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Special Issue "Texture and Colour in Image Analysis"

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (25 April 2020) | Viewed by 60251

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Special Issue Editors

Prof. Dr. Francesco Bianconi

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Department of Engineering, Università degli Studi di Perugia, Via Goffredo Duranti 93, 06135 Perugia, Italy
Interests: computer vision; machine learning; image processing; colour; texture; biomedical image analysis; radiomics
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Antonio Fernández

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Guest Editor

Department of Engineering Design, Universidade de Vigo, Rúa Maxwell s/n, 36310 Vigo, Spain
Interests: image processing; machine learning; computer vision; texture analysis
Special Issues, Collections and Topics in MDPI journals

Prof. Raúl E. Sánchez-Yáñez

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Guest Editor

Department of Electronics Engineering, Universidad de Guