to the increasing importance of obtaining data in high resolution both spatially and temporally. Applications that will benefit from data fusion include ecosystem disturbance and recovery assessment, ecological forecasting, and others. This paper introduces a novel spatiotemporal fusion approach, the wavelet-based Spatiotemporal Adaptive Data Fusion Model (WSAD-FM). This new technique is motivated by the popular STARFM tool, which utilizes lower-resolution MODIS imagery to supplement Landsat scenes using a linear model. The novelty of WSAD-FM is twofold. First, unlike STARFM, this technique does not predict an entire new image in one linear step, but instead decomposes input images into separate "approximation" and "detail" parts. The different portions are fed into a prediction model that limits the effects of linear interpolation among images. Low-spatial-frequency components are predicted by a weighted mixture of MODIS images and low-spatial-frequency components of Landsat images that are neighbors in the temporal domain. Meanwhile, high-spatialfrequency components are predicted by a weighted average of high-spatial-frequency components of Landsat images alone. The second novelty is that the method has demonstrated good performance using only one input Landsat image and a pair of MODIS images. The technique has been tested using several Landsat and MODIS images for a study area from Central North Carolina (WRS-2 path/row 16/35 in Landsat and H/V11/5 in MODIS), acquired in 2001. NDVI images that were calculated from the study area were used as input to the algorithm. The technique was tested experimentally by predicting existing Landsat images, and we obtained <i>R</i><sup>2</sup> values in the range 0.70 to 0.92 for estimated Landsat images in the red band, and 0.62 to 0.89 for estimated NDVI images.

Download Full-text