Comparison of three image-analysis-based visual texture calculation methods: energy, entropy, and texture change index.

BACKGROUND: Three image analysis methods to measure visual texture were applied to an image with much texture (scaled carp), and one with little texture (mirror carp). For each method, the effect of image rotation at 0°, 10°, 20°, 30°, 45°, 60°, 75° and 90° was evaluated. RESULTS: Rotation did not change energy (E) and entropy (H) calculations using image histograms. Using co-occurrence matrices with different step size d (1-19 in increases of 2) and step angle θ (0°, 45°, 90° and 135°) showed that, as d increased, E decreased and H increased, and the number of legitimate pixel pairs decreased linearly. Averaging E and H at different θ values rendered the results rotation invariant. Theoretically, the 'texture primitives' method is not rotation independent. However, the variation in texture change index (TCI) with image rotation was negligible. Also, the increase in TCI between the less textured image and the more textured image was 5.3-11. In comparison, the E values from histograms for the images above were 0.0069-0.0081. For co-occurrence matrix-based calculations, at d = 1 and for all θ, E range was from 220 to 389 for scaled carp and from 232 to 412 for mirror carp. CONCLUSION: The more than doubling of TCI for these images implies that it is a more precise method than energy and entropy to discern between visual texture levels. © 2022 Society of Chemical Industry.

Visual texture agnosia caused by bilateral posterior cerebral artery stroke: a case study.

A 57-year-old man presented with a bilateral posterior cerebral artery attack and was visually impaired. He had a hard time identifying familiar faces and shades. He also felt that the familiar building looked different, and complained that it was not possible to tell from visual information alone whether the food was cooked or the kimono fabric was soft.We assessed the patient's visual function using real materials and material images and was diagnosed with visual texture agnosia. There are few reports of visual texture agnosia, detailed evaluation is considered important because perceiving texture is important for activities of daily living.

On Visual Texture Preference: Can an Ecological Model Explain Why People Like Some Textures More Than Others?

What visual textures do people like and why? Here, we test whether the ecological valence theory proposed for color preferences can also predict people's preferences for visual texture. According to the theory, people should like visual textures associated with positive objects or entities and dislike visual textures associated with negative objects or entities. We compare the results for the ecological model with a more traditional texture-preference model based on computational features and find that the ecological model performs reasonably well considering its lower complexity, explaining 63% of the variance in the human preference data.

Quantification of Visual Texture and Presentation of Intermediate Visual Texture by Spatial Mixing.

We proposed a method to display an intermediate visual texture by spatial mixing. In addition to color information, the visual texture is an important element that characterizes the nature of an object's surface. While the system to display various color information has well matured in engineering, there is no method to reproduce visual textures in ambient light. In our method, the matte and glossy surfaces are used as "primary visual textures", and an intermediate visual texture is displayed by spatially mixing the primary visual textures. In this paper, we first quantified the visual texture of an object's surface based on measured intensities of scattered and reflected lights. Next, based on the quantification, we evaluated spatially mixed surfaces consisting of two primary visual textures, an acrylic plate and a holed sheet of drawing paper, by changing the area proportion of the two primary visual textures. Finally, a sensory evaluation showed significant differences between each intermediate visual texture, and the results corresponded to a trend in the optical evaluation. This study illustrates that visual textures could be quantified based on the intensity of scattered and reflected light and reveals the applicability of our method to the display for intermediate visual texture.

Tactile disgust: Post-contact can be more disgusting than contact.

Several studies have examined if disgust can be evoked by contacting an object-yet none have examined if reported disgust changes when the hand leaves the object. This is surprising given that post-contact tactile disgust is probably a driver of hand hygiene. We examined contact and post-contact tactile disgust and its sensory origins. Participants were asked to touch several objects, making sensory, disgust, and desire-to-handwash evaluations. These ratings were made at three stages-of-contact: object-contact (just touch), post-contact (just touch), and visual post-contact (touch, vision). Disgust was typically highest at post-contact (when the hand left the object). Stickiness and wetness were uniquely predictive of object-contact disgust. Only stickiness drove post-contact disgust, and only wetness visual post-contact disgust. Hand-washing desire was primarily driven by quantity of residue perceived on the hand. These findings suggest that tactile disgust is a multisensory and iterative process relating to object- and residue-adhesiveness.

Modeling visual aesthetic perception: bridges between computed texture features and perceived beauty qualities in semantic experiments.

The exploration of the potential relationship between computable low-level texture, such as features extracted from color and texture, and the perceived high-level aesthetic properties, such as warm or cold, soft or hard, has been a hot research topic of neuroaesthetics. First, the selection and clustering of aesthetic antonyms used to represent the aesthetic properties of visual texture are completed through two semantic differential experiments. Subsequently, 151 visual textures are rated according to the selected aesthetic antonyms by participants in a third semantic differential experiment. Third, 106 textural features are extracted using four different image analysis algorithms to describe the low-level characteristics of visual textures. Finally, the construction and evaluation of the visual aesthetic perception model based on multiple linear and nonlinear regression algorithms are discussed. We analyzed the frequency of each aesthetic antonym selected from 20 pairs of semantic antonyms, and the most frequently mentioned 8 pairs of semantic antonyms were selected as the core set for model building. The extracted low-level features are highly correlative. Of the correlation coefficients based on absolute values, 3383 are less than 0.75, accounting for 14.84% of the total. The correlation coefficients were larger than 0.5 accounts for 27.29% of the total. Through neighborhood component analysis, the top 10 low-level features are selected with lower correlation. The gap between low-level calculated features and high-level perceived aesthetic emotions can be bridged by a brain-inspired model of visual aesthetic perception.

Selective representations of texture and motion in mouse higher visual areas.

The mouse visual cortex contains interconnected higher visual areas, but their functional specializations are unclear. Here, we used a data-driven approach to examine the representations of complex visual stimuli by L2/3 neurons across mouse higher visual areas, measured using large-field-of-view two-photon calcium imaging. Using specialized stimuli, we found higher fidelity representations of texture in area LM, compared to area AL. Complementarily, we found higher fidelity representations of motion in area AL, compared to area LM. We also observed this segregation of information in response to naturalistic videos. Finally, we explored how receptive field models of visual cortical neurons could produce the segregated representations of texture and motion we observed. These selective representations could aid in behaviors such as visually guided navigation.

Photorealistic Reconstruction of Visual Texture From EEG Signals.

Recent advances in brain decoding have made it possible to classify image categories based on neural activity. Increasing numbers of studies have further attempted to reconstruct the image itself. However, because images of objects and scenes inherently involve spatial layout information, the reconstruction usually requires retinotopically organized neural data with high spatial resolution, such as fMRI signals. In contrast, spatial layout does not matter in the perception of "texture," which is known to be represented as spatially global image statistics in the visual cortex. This property of "texture" enables us to reconstruct the perceived image from EEG signals, which have a low spatial resolution. Here, we propose an MVAE-based approach for reconstructing texture images from visual evoked potentials measured from observers viewing natural textures such as the textures of various surfaces and object ensembles. This approach allowed us to reconstruct images that perceptually resemble the original textures with a photographic appearance. The present approach can be used as a method for decoding the highly detailed "impression" of sensory stimuli from brain activity.

Vegetable Freshness Perception in Dementia with Lewy Bodies and Alzheimer's Disease.

INTRODUCTION: Although various visual function deficits have been reported in patients with Alzheimer's disease (AD) and dementia with Lewy bodies (DLB), vegetable freshness perception has not been thoroughly examined. OBJECTIVE: To investigate vegetable freshness perception in patients with AD and DLB and to clarify the relationship between vegetable freshness perception and various visuoperceptual functions. METHODS: We enrolled 37 patients with probable DLB, 58 patients with probable AD, and 32 age-matched healthy controls. We assessed vegetable freshness perception and visuoperceptual functions, including vegetable brightness perception, contrast sensitivity, color perception, and stereopsis. Patients with DLB showed disproportionate deficits in vegetable freshness perception and vegetable luminance perception compared to patients with AD and controls. Analyses of the groups with higher and lower vegetable freshness perceptions revealed significant differences in contrast sensitivity and visual texture recognition. RESULTS: In the vegetable freshness test, we found significant differences among the 3 groups (F = 30.029, p < 0.0001); the extent of impairment in patients with DLB was greater than that in patients with AD. In patients with DLB, the vegetable freshness judgments were significantly correlated with texture judgment scores and contrast sensitivity. CONCLUSION: Our findings revealed significantly impaired vegetable freshness perception in patients with DLB. Vegetable freshness perception may be related to visual texture recognition in patients with DLB.

Visual texture agnosia influences object identification in dementia with Lewy bodies and Alzheimer's disease.

BACKGROUND: Numerous studies have shown visuoperceptual/visuospatial deficits in dementia with Lewy bodies (DLB) and Alzheimer's disease (AD). Visual texture recognition is also impaired in patients with DLB and AD. Although patients with DLB often exhibit visual misidentifications of objects, there are few studies on the relationships between visual texture recognition and viewpoints for object recognition. OBJECTIVES: The aim of this study was to clarify how viewpoints, textures, and visual cognitive functions affect object recognition and result in visual misidentifications in patients with DLB or AD. METHODS: A total of 37 patients with probable DLB and 58 with probable AD and 32 age-matched healthy controls underwent neuropsychological and visuoperceptual assessments, and performed object identification tasks under four conditions (non-canonical view + blurry texture, non-canonical view + clear texture, canonical view + blurry texture, canonical view + clear texture). The relationship between object identification and other visuoperceptual functions was analyzed. RESULTS: Patients with DLB and AD exhibited significantly impaired object recognition under non-canonical viewing with blurry texture conditions, with the DLB patients exhibiting a significantly worse performance than the AD patients. Patients with DLB and AD exhibited visual misidentifications during object identification tasks under non-canonical viewing. In patients with DLB, the number of visual misidentifications was significantly correlated with the scores of visual texture recognition. CONCLUSIONS: The present study showed that significantly impaired object recognition in patients with DLB under the influences by both viewpoint and visual texture and in those with AD under the influence by viewpoint. Visual misidentification in object recognition could be associated with impaired visual texture recognition in DLB.

Discrimination of spatial phase: The roles of luminance distribution and attention.

We can easily discriminate certain phase relations in spatial patterns but not others. Phase perception has been found different in the fovea vs. periphery, and for single patterns vs. textures. Different numbers of mechanisms have been proposed to account for the regularities of phase perception. In this study, I attempt to better understand the mechanisms behind discrimination of spatial phase. In order to reveal the role of luminance cues, I use histogram matching of patterns with different phases. Possible effects of attention were studied using visual search experiments with varied stimulus set size. Simple and compound Gabor patches, broadband lines and edges, and textures composed of those patterns were used as stimuli. The experiments indicate that phase discrimination is mediated by two mechanisms. The first uses luminance differences and operates pre-attentively, in parallel across the visual field. The second compares relative positions of dark and bright segments within an image, and is strictly limited by capacity of attention.

Visual texture agnosia in dementia with Lewy bodies and Alzheimer's disease.

BACKGROUND: Neuroimaging and some clinical studies have reported that the ventral visual pathway is relevant for visual texture recognition. Although a variety of visual deficits have been reported in patients with Alzheimer's disease (AD) and dementia with Lewy bodies (DLB), visual material identification and texture recognition have not been thoroughly examined. OBJECTIVES: To investigate visual texture recognition in patients with AD and DLB and to clarify the relationship between visual texture recognition and various visuoperceptual functions. METHODS: Twenty-five patients with probable DLB, 53 patients with probable AD, and 32 age-matched healthy controls were included. We assessed visual texture recognition of real materials/images and visuoperceptual functions including contrast sensitivity, color perception, stereopsis, shape detection, and position in space. RESULTS: DLB patients showed disproportionate deficits in visuoperceptual functions and visual texture recognition compared with AD patients and controls, but these dysfunctions were not correlated with each other. AD patients had significantly impaired visual texture recognition but with intact visuoperceptual functions, except contrast sensitivity. Using an optimal cut-off score according to the receiver operating characteristic (ROC) curve analysis, the results from the visual texture recognition of images could differentiate DLB patients from controls with a sensitivity of 92% and a specificity of 97%. CONCLUSIONS: We demonstrated significantly impaired visual texture recognition in patients with DLB and AD, with patients with DLB performing significantly worse than patients with AD. Additionally, visual texture recognition and visuoperceptual functions are independently disturbed in DLB.

Visual perception of order-disorder transition.

Our experience with the natural world, as composed of ordered entities, implies that perception captures relationships between image parts. For instance, regularities in the visual scene are rapidly identified by our visual system. Defining the regularities that govern perception is a basic, unresolved issue in neuroscience. Mathematically, perfect regularities are represented by symmetry (perfect order). The transition from ordered configurations to completely random ones has been extensively studied in statistical physics, where the amount of order is characterized by a symmetry-specific order parameter. Here we applied tools from statistical physics to study order detection in humans. Different sets of visual textures, parameterized by the thermodynamic temperature in the Boltzmann distribution, were designed. We investigated how much order is required in a visual texture for it to be discriminated from random noise. The performance of human observers was compared to Ideal and Order observers (based on the order parameter). The results indicated a high consistency in performance across human observers, much below that of the Ideal observer, but well-approximated by the Order observer. Overall, we provide a novel quantitative paradigm to address order perception. Our findings, based on this paradigm, suggest that the statistical physics formalism of order captures regularities to which the human visual system is sensitive. An additional analysis revealed that some order perception properties are captured by traditional texture discrimination models according to which discrimination is based on integrated energy within maps of oriented linear filters.

Aesthetic perception of visual textures: a holistic exploration using texture analysis, psychological experiment, and perception modeling.

Modeling human aesthetic perception of visual textures is important and valuable in numerous industrial domains, such as product design, architectural design, and decoration. Based on results from a semantic differential rating experiment, we modeled the relationship between low-level basic texture features and aesthetic properties involved in human aesthetic texture perception. First, we compute basic texture features from textural images using four classical methods. These features are neutral, objective, and independent of the socio-cultural context of the visual textures. Then, we conduct a semantic differential rating experiment to collect from evaluators their aesthetic perceptions of selected textural stimuli. In semantic differential rating experiment, eights pairs of aesthetic properties are chosen, which are strongly related to the socio-cultural context of the selected textures and to human emotions. They are easily understood and connected to everyday life. We propose a hierarchical feed-forward layer model of aesthetic texture perception and assign 8 pairs of aesthetic properties to different layers. Finally, we describe the generation of multiple linear and non-linear regression models for aesthetic prediction by taking dimensionality-reduced texture features and aesthetic properties of visual textures as dependent and independent variables, respectively. Our experimental results indicate that the relationships between each layer and its neighbors in the hierarchical feed-forward layer model of aesthetic texture perception can be fitted well by linear functions, and the models thus generated can successfully bridge the gap between computational texture features and aesthetic texture properties.