Synthetic Textures Research Articles

A sensibility study of the autobinomial model estimation methods based on a feature similarity index

The estimation of parameters in the Autobinomial model is an important task for characterizing the content of an image and generating synthetic textures. This paper compares the performance of three estimation methods of the model: coding, maximum pseudo-likelihood and conditional least squares, under textures with different levels of additive contamination, using a feature similarity image index, via Monte Carlo studies. This novel framework quantifies the similarity between the original texture and its texture regenerated by each method. Differences in performance were tested with a Repeated Measures ANOVA model design. Simulation results show that the Conditional Least Squares method is associated with the highest value of the similarity image measure in contaminated textures, while Coding and Maximum Pseudo-Likelihood methods have a comparable behavior and there is no clear pattern whether to prefer one over the other. An application for landscape classification using real Landsat images with different spatial resolutions is described.

The Visual N1 Is Sensitive to Deviations from Natural Texture Appearance.

Disruptions of natural texture appearance are known to negatively impact performance in texture discrimination tasks, for example, such that contrast-negated textures, synthetic textures, and textures depicting abstract art are processed less efficiently than natural textures. Presently, we examined how visual ERP responses (the P1 and the N1 in particular) were affected by violations of natural texture appearance. We presented participants with images depicting either natural textures or synthetic textures made from the original stimuli. Both stimulus types were additionally rendered either in positive or negative contrast. These appearance manipulations (negation and texture synthesis) preserve a range of low-level features, but also disrupt higher-order aspects of texture appearance. We recorded continuous EEG while participants completed a same/different image discrimination task using these images and measured both the P1 and N1 components over occipital recording sites. While the P1 exhibited no sensitivity to either contrast polarity or real/synthetic appearance, the N1 was sensitive to both deviations from natural appearance. Polarity reversal and synthetic appearance affected the N1 latency differently, however, suggesting a differential impact on processing. Our results suggest that stages of visual processing indexed by the P1 and N1 are sensitive to high-order statistical regularities in natural textures and also suggest that distinct violations of natural appearance impact neural responses differently.

A multiphase texture segmentation method based on local intensity distribution and Potts model

Because texture images cannot be directly processed by the gray level information of individual pixel, we propose a new texture descriptor which reflects the intensity distribution of the patch centered at each pixel. Then the general multiphase image segmentation model of Potts model is extended for texture segmentation by adding the region information of the texture descriptor. A fast numerical scheme based on the split Bregman method is designed to speed up the computational process. The algorithm is efficient, and both the texture descriptor and the characteristic functions can be implemented easily. Experiments using synthetic texture images, real natural scene images and synthetic aperture radar images are presented to give qualitative comparisons between our method and other state-of-the-art techniques. The results show that our method can accurately segment object regions and is competitive compared with other methods especially in segmenting natural images.

Text-independent printer identification based on texture synthesis

There are many methods available for printer identification of questioned documents, however most of them need identical contents of the training and testing documents. There is no effective method yet when the contents of the training and testing documents are different. To overcome this obstacle a method based on synthetic texture analysis is proposed in this paper. The inner texture of printed characters is reconstructed in order to generate an artificial textural image similar to the real texture while independent of document content. Fast Fourier Transform (FFT) and Gray Level Co-occurrence Matrix (GLCM) methods are then used for feature extraction. Experimental results demonstrate that the proposed method can achieve a high recognition rate and provide a new platform for printer identification independent on the content of documents.

