Color Texture Analysis Research Articles

Towards Robust Colour Texture Analysis with Limited Training Data

Texture analysis plays an important role in different domains of healthcare, agriculture, and industry, where multi-channel sensors are gaining more attention. This contribution presents an interpretable and efficient framework for texture classification and segmentation that exploits colour or channel information and does not require much data to produce accurate results. This makes such a framework well-suited for medical applications and resource-limited hardware. Our approach builds upon a distance-based generalized matrix learning vector quantization (GMLVQ) algorithm. We extend it with parametrized angle-based dissimilarity and introduce a special matrix format for multi-channel images. Classification accuracy evaluation of various model designs was performed on VisTex and ALOT data, and the segmentation application was demonstrated on an agricultural data set. Our extension of parametrized angle dissimilarity measure leads to better model generalization and robustness against varying lighting conditions than its Euclidean counterpart. The proposed matrix format for multichannel images enhances classification accuracy while reducing the number of parameters. Regarding segmentation, our method shows promising results, provided with a small class-imbalanced training data set. Proposed methodology achieves higher accuracy than prior work benchmarks and a small-scale CNN while maintaining a significantly lower parameter count. Notably, it is interpretable and accurate in scenarios where limited and unbalanced training data are available.

Rozpoznawanie materiałów budowlanych i ekstrakcja cech przy użyciu małego czujnika CCD oraz technik analizy obrazu i klastrowania

Heritage Building material recognition is the process of classifying building materials based on their visual appearance. It is important in construction, urban planning, and archaeology. Image analysis is a common approach, starting with acquiring RGB images, then extracting features using techniques such as colour histograms and texture analysis, and clustering the materials into groups using algorithms like k-means. Finally, the materials are classified into categories using classifiers like decision trees, SVM, or neural networks. Image analysis is a useful tool for building material recognition, as it allows for accurate classification of building materials based on their visual characteristics.

Innovative quaternion algebra-based segmentation for improved jpeg color texture analysis

Innovative quaternion algebra-based segmentation for improved jpeg color texture analysis

Quality Control of Hidden Construction Work Using Photographs Attached to Inspection Reports

Introduction. In construction production the problem of increasing the reliability of information contained in the certificates of inspection of concealed works is more and more often solved with the help of their photofixation. Based on the results of earlier studies, the authors prove that using the diagnostic informativeness of photographs it is possible not only to confirm the fact of execution of concealed works in the required volume, but also to obtain additional information about their quality. For effective extraction of this information it is necessary to develop a method of quality control of hidden construction works on the basis of photographs attached to the inspection certificates, taking into account modern scientific achievements in the field of photogrammetry and color-texture analysis of photographic images.Materials and Methods. The developed method of quality control of hidden construction work is based on the use of contour, pixel, macro- and micro-texture analysis of photographic images. When developing the method, the results of numerous visual inspections of building structures (including the opening of their internal elements) and comparison of these results with the information contained in the certificates of certification of concealed works were used.Results. The article presents the results of a study carried out at the Don State Technical University on the development of a method for quality control of hidden construction work using photographs attached to inspection reports. The algorithms developed by the authors for analyzing photographs of building structures to control their quality are presented. The article contains proposals for the systematization and storage of typical textures of surfaces of building structures.Discussion and Conclusion. The priority condition for successful application of the new method of quality control of concealed construction works is its methodological support, which establishes a unified procedure for their photo fixation, additional requirements for the composition and parameters of photographs, rules of registration, storage and use of them as annexes to certificates of inspection of concealed construction works, as well as the regulations for comprehensive analysis of photographs with the use of appropriate software.

Color Texture Analysis as a Tool for Quantitative Evaluation of Radiation-Induced Skin Injuries

Background: Color texture analysis was applied as a tool for quantitative evaluation of radiation-induced skin injuries.Materials and Methods: We prospectively selected 20 breast cancer patients who underwent whole-breast radiotherapy after breast-conserving surgery. Color images of skin surfaces for irradiated breasts were obtained by using a mobile skin analyzer. The first skin measurement was performed before the first fraction of radiotherapy, and the subsequent measurement was conducted approximately 10 days after the completion of the entire series of radiotherapy sessions. For comparison, color images of the skin surface for the unirradiated breasts were measured similarly. For each color image, six co-occurrence matrices (red-green [RG], red-blue [RB], and green-blue [GB] from color channels, red [R], green [G], blue [B] from gray channels) can be generated. Four textural features (contrast, correlation, energy, and homogeneity) were calculated for each co-occurrence matrix. Finally, several statistical analyses were used to investigate the performance of the color textural parameters to objectively evaluate the radiation-induced skin damage.Results and Discussion: For the R channel from the gray channel, the differences in the values between the irradiated and unirradiated skin were larger than those of the G and B channels. In addition, for the RG and RB channels, where R was considered in the color channel, the differences were larger than those in the GB channel. When comparing the relative values between gray and color channels, the ‘contrast’ values for the RG and RB channels were approximately two times greater than those for the R channel for irradiated skin. In contrast, there were no noticeable differences for unirradiated skin.Conclusion: The utilization of color texture analysis has shown promising results in evaluating the severity of skin damage caused by radiation. All textural parameters of the RG and RB cooccurrence matrices could be potential indicators of the extent of skin damage caused by radiation.

Screen recapture detection based on color-texture analysis of document boundary regions

This paper examines a presentation attack detection when a document recaptured from a screen is presented instead of the original document. We propose an algorithm based on analyzing a moiré pattern within document boundary regions as a distinctive feature of the recaptured image. It is assumed that the pattern overlapping the document boundaries is a recapture artifact, not a match between document and background textures. To detect such a pattern, we propose an algorithm that employs the result of the fast Hough transform of the document boundary regions with enhanced pattern contrast. The algorithm performance was measured for the open dataset DLC-2021, which contains images of mock documents as originals and their screen recaptures. The precision of the proposed solution was evaluated as 95.4 %, and the recall as 90.5 %.

A New Automatic Watercolour Painting Algorithm Based on Dual Stream Image Segmentation Model with Colour Space Estimation

Image processing plays a crucial role in automatic watercolor painting by manipulating the digital image to achieve the desired watercolor effect. segmentation in automatic watercolor painting algorithms is essential for region-based processing, color mixing and blending, capturing brushwork and texture, and providing artistic control over the final result. It allows for more realistic and expressive watercolor-like paintings by processing different image regions individually and applying appropriate effects to each segment. Hence, this paper proposed an effective Dual Stream Exception Maximization (DSEM) for automatic image segmentation. DSEM combines both color and texture information to segment an image into meaningful regions. This approach begins by converting the image from the RGB color space to a perceptually-based color space, such as CIELAB, to account for variations in lighting conditions and human perception of color. With the color space conversion, DSEM extracts relevant features from the image. Color features are computed based on the values of the color channels in the chosen color space, capturing the nuances of color distribution within the image. Simultaneously, texture features are derived by computing statistical measures such as local variance or co-occurrence matrices, capturing the textural characteristics of the image. Finally, the model is applied over the deep learning model for the classification of the color space in the painting. Simulation analysis is performed compared with conventional segmentation techniques such a CNN and RNN. The comparative analysis states that the proposed DSEM exhibits superior performance compared to conventional techniques in terms of color space estimation, texture analysis and region merging. The performance of classification with DSEM is ~12% higher than the conventional techniques.

Color and Texture Analysis of Textiles Using Image Acquisition and Spectral Analysis in Calibrated Sphere Imaging System-I

Numerous imaging applications and analyses demand human perception, and color space transformation of device-dependent tri-band color interpretation (RGB) to device-independent CIE color space standards needs human intervention. The imaging acquisition environment, theoretical conversion errors, viewing geometry, well-defined illumination uniformity, and calibration protocols limit their precision and applicability. It is unfortunate that in most image processing applications, the spectral data are either unavailable or immeasurable. This study is based on developing a novel integrating sphere imaging system and experimentation with textiles’ controlled variation of texture and color. It proposes a simple calibration technique and describes how unique digital color signatures can be derived from calibrated RGB derivatives to extract the best features for color and texture. Additionally, an alter-ego of reflectance function, missing in the imaging domain, is suggested that could be helpful for visualization, identification, and application for qualitative and quantitative color-texture analysis. Our further investigation revealed promising colorimetric results while validating color characterization and different color combinations over three textures.

Polarization-based optical characterization for color texture analysis and segmentation

• Hand-crafted texture characterization based on light polarization signatures. • Image segmentation relying on pixel based circular polarization signature. • Multispectral image (185 bands) with five total variation planes. • The Karhunen-Loeve transform and then k-means were applied to the 185 bands. • Superior segmentation accuracy (compared with classic k-means on RGB). Texture characterization is very useful for automatic analysis of object surface images for a plethora of applications in medicine, agriculture, industry or remote sensing. Various texture characterization techniques exist, from the classical Haralick descriptors, Gabor filters, local binary patterns to automatically-extracted features using machine learning models. We propose a new hand-crafted texture characterization technique, based on light polarization property, by deploying a circular polarization filter (rotated from 0° to 360° in steps of 10°) in the image acquisition process. The hypothesis is that different materials and surfaces will exhibit different polarization signatures defined as pixel values variation as a function of polarization angle. Such polarization signature is able to locally characterize texture as a consequence of light reflections captured in every pixel due to the texture intrinsic variations. We show the usefulness of our approach for surface/material classification for the purpose of color image segmentation of natural outdoor scenes.

Phase transitions and physical properties by a color texture analysis: Results for liquid crystals

A new approach to investigate subtle liquid crystal transitions and some of their light wavelength dependent physical properties is developed and demonstrated. In this method color texture changes obtained from a polarizing optical microscope are processed, and their color features are exploited. The significance of color features in image processing is discussed with reference to a color extension of the GLCM method which demonstrates smooth phase transitions occurring in two types of chiral liquid crystals (where ferroelectric smectic phases are observed). In the proposed method representation of the physical light wavelength distribution is obtained from suitable image conversion from RGB color space model to the Hue-Saturation-Lightness (HSL) model. This allows the identification of local color changes caused by external factors e.g., electric field or stresses. In this way the analysis of certain physical quantities is possible. The approach was applied to liquid crystal blue phases to show changes in the light wavelength under an electric field, which are related to the electrostriction coefficients. The result from image analysis are compared to results from a conventional spectrophotometer.

Quantitative Measurement of Throat and Larynx After Endotracheal Intubation for Palatoplasty.

IntroductionQuantitative morphometric measurements of living human upper airway remain challenging. This study aimed to introduce a special laser projection device that can facilitate computer-assisted, digitalized analysis and provide important information on airway mucosa change, before and after endotracheal intubation for palatoplasty.Materials and MethodsThe laryngeal images were captured before and after surgery. Image processing techniques were used to quantize the post-operative laryngeal variation, with its distinct color space and texture features. Meanwhile, the maximum length of the vocal fold, vocal width at the midpoint, maximum cross-sectional area of the glottic space, maximum cross-sectional area of the oropharyngeal inlet (CSAOI) and the depth of the retropalatal space were determined and calculated. These parameters were analyzed and compared before and after surgery.ResultsA total of 39 subjects were enrolled in this study. The color space and texture analysis all show trends toward higher measures in post-operative images than in pre-operative images, especially in the interarytenoid region. Furthermore, the glottic area showed a significant decrease of 31.2%, while the vocal width showed a significant increase after intubation. The post-operative retropalatal depth and CSAOI were significantly deeper and larger than the baseline, reaching their peak in the 4th week after the surgery, and then slightly reduced in the 12th week.ConclusionFor the first time we present a series of changes in upper airway after surgery, by using a laser module with quantitative measurement. Our equipment and processing can measure subtle mucosal changes that would allow a clinician to diagnose post-operative airway inflammation in a simpler and less invasive way. Here additional information collected by different imaging modalities would help to solve multiple current unmet needs in post-operative airway inflammation.

Quantitative Measurement of Adult Human Larynx post General Anesthesia with Intubation.

Introduction: Post-anaesthetic sore throat (PAST) is a well-recognized consequence of tracheal intubation; however, quantitative morphometric measurements remain challenging. This study aimed to introduce a special laser projection device that can facilitate computer-assisted, digitalized analysis and provide important information on laryngeal mucosa change, pre and post-surgery under general anesthesia with intubation.Materials and methods: The laryngeal images were captured and divided into the control group and the intubation group. Image processing techniques were used to quantify the post-extubation laryngeal variation, with its distinct color space and texture features. Meanwhile, the maximum length of the vocal fold, vocal width at the midpoint, and maximum cross-sectional area of the glottic space were determined and calculated. These parameters were analyzed and compared pre and post-surgery.Results: A total of 69 subjects were enrolled in this study, comprising 32 subjects in the healthy group and 37 subjects in the intubation group. The color space and texture analysis with contrast and correlation profiles all shows trend toward higher measures in the intubation group than in the healthy group, with statistical significance and outstanding discrimination ability, especially in the interarytenoid region. The incidence of PAST was approximately 46% (17 patients). The gender difference, type of surgery, and the fixation position of the tube were not significantly related to the PAST occurrence. All the eigenvalues showed significant differences pre and post-surgery in the interarytenoid region and a significant trend toward red and increased contrast texture profiles was revealed. Furthermore, the glottic area showed a significant decrease of 25.29%, while the vocal width showed a significant increase post extubation.Conclusion: Our equipment and processing can measure subtle laryngeal changes that would allow a clinician to diagnose postoperative laryngeal inflammation in a simpler and less invasive way. The trend toward red, the increased contrast texture and vocal width, and the reduced glottic space were all compatible with post-intubation inflammatory response, especially in the interarytenoid region. This is important to know so that one can take appropriate steps to alleviate PAST in the future.

Effect of nanoemulsion loading a mixture of clove essential oil and carboxymethyl chitosan‐coated ε‐polylysine on the preservation of donkey meat during refrigerated storage

The objective of this research was to investigate the effects of clove essential oil, the carboxymethyl chitosan-coated ε-polylysine (ε-PL) nanoemulsion (Nano-CEO-ε-PL-CMC) on the shelf-life, and the quality characteristics of donkey meat during refrigerated storage. The physicochemical, microbiota, and sensory properties of donkey meat were regularly monitored. The results indicated that Nano-CEO-ε-PL-CMC was stable with the smallest particle size. Furthermore, we found that Nano-CEO-ε-PL-CMC had a significant preservation effect on refrigerated donkey meat by maintaining the stability of pH value, inhibiting the proliferation of microorganisms (p < .05), and sharply reducing the oxidation of fat and protein (p < .05) during the storage period. Interestingly, the flesh color measurement and texture analysis revealed that the application of Nano-CEO-ε-PL-CMC could effectively reduce the color change, and delay the decline of springiness and cohesiveness of refrigerated donkey meat. Our findings suggested that Nano-CEO-ε-PL-CMC has great potential in the preservation of donkey meat during refrigerated storage. Novelty impact statement In the present study, a novel nano-dispersion system with good stability was successfully developed by incorporating the ε-PL into clove essential oil nanoemulsion. The nanoemulsion film containing clove essential oil and ε-PL (Nano-CEO-ε-PL-CMC) has a significant preservation effect on refrigerated donkey meat by inhibiting the proliferation of microorganisms and reducing the oxidation of fat and protein (p < .05).

Multi-angle head pose classification with masks based on color texture analysis and stack generalization.

SummaryHead pose classification is an important part of the preprocessing process of face recognition, which can independently solve application problems related to multi‐angle. But, due to the impact of the COVID‐19 coronavirus pandemic, more and more people wear masks to protect themselves, which covering most areas of the face. This greatly affects the performance of head pose classification. Therefore, this article proposes a method to classify the head pose with wearing a mask. This method focuses on the information that is helpful for head pose classification. First, the H‐channel image of the HSV color space is extracted through the conversion of the color space. Then use the line portrait to extract the contour lines of the face, and train the convolutional neural networks to extract features in combination with the grayscale image. Finally, stacked generalization technology is used to fuse the output of the three classifiers to obtain the final classification result. The results on the MAFA dataset show that compared with the current advanced algorithm, the accuracy of our method is 94.14% on the front, 86.58% on the more side, and 90.93% on the side, which has better performance.

Colour texture analysis of fundus image for glaucoma diagnosis using hidden Markov models

Colour texture analysis of fundus image for glaucoma diagnosis using hidden Markov models

Detection of Face Spoofing using Color Texture and Edge Features

The wide scale use of facial recognition systems has caused concerns about spoofing attacks. Security is essential requirement for a face recognition system to provide reliable protection against spoofing attacks. Spoofing happens in situations where someone tries to behave as an authorized user to obtain illicitly access the protected system to gain advantage over it. In order to identify spoofing attacks, face spoofing detection approaches have been used. Traditional face spoofing detection techniques are not good enough as most of them focus only on the gray scale information and discarding the color information. Here a face spoofing detection approach with color texture and edge analysis is presented. The approach for investigating the texture of input images, Local binary pattern and Edge Histogram descriptor are proposed. Experiments on a publicly available dataset, Replay attack, showed excellent results compared to existing works.

A Local Binary Pattern-Based Method for Color and Multicomponent Texture Analysis

Local Binary Patterns (LBPs) have been highly used in texture classification for their robustness, their ease of implementation and their low computational cost. Initially designed to deal with gray level images, several methods based on them in the literature have been proposed for images having more than one spectral band. To achieve it, whether assumption using color information or combining spectral band two by two was done. Those methods use micro structures as texture features. In this paper, our goal was to design texture features which are relevant to color and multicomponent texture analysis without any assumption. Based on methods designed for gray scale images, we find the combination of micro and macro structures efficient for multispectral texture analysis. The experimentations were carried out on color images from Outex databases and multicomponent images from red blood cells captured using a multispectral microscope equipped with 13 LEDs ranging from 375 nm to 940 nm. In all achieved experimentations, our proposal presents the best classification scores compared to common multicomponent LBP methods. 99.81%, 100.00%, 99.07% and 97.67% are maximum scores obtained with our strategy respectively applied to images subject to rotation, blur, illumination variation and the multicomponent ones.

Phytochemical characterization, and antioxidant and antimicrobial activities of white cabbage extract on the quality and shelf life of raw beef during refrigerated storage.

Brassica vegetables are well-characterized, containing a wide-spectrum of phenolic compounds that are responsible for their diverse biological activities like antioxidant and antimicrobial activities. This study explored the preservative effect of Brassica oleracea var. capitate f. alba (white cabbage; WC) on beef under refrigerated conditions for 16 days. The antimicrobial activities of WC were evaluated against foodborne pathogenic bacteria and fungi. The antioxidant activity was determined on the basis of total phenolic and flavonoid contents, through employing DPPH and ABTS assays. The chemical composition was analyzed by GC-MS analysis. The results indicated that among the different solvent extracts, white cabbage chloroform extract [WCCE] exhibited outstanding bioactive properties due to the presence of 4-nitro-3-(trifluoromethyl)phenol, and the effects of WCCE at different levels (A and B) on the quality and shelf life of beef in storage were evaluated. The color parameters (lightness, yellowness, and redness), texture analysis, and pH values were monitored constantly with 4 days interval, and microbial analysis was conducted. The results showed that WCCE-A treatment significantly reduced the total viable counts, psychrotrophic bacteria, and yeast-molds when compared with WCCE-B and control during refrigeration storage, with the activity varying in a dose-dependent manner (p < 0.05). Significantly, the WCCE-A treatments had better appearance compared with the control after 16 days of storage. All results confirmed that WCCE which is rich in bioactive compounds, effectively maintains the quality of beef compared to the control by retarding lipid oxidation and microbial growth at refrigeration temperature and also emphasize the potential applications of this plant in different industrial sectors.

Spatio-spectral networks for color-texture analysis

Texture is one of the most-studied visual attributes for image characterization since the 1960s. However, most hand-crafted descriptors are monochromatic, focusing on grayscale images and discarding the color information. Therefore this work proposes a new method for color texture analysis considering all color channels in a more thorough approach. It consists of modeling color images as directed complex networks that we named Spatio-Spectral Network (SSN). Its topology includes within-channel connections that cover spatial patterns of individual color channels, while between-channel connections tackle spectral properties of channel pairs in an opponent fashion. Image descriptors are obtained through topological characterization of the modeled network in a multiscale approach with radially symmetric neighboring. Experiments with four datasets cover several aspects of color-texture analysis, and results demonstrate that SSN overcomes all the compared literature methods, including known deep convolutional networks. It also has the most stable performance between datasets, achieving 98.5(± 1.1) of average accuracy against 97.1(± 1.3) of MCND and 96.8(± 3.2) of AlexNet. Additionally, an experiment verifies the performance of the methods under different color spaces, showing that SSN presents the highest performance and robustness.

On specific features of the endoscopic image processing

Main principles of endoscopic image processing are considered. Specific characteristics of such images and complexities in their classification are discussed. We suggest that several features may be regarded as specific signs that can be used to discriminate between benign and malignant loci. The methods of colour histograms, gradients and texture analysis are considered and discussed.