Visual Texture Research Articles

Image Representation Using Stacked Colour Histogram

Image representation plays a vital role in the realisation of Content-Based Image Retrieval (CBIR) system. The representation is performed because pixel-by-pixel matching for image retrieval is impracticable as a result of the rigid nature of such an approach. In CBIR therefore, colour, shape and texture and other visual features are used to represent images for effective retrieval task. Among these visual features, the colour and texture are pretty remarkable in defining the content of the image. However, combining these features does not necessarily guarantee better retrieval accuracy due to image transformations such rotation, scaling, and translation that an image would have gone through. More so, concerns about feature vector representation taking ample memory space affect the running time of the retrieval task. To address these problems, we propose a new colour scheme called Stack Colour Histogram (SCH) which inherently extracts colour and neighbourhood information into a descriptor for indexing images. SCH performs recurrent mean filtering of the image to be indexed. The recurrent blurring in this proposed method works by repeatedly filtering (transforming) the image. The output of a transformation serves as the input for the next transformation, and in each case a histogram is generated. The histograms are summed up bin-by-bin and the resulted vector used to index the image. The image blurring process uses pixel’s neighbourhood information, making the proposed SCH exhibit the inherent textural information of the image that has been indexed. The SCH was extensively tested on the Coil100, Outext, Batik and Corel10K datasets. The Coil100, Outext, and Batik datasets are generally used to assess image texture descriptors, while Corel10K is used for heterogeneous descriptors. The experimental results show that our proposed descriptor significantly improves retrieval and classification rate when compared with (CMTH, MTH, TCM, CTM and NRFUCTM) which are the start-of-the-art descriptors for images with textural features.

Human Texture Vision as Multi-Order Spectral Analysis.

Texture information plays a critical role in the rapid perception of scenes, objects, and materials. Here, we propose a novel model in which visual texture perception is essentially determined by the 1st-order (2D-luminance) and 2nd-order (4D-energy) spectra. This model is an extension of the dimensionality of the Filter-Rectify-Filter (FRF) model, and it also corresponds to the frequency representation of the Portilla-Simoncelli (PS) statistics. We show that preserving two spectra and randomizing phases of a natural texture image result in a perceptually similar texture, strongly supporting the model. Based on only two single spectral spaces, this model provides a simpler framework to describe and predict texture representations in the primate visual system. The idea of multi-order spectral analysis is consistent with the hierarchical processing principle of the visual cortex, which is approximated by a multi-layer convolutional network.

Redundancy between spectral and higher-order texture statistics for natural image segmentation

Visual texture, defined by local image statistics, provides important information to the human visual system for perceptual segmentation. Second-order or spectral statistics (equivalent to the Fourier power spectrum) are a well-studied segmentation cue. However, the role of higher-order statistics (HOS) in segmentation remains unclear, particularly for natural images. Recent experiments indicate that, in peripheral vision, the HOS of the widely adopted Portilla-Simoncelli texture model are a weak segmentation cue compared to spectral statistics, despite the fact that both are necessary to explain other perceptual phenomena and to support high-quality texture synthesis. Here we test whether this discrepancy reflects a property of natural image statistics. First, we observe that differences in spectral statistics across segments of natural images are redundant with differences in HOS. Second, using linear and nonlinear classifiers, we show that each set of statistics individually affords high performance in natural scenes and texture segmentation tasks, but combining spectral statistics and HOS produces relatively small improvements. Third, we find that HOS improve segmentation for a subset of images, although these images are difficult to identify. We also find that different subsets of HOS improve segmentation to a different extent, in agreement with previous physiological and perceptual work. These results show that the HOS add modestly to spectral statistics for natural image segmentation. We speculate that tuning to natural image statistics under resource constraints could explain the weak contribution of HOS to perceptual segmentation in human peripheral vision.

Shadows of Forgotten Ancestors: In search of national representation in the image of folklore

In this case study analysis of the film Shadows of Forgotten Ancestors (1965) directed by Sergei Parajanov (1924–90) I will explore the ways in which traditional dress in this film, as part of a wider imagery of folklore, has been defamiliarized from the ideological canon of social realism. More specifically, I will look at the ways Parajanov’s film, filled with music, dance, colour and ethnographic texture, significantly departed from the traditional representation of non-Russians on the Soviet screen under the Friendship of Peoples policy. Based on a folkloric legend, adapted and published in 1911 by Mykhailo Kotsiubynskyi (1864–1913), Shadows celebrates the ethnographic texture of the Hutsul region by departing significantly from causality and narrative logic and bringing together primitive and modern elements instead. Praised for its authenticity, the film became a turning point in the search for a new site of national expression for Ukrainian filmmakers and more specifically, the role of folklore in its visual presentation. The work of the costume designer Lidiya Bajkova (1905–80) is emblematic in the way it renders authenticity beyond historical, ethnic and material accuracy by seamlessly integrating the costumes into the visual texture of the cinematic image. Her approach demonstrates how motifs and patterns that have previously been delegated to domesticated and melodramatic narratives could conversely become a fundamental substance of the cinematic experience.

The roles of lower- and higher-order surface statistics in tactile texture perception.

Humans can haptically discriminate surface textures when there is a significant difference in the statistics of the surface profile. Previous studies on tactile texture discrimination have emphasized the perceptual effects of lower-order statistical features such as carving depth, inter-ridge distance, and anisotropy, which can be characterized by local amplitude spectra or spatial-frequency/orientation subband histograms. However, the real-world surfaces we encounter in everyday life also differ in the higher-order statistics, such as statistics about correlations of nearby spatial-frequencies/orientations. For another modality, vision, the human brain has the ability to use the textural differences in both higher- and lower-order image statistics. In this work, we examined whether the haptic texture perception can use higher-order surface statistics as visual texture perception does, by three-dimensional (3-D)-printing textured surfaces transcribed from different "photos" of natural scenes such as stones and leaves. Even though the maximum carving depth was well above the haptic detection threshold, some texture pairs were hard to discriminate. Specifically, those texture pairs with similar amplitude spectra were difficult to discriminate, which suggests that the lower-order statistics have the dominant effect on tactile texture discrimination. To directly test the poor sensitivity of the tactile texture perception to higher-order surface statistics, we matched the lower-order statistics across different textures using a texture synthesis algorithm and found that haptic discrimination of the matched textures was nearly impossible unless the stimuli contained salient local features. We found no evidence for the ability of the human tactile system to use higher-order surface statistics for texture discrimination.NEW & NOTEWORTHY Humans can discriminate subtle spatial patterns differences in the surrounding world through their hands, but the underlying computation remains poorly understood. Here, we 3-D-printed textured surfaces and analyzed the tactile discrimination performance regarding the sensitivity to surface statistics. The results suggest that observers have sensitivity to lower-order statistics whereas not to higher-order statistics. That is, touch differs from vision not only in spatiotemporal resolution but also in (in)sensitivity to high-level surface statistics.

A compact multi-pattern encoding descriptor for texture classification

Binary pattern family is considered as a powerful tool for visual texture classification. Most popular methods improve the classification performance by multi-feature fusion. However, many sub-features are redundant and low-discriminative and the classification system has high computational complexity and unsatisfactory results. To handle above problems, this paper proposes a compact multi-pattern encoding descriptor for visual texture classification. First, we develop local extremum patterns and local center pattern to represent the neighborhood intensity changes. Then, we design a compact encoding scheme to encode local maximum, minimum and center patterns into a three-bit binary code, named MMC pattern. Finally, a compact multi-pattern encoding descriptor is proposed by combining the traditional local sign pattern and MMC pattern. Experimental results on five representative texture databases demonstrate that our method achieves the state-of-the-art texture classification performance.

FSD-SLAM: a fast semi-direct SLAM algorithm

Current visual-based simultaneous localization and mapping(SLAM) system suffers from feature loss caused by fast motion and unstructured scene in complex environments. Addressing this problem, a fast semi-direct SLAM algorithm is proposed in this paper. The main idea of this method is to combine the feature point method with the direct method in order to improve the robustness of the system in the environment of scarce visual features and low texture. First, the feature enhancement module based on subgraph is developed to extract image feature points more stably. Second, an apparent shape weighted fusion method is proposed for camera pose estimation, which can still work robustly in the absence of feature points. Third, an incremental dynamic covariance scaling algorithm is studied for optimizing the error of camera pose estimation. Finally, based on the optimized camera pose, a face element model is designed to estimate and fuse the point cloud pose, and obtain an ideal three-dimensional point cloud map. The proposed algorithm has been tested extensively on the benchmark TUM dataset and the real environment. The results show that the algorithm has better performance than existing visual based SLAM algorithms.

Failure of resting-state frontal–occipital connectivity in linking visual perception with reading fluency in Chinese children with developmental dyslexia

It is widely accepted that impairment in visual perception impedes children's reading development, and further studies have demonstrated significant enhancement in reading fluency after visual perceptual training. However, the mechanism of the neural linkage between visual perception and reading is unclear. The purpose of this study was to examine the intrinsic functional relationship between visual perception (indexed by the texture discrimination task,TDT) and reading ability (character reading and reading fluency) in Chinese children with developmental dyslexia (DD) and those with typical development (TD). The resting-state functional connectivity (RSFC) between the primary visual cortex (V1, BA17) and the entire brain was analyzed. In addition, how RSFC maps are associated with TDT performance and reading ability in the DD and TD groups was examined. The results demonstrated that the strength of the RSFC between V1 and the left middle frontal gyrus (LMFG, BA9/BA46) was significantly correlated with both the threshold (SOA) of the TDT and reading fluency in TD children but not in DD children. Moreover, LMFG-V1 resting-state connectivity played a mediating role in the association of visual texture discrimination and reading fluency, but not in character reading, in TD children. In contrast, this mediation was absent in DD children, albeit their strengths of RSFC between V1 and the left middle frontal gyrus (LMFG) were comparable to those for the TD group. These findings indicate that typically developing children use the linkage of the RSFC between the V1 and LMFG for visual perception skills, which in turn promote fluent reading; in contrast, children with dyslexia, who had higher TDT thresholds than TD children, could not take advantage of their frontal–occipital connectivity to improve reading fluency abilities. These findings suggest that visual perception plays an important role in reading skills and that children with developmental dyslexia lack the ability to use their frontal–occipital connectivity to link visual perception with reading fluency.

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

ABSTRACT 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.

Preliminary measurement scales for sparkle and graininess

Large effect pigments, widely used in various fields of industrial applications, produce characteristic visual textures known as sparkle and graininess, which need to be quantified by objective or subjective methods. The development of preliminary measurement scales for sparkle and graininess, whose recommendation is now under discussion in the International Commission on Illumination (CIE), is described in this article. These scales are absolute, linear and traceable to standards of optical radiation metrology. The main purpose of this article is to justify the convenience of adopting these preliminary measurements scales, showing clear evidence that they correlate well with subjective evaluations. Before standardization, these scales need to be validated with more experimental data, including different specimens and experimental systems from other research groups.

The Camouflage Machine: Optimizing protective coloration using deep learning with genetic algorithms.

Evolutionary biologists frequently wish to measure the fitness of alternative phenotypes using behavioral experiments. However, many phenotypes are complex. One example is coloration: camouflage aims to make detection harder, while conspicuous signals (e.g., for warning or mate attraction) require the opposite. Identifying the hardest and easiest to find patterns is essential for understanding the evolutionary forces that shape protective coloration, but the parameter space of potential patterns (colored visual textures) is vast, limiting previous empirical studies to a narrow range of phenotypes. Here, we demonstrate how deep learning combined with genetic algorithms can be used to augment behavioral experiments, identifying both the best camouflage and the most conspicuous signal(s) from an arbitrarily vast array of patterns. To show the generality of our approach, we do so for both trichromatic (e.g., human) and dichromatic (e.g., typical mammalian) visual systems, in two different habitats. The patterns identified were validated using human participants; those identified as the best for camouflage were significantly harder to find than a tried-and-tested military design, while those identified as most conspicuous were significantly easier to find than other patterns. More generally, our method, dubbed the "Camouflage Machine," will be a useful tool for identifying the optimal phenotype in high dimensional state spaces.

Computer vision for microscopic skin cancer diagnosis using handcrafted and non-handcrafted features.

Skin covers the entire body and is the largest organ. Skin cancer is one of the most dreadful cancers that is primarily triggered by sensitivity to ultraviolet rays from the sun. However, the riskiest is melanoma, although it starts in a few different ways. The patient is extremely unaware of recognizing skin malignant growth at the initial stage. Literature is evident that various handcrafted and automatic deep learning features are employed to diagnose skin cancer using the traditional machine and deep learning techniques. The current research presents a comparison of skin cancer diagnosis techniques using handcrafted and non-handcrafted features. Additionally, clinical features such as Menzies method, seven-point detection, asymmetry, border color and diameter, visual textures (GRC), local binary patterns, Gabor filters, random fields of Markov, fractal dimension, and an oriental histography are also explored in the process of skin cancer detection. Several parameters, such as jacquard index, accuracy, dice efficiency, preciseness, sensitivity, and specificity, are compared on benchmark data sets to assess reported techniques. Finally, publicly available skin cancer data sets are described and the remaining issues are highlighted.

Perception and Appreciation of Tactile Objects: The Role of Visual Experience and Texture Parameters

This exploratory study was designed to examine the effects of visual experience and specific texture parameters on both discriminative and aesthetic aspects of tactile perception. To this end, the authors conducted two experiments using a novel behavioral (ranking) approach in blind and (blindfolded) sighted individuals. Groups of congenitally blind, late blind, and (blindfolded) sighted participants made relative stimulus preference, aesthetic appreciation, and smoothness or softness judgment of two-dimensional (2D) or three-dimensional (3D) tactile surfaces through active touch. In both experiments, the aesthetic judgment was assessed on three affective dimensions, Relaxation, Hedonics, and Arousal, hypothesized to underlie visual aesthetics in a prior study. Results demonstrated that none of these behavioral judgments significantly varied as a function of visual experience in either experiment. However, irrespective of visual experience, significant differences were identified in all these behavioral judgments across the physical levels of smoothness or softness. In general, 2D smoothness or 3D softness discrimination was proportional to the level of physical smoothness or softness. Second, the smoother or softer tactile stimuli were preferred over the rougher or harder tactile stimuli. Third, the 3D affective structure of visual aesthetics appeared to be amodal and applicable to tactile aesthetics. However, analysis of the aesthetic profile across the affective dimensions revealed some striking differences between the forms of appreciation of smoothness and softness, uncovering unanticipated substructures in the nascent field of tactile aesthetics. While the physically softer 3D stimuli received higher ranks on all three affective dimensions, the physically smoother 2D stimuli received higher ranks on the Relaxation and Hedonics but lower ranks on the Arousal dimension. Moreover, the Relaxation and Hedonics ranks accurately overlapped with one another across all the physical levels of softness/hardness, but not across the physical levels of smoothness/roughness. These findings suggest that physical texture parameters not only affect basic tactile discrimination but differentially mediate tactile preferences, and aesthetic appreciation. The theoretical and practical implications of these novel findings are discussed.

Perception and Appreciation of Tactile Objects: The Role of Visual Experience and Texture Parameters †

This exploratory study was designed to examine the effects of visual experience and specific texture parameters on both discriminative and aesthetic aspects of tactile perception. To this end, the authors conducted two experiments using a novel behavioral (ranking) approach in blind and (blindfolded) sighted individuals. Groups of congenitally blind, late blind, and (blindfolded) sighted participants made relative stimulus preference, aesthetic appreciation, and smoothness or softness judgment of two-dimensional (2D) or three-dimensional (3D) tactile surfaces through active touch. In both experiments, the aesthetic judgmen t was assessed on three affective dimensions, Relaxation, Hedonics, and Arousal, hypothesized to underlie visual aesthetics in a prior study. Results demonstrated that none of these behavioral judgments significantly varied as a function of visual experience in either experiment. However, irrespective of visual experience, significant differences were identified in all these behavioral judgments across the physical levels of smoothness or softness. In general, 2D smoothness or 3D softness discrimination was proportional to the level of physical smoothness or softness. Second, the smoother or softer tactile stimuli were preferred over the rougher or harder tactile stimuli. Third, the 3D affective structure of visual aesthetics appeared to be amodal and applicable to tactile aesthetics. However, analysis of the aesthetic profile across the affective dimensions revealed some striking differences between the forms of appreciation of smoothness and softness, uncovering unanticipated substructures in the nascent field of tactile aesthetics. While the physically softer 3D stimuli received higher ranks on all three affective dimensions, the physically smoother 2D stimuli received higher ranks on the Relaxation and Hedonics but lower ranks on the Arousal dimension. Moreover, the Relaxation and Hedonics ranks accurately overlapped with one another across all the physical levels of softness/hardness, but not across the physical levels of smoothness/roughness. These findings suggest that physical texture parameters not only affect basic tactile discrimination but differentially mediate tactile preferences, and aesthetic appreciation. The theoretical and practical implications of these novel findings are discussed.

Title Design in Bollywood Movie Posters: Design Features, Trends, and the Role of Technology

India has a diverse visual culture and languages. Cinema art, especially the publicity materials used in Bollywood, is one of the significant parts of this visual culture. Publicity in print media is evident since the beginning. It started with text-based newspaper advertisements, shortly followed by posters and banners full of visuals. Film posters act as a form of visual language through which the gist of the film can be perceived. It is a composition of static image and text that gives the firsthand visual experience of the movie to the viewers. Along with the visuals, title design is another key element of a movie poster. It has been used as a logotype. Unlike the conventional typography, title design has been explored in many ways such as exploration with the shape, size, color, position, orientation, visual texture, and letterform structure to convey meaning and theme of the movie more effectively. There is significant literature on the visual content of the poster art; however, the title design has been neglected throughout the timeline. This study is intended to examine the unique design features used in the title design, their trends, and the role of technology. The finding reveals key design features and their trend across the timeline. These features also show a significant change from pre-digital to the digital era, revealing the role of technology and other factors in the development.

Accelerated Path Tracing With GAN and Matrix Completion

Denoising-based techniques have recently been shown to be effective for accelerating path tracing rendering methods. However, there remains a problem which is input images need the minimum necessary samples number in order to ensure the quality of the output. In this paper, we propose a new accelerated path tracing approach with generative adversarial networks(GAN) and matrix completion. Unlike the methods based on denoising with neural network, we randomly render part of pixels of input image, which are much less than other methods. Next, we utilize the trained GAN to pre-complete the initializing missing pixels. Because of the accuracy and fast-convergence of GAN, our pre-completion results are more accurate than other methods. Then, according to the results of pre-completion, we present the pre-completed images as a low-rank matrix and make use of the matrix completion to recovers missing values accurately even in high details. To improve the efficiency of solving matrix completion, we modified the original weighted nuclear norm minimization with a parameter adjustment(PAWNNM) strategy. The result shows better visual quality, texture details and convergence efficiency than the state-of-the-art acceleration methods, especially the methods based on denoising with neural network.

Imitating Christ and the Saints: Katrin Gebbe’s Tore tanzt/Nothing Bad Can Happen (DE 2013) as Hagiographic Narration

Erwin Panofsky already pointed to the fact that the then fairly new medium of film relied on the Western iconographic tradition and created iconographic conventions of its own. Therefore, it is not surprising that film makers exploited the visual and narrative treasures of the Christian hagiographic tradition on the movie screen throughout the medium’s history. They did not only re-tell the life of Christ and the saints, but also created new plots following and re-interpreting the established conventions of hagiographic genres. Furthermore, the citation and adaptation of Christian iconographies is even used to render protagonists more complex and to regulate the viewer’s emotional response. The present article analyses Katrin Gebbe’s film Tore tanzt/Nothing Bad Can Happen (DE 2013) as an example of how biblical and hagiographic traditions are used in contemporary cinema to critically discuss religious mind-sets and the social dynamics of violent family structures. The director does not simply model the protagonist of her film as a modern day Christ who sacrifices himself for others, but clearly characterises Tore through references to other saints and biblical figures among whom Saint Francis, the alter Christus of the 13th century, is of major importance. Around this core idea, Gebbe creates a dense visual texture of hagiographic signs and allusions contradicting and enforcing one another so that the viewer’s uncertainty grows as to how to judge the events on screen.

Synthesizing the Roughness of Textured Surfaces for an Encountered-Type Haptic Display Using Spatiotemporal Encoding.

Encountered-type haptic rendering provides realistic, free-to-touch, and move-and-collide haptic sensation to a user. However, inducing haptic-texture sensation without complicated tactile actuators is challenging for encountered-type haptic rendering. In this article, we propose a novel texture synthesizing method for an encountered-type haptic display using spatial and temporal encoding of roughness, which provides both active and passive touch sensation requiring no complicated tactile actuation. Focused on macro-scale roughness perception, we geometrically model the textured surface with a grid of hemiellipsoidal bumps, which can provide a variety of perceived roughness as the user explores the surface with one's bare hand. Our texture synthesis method is based on two important hypotheses. First, we assume that perceptual roughness can be spatially encoded along the radial direction of a textured surface with hemiellipsoidal bumps. Second, perceptual roughness temporally varies with the relative velocity of a scanning human hand with respect to the surface. To validate these hypotheses on our spatiotemporal encoding method, we implemented an encountered-type haptic texture rendering system using an off-the-shelf collaborative robot that can also track the user's hand using IR sensors. We performed psychophysical user tests with 25 participants and verified the main effects of spatiotemporal encoding of a textured model on the user's roughness perception. Our empirical experiments imply that the users perceive a more rough texture as the surface orientation or the relative hand motion increases. Based on these findings, we show that our visuo-haptic system can synthesize an appropriate level of roughness corresponding to diverse visual textures by suitably choosing encoding values.

Research on the Visual Texture Presentation of Fabrics

This article discusses the process of repairing, optimizing and manufacturing of fabrics by 3D software, and the 3D scanned imaging theory. To present a simulated texture effect on the fabric, it needs to refer to three necessary conditions of visual texture and use the advantages of 3D software. It will be possible to form a highly simulated visual texture fabric after optimized testing.

Uncertainty-Aware Semantic Guidance and Estimation for Image Inpainting

Completing a corrupted image by filling in correct structures and reasonable textures for a complex scene remains an elusive challenge. In case that a missing hole involves diverse semantic information, conventional two-stage approaches based on structural information often lead to unreliable structural prediction and ambiguous visual texture generation. To address the problem, we propose a SEmantic GUidance and Estimation Network (SeGuE-Net) that iteratively evaluates the uncertainty of inpainted visual contents based on pixel-wise semantic inference and optimize structural priors and inpainted contents alternatively. Specifically, SeGuE-Net utilizes semantic segmentation maps as guidance in each iteration of image inpainting, under which location-dependent inferences are re-estimated, and, accordingly, poorly-inferred regions are refined in subsequent iterations. Extensive experiments on real-world images demonstrate the superiority of our proposed method over state-of-the-art approaches in terms of clear boundaries and photo-realistic textures.