Invariant texture perception is harder with synthetic textures: Implications for models of texture processing

Texture synthesis models have become a popular tool for studying the representations supporting texture processing in human vision. In particular, the summary statistics implemented in the Portilla–Simoncelli (P–S) model support high-quality synthesis of natural textures, account for performance in crowding and search tasks, and may account for the response properties of V2 neurons. We chose to investigate whether or not these summary statistics are also sufficient to support texture discrimination in a task that required illumination invariance. Our observers performed a match-to-sample task using natural textures photographed with either diffuse overhead lighting or lighting from the side. Following a briefly presented sample texture, participants identified which of two test images depicted the same texture. In the illumination change condition, illumination differed between the sample and the matching test image. In the no change condition, sample textures and matching test images were identical. Critically, we generated synthetic versions of these images using the P–S model and also tested participants with these. If the statistics in the P–S model are sufficient for invariant texture perception, performance with synthetic images should not differ from performance in the original task. Instead, we found a significant cost of applying texture synthesis in both lighting conditions. We also observed this effect when power-spectra were matched across images (Experiment 2) and when sample and test images were drawn from unique locations in the parent textures to minimize the contribution of image-based processing (Experiment 3). Invariant texture processing thus depends upon measurements not implemented in the P–S algorithm.

Extracting Corresponding Point Based on Texture Synthesis for Nearly Flat Textureless Object Surface

Since the image feature points are always gathered at the range with significant intensity change, such as textured portions or edges of an image, which can be detected by the state-of-the-art intensity based point-detectors, there is nearly no point in the areas of low textured detected by classical interest-point detectors. In this paper we describe a novel algorithm based on affine transform and graph cut for interest point detecting and matching from wide baseline image pairs with weakly textured object. The detection and matching mechanism can be separated into three steps: firstly, the information on the large textureless areas will be enhanced by adding textures through the proposed texture synthesis algorithm TSIQ. Secondly, the initial interest-point set is detected by classical interest-point detectors. Finally, graph cuts are used to find the globally optimal set of matching points on stereo pairs. The efficacy of the proposed algorithm is verified by three kinds of experiments, that is, the influence of point detecting from synthetic texture with different texture sample, the stability under the different geometric transformations, and the performance to improve the quasi-dense matching algorithm, respectively.

Steganography using reversible texture synthesis.

We propose a novel approach for steganography using a reversible texture synthesis. A texture synthesis process resamples a smaller texture image, which synthesizes a new texture image with a similar local appearance and an arbitrary size. We weave the texture synthesis process into steganography to conceal secret messages. In contrast to using an existing cover image to hide messages, our algorithm conceals the source texture image and embeds secret messages through the process of texture synthesis. This allows us to extract the secret messages and source texture from a stego synthetic texture. Our approach offers three distinct advantages. First, our scheme offers the embedding capacity that is proportional to the size of the stego texture image. Second, a steganalytic algorithm is not likely to defeat our steganographic approach. Third, the reversible capability inherited from our scheme provides functionality, which allows recovery of the source texture. Experimental results have verified that our proposed algorithm can provide various numbers of embedding capacities, produce a visually plausible texture images, and recover the source texture.

Nonlocal TV-L1 Inpainting Model and its Augmented Lagrangian Algorithm

Nonlocal differential operators have been extensively applied to variational models for image restoration due to its texture-preserving capability. In this paper, we propose a nonlocal TV (total variation)-L1 model for texture image inpainting, which, technically, combines nonlocal operators for regularization term and L1 norm for data term. The former is used to regularize texture and the latter to preserve contrast of images. In addition, we develop augmented Lagrangian algorithm for proposed model by introducing nonlocal auxiliary variable and Lagrangian multiplier. Finally, extensive experiments on synthetic and real texture images are presented to validate the effectiveness and efficiency of our proposed model and algorithm.

Invariant texture recognition depends on high-order statistics

Texture synthesis models based on joint statistics of wavelet coefficients across scale, orientation, and position have become a popular tool for studying the representations that support texture processing in the human visual system. In particular, the summary statistics implemented in the Portilla-Simoncelli model account for observer performance in crowding, search tasks, and the response properties of V2 neurons. Presently, we chose to investigate whether or not this set of summary statistics was also sufficient to support performance in a texture discrimination task that required invariance to illumination. We selected 14 pairs of visually-matched textures from the Amsterdam Library of Textures, including a diverse range of material properties. Each texture was depicted under two illumination conditions: diffuse overhead lighting and strong side lighting. We generated a synthetic image from each original texture image using the Portilla-Simoncelli model. Using these images, we asked observers (N=13) to complete four match-to-sample tasks. Each task briefly presented (250ms) a sample texture to be matched to one of two test images presented after a 500ms ISI- one test image depicted the same texture as the sample, the other a visually-matched distractor. In the Illumination-change condition, the target texture and the sample were differently illuminated; In the No-change condition, lighting was the same. Observers completed both conditions using real and synthetic textures in separate blocks. We observed significant main effects of real/synthetic appearance (p<0.001) and illumination condition on accuracy (p<0.001), qualified by an interaction between these factors (p<0.001), such that performance in the No-change condition was slightly worse for synthetic textures (~5% cost), but that this difference was much larger in the Illumination-change condition (~20% cost). We conclude that invariant texture recognition relies on statistics not included in the P-S model and natural texture appearance overall leads to better discrimination performance. Meeting abstract presented at VSS 2014

Multi-Phase Texture Segmentation Using Gabor Features Histograms Based on Wasserstein Distance

AbstractWe present a multi-phase image segmentation method based on the histogram of the Gabor feature space, which consists of a set of Gabor-filter responses with various orientations, scales and frequencies. Our model replaces the error function term in the original fuzzy region competition model with squared 2-Wasserstein distance function, which is a metric to measure the distance of two histograms. The energy functional is minimized by alternative minimization method and the existence of closed-form solutions is guaranteed when the exponent of the fuzzy membership term being 1 or 2. We test our model on both simple synthetic texture images and complex natural images with two or more phases. Experimental results are shown and compared to other recent results.

Images as Occlusions of Textures: A Framework for Segmentation

We propose a new mathematical and algorithmic framework for unsupervised image segmentation, which is a critical step in a wide variety of image processing applications. We have found that most existing segmentation methods are not successful on histopathology images, which prompted us to investigate segmentation of a broader class of images, namely those without clear edges between the regions to be segmented. We model these images as occlusions of random images, which we call textures, and show that local histograms are a useful tool for segmenting them. Based on our theoretical results, we describe a flexible segmentation framework that draws on existing work on nonnegative matrix factorization and image deconvolution. Results on synthetic texture mosaics and real histology images show the promise of the method.

Infant Preference for Natural Texture Statistics is Modulated by Contrast Polarity.

Adult observers are sensitive to statistical regularities present in natural images. Developmentally, research has shown that children do not show sensitivity to these natural regularities until approximately 8-10 years of age. This finding is surprising given that even infants gradually encode a range of high-level statistical regularities of their visual environment in the first year of life, We suggest that infants may in fact exhibit sensitivity to natural image statistics under circumstances where images of complex, natural textures, such as a photograph of rocks, are used as experimental stimuli and natural appearance is substantially manipulated. We tested this hypothesis by examining how infants' visual preference for real versus computer-generated synthetic textures was modulated by contrast negation, which produces an image similar to a photographic negative. We observed that older infants' (9-months of age) preferential looking behavior in this task was affected by contrast polarity, suggesting that the infant visual system is sensitive to deviations from natural texture appearance, including (1) discrepancies in appearance that differentiate natural and synthetic textures from one another and (2) the disruption of contrast polarity following negation. We discuss our results in the context of adult texture processing and the "perceptual narrowing" of visual recognition during the first year of life.

Unsupervised Texture Segmentation Using Active Contour Model and Oscillating Information

Textures often occur in real-world images and may cause considerable difficulties in image segmentation. In order to segment texture images, we propose a new segmentation model that combines image decomposition model and active contour model. The former model is capable of decomposing structural and oscillating components separately from texture image, and the latter model can be used to provide smooth segmentation contour. In detail, we just replace the data term of piecewise constant/smooth approximation in CCV (convex Chan-Vese) model with that of image decomposition model-VO (Vese-Osher). Therefore, our proposed model can estimate both structural and oscillating components of texture images as well as segment textures simultaneously. In addition, we design fast Split-Bregman algorithm for our proposed model. Finally, the performance of our method is demonstrated by segmenting some synthetic and real texture images.

Higher order image structure enables boundary segmentation in the absence of luminance or contrast cues.

Lower order image statistics, which can be described by an image's Fourier energy content, enable segmentation when they are different on either side of a boundary. We have previously demonstrated that the spatial distribution of the energy in an image (described by its higher order statistics or structure) could influence segmentation thresholds for contrast- and orientation-defined boundaries, even though it was the same on either side of the boundary and thus task irrelevant (Zavitz & Baker, 2013). Here we examined whether higher order statistics can also enable segmentation when boundaries are defined by differences in structure or density of texture elements. We used micropattern-based naturalistic synthetic textures to manipulate the sparseness, global phase alignment, and local phase alignment of carrier textures and measured segmentation thresholds based on forced-choice judgments of boundary orientation. We found that both global phase structure and sparseness, but not local phase alignment, enable segmentation and that local structure also has a small effect on segmentation thresholds in both cases. Simulations of a two-stage filter model with a compressive intermediate nonlinearity can reproduce the major features of the experimental data, segmenting boundaries defined by higher order statistics alone while capturing the influence of global image structure on segmentation thresholds.

Texture sparseness, but not local phase structure, impairs second-order segmentation

Texture boundary segmentation is typically thought to reflect a comparison of differences in Fourier energy (i.e. low-order texture statistics) on either side of a boundary. However in a previous study (Arsenault, Yoonessi, & Baker, 2011) we showed that the distribution of energy within a natural texture (i.e. its higher-order statistical structure) also influences segmentation of contrast boundaries. Here we examine the influence of specific higher-order texture statistics on segmentation of contrast- and orientation-defined boundaries. Using naturalistic synthetic textures to manipulate the sparseness, global phase structure, and local phase alignments of carrier textures, we measure segmentation thresholds based on forced-choice judgments of boundary orientation. We find a similar pattern of results for both contrast and orientation boundaries: (1) randomizing all structure by globally phase scrambling the texture reduces segmentation thresholds substantially, (2) decreasing sparseness also reduces thresholds, and (3) removing local phase alignments has little or no effect on segmentation thresholds. We show that a two-stage filter model with an intermediate compressive nonlinearity and expansive output nonlinearity can account for these data using synthetic textures. Furthermore, the model parameter fits obtained using synthetic textures also predict the segmentation thresholds presented in Arsenault, Yoonessi, and Baker (2011) for natural and phase-scrambled natural texture carriers.

Natural scene statistics relate to perceptual salience of second-, third-, and fourth-order spatial correlations

The statistical regularities of natural scenes are a starting point for understanding the characteristics of early visual processing, e.g. the center-surround architecture of retinal ganglion cell receptive fields. Can this matching between natural signal statistics and neural processing mechanisms be extended beyond the sensory periphery? Our recent work [1] revealed that human sensitivity to fourth-order correlations in synthetic textures, known to arise in cortex, is closely related to the structure of fourth-order spatial correlations in natural scenes. This leads us to propose a specific organizing principle: The perceptual salience of visual textures increases with the variance, or unpredictability, of the corresponding correlations over the ensemble of natural scenes. To test this principle, we examined the statistical regularities of binarized natural images as characterized by correlations between adjacent pixels within a 2 × 2 square. Local binary textures can be described by four types of second-order correlations between pixels arranged in vertical (βV), horizontal (βH) and diagonal (β and β/) configurations, four types of third-order correlations between pixel triplets (θ1,2,3,4), and one type of fourth-order correlation between pixel quadruplets (α). We measured the values of these correlations in a large ensemble of image patches, and we compared the results to psychophysical experiments that measure human sensitivity to synthetic visual textures. Both the ordering and magnitude of natural image variances was found to match perceptual sensitivities to synthetic textures generated with the corresponding correlations (Figure ​(Figure1a).1a). Furthermore, the principal components of the full 9D space of image statistics match the principal components of the corresponding space of sensitivities (Figure 1b-e). These results suggest that central neural mechanisms are efficiently tuned to the higher-order statistics of natural scenes. Figure 1 (a) Variances in image statistics match perceptual sensitivities. Principal components of image statistics match principal components of human sensitivities, as shown by the fractional contribution of (b) βHV, (c) β/\, (d) θ, and ...

Predicament of identity in Iran: a qualitative meta-analysis of theoretical and empirical studies on identity

Identity is regarded as a theoretical and practical problematic issue constituting a large range of literature in social planning, political activities and cultural policy-making in the last decades. Thus, this study is to Meta-analyze three sorts of studies on identity in a distinctive perspective. These are: universal theories of identity, theoretical studies of identity in Iran and research studies on Iranian identity in Iranian academic sphere. The findings demonstrate that the sociological theories are more effective to discuss identity than other scientific fields. While the theoretical studies focused mainly on national level of identity, the empirical ones highlighted the sub-nation and global dimensions of identity. Most of studied researches employed constructivist approach. According to literature review of identity studies in Iran, the main sources of identity are the Persian language, Shiite religion, Islam, Oriental Culture, Oriental Mysticism, Modernity and National History. Generally, the sources of identity are diverse. The results indicate that the constitution of Iranian identity portrays a hybrid and synthetic texture in all levels.

Image segmentation based on differential immune clone clustering algorithm

PurposeThe purpose of this paper is to present a Differential Immune Clone Clustering Algorithm (DICCA) to solve image segmentation.Design/methodology/approachDICCA combines immune clone selection and differential evolution, and two populations are used in the evolutionary process. Clone reproduction and selection, differential mutation, crossover and selection are adopted to evolve two populations, which can increase population diversity and avoid local optimum. After extracting the texture features of an image and encoding them with real numbers, DICCA is used to partition these features, and the final segmentation result is obtained.FindingsThis approach is applied to segment all sorts of images into homogeneous regions, including artificial synthetic texture images, natural images and remote sensing images, and the experimental results show the effectiveness of the proposed algorithm.Originality/valueThe method presented in this paper represents a new approach to solving clustering problems. The novel method applies the idea two populations are used in the evolutionary process. The proposed clustering algorithm is shown to be effective in solving image segmentation.

Unsupervised texture segmentation based on latent topic assignment

We present an effective solution for unsupervised texture segmentation by taking advantage of the latent Dirichlet allocation (LDA) model. LDA is a generative topic model that is capable of hierarchically organizing discrete data including texts and images. We propose a new texture model by connecting texture primitives to the topic of LDA. The model is able to extract the characteristic features of a texture primitive and group them into a topic based on their frequencies of co-occurrence. Here, the feature descriptor is the connection of Haar-like features of multiple sizes. The segments of an image are finally obtained by identifying the homogeneous regions in the corresponding topic assignment map. The evaluation results for synthetic texture mosaics, remote sensing images, and natural scene images are illustrated.

TEXTURAL DESCRIPTORS FOR MULTIPHASIC ORE PARTICLES

Monitoring of mineral processing circuits by means of particle liberation analysis through quantitative image analysis has become a routine technique within the last decades. Usually, liberation indices are computed as weight proportions, which is not informative enough when complex texture ores are treated by flotation. In these cases, liberation has to be computed as phase surface exposed to reactants, and textural relationships between minerals have to be characterized to determine the possibility of increasing exposure. In this paper, some indices to achieve a complete texture characterization have been developed in terms of 2D phase contact and mineral surfaces exposure. Indices suggested by other authors are also compared. The response of this set of parameters against textural changes has been explored on simple synthetic textures ranging from single to multiple inclusions and single to multiple veins and their ability to discriminate between different textural features is analyzed over real mineral particles with known internal structure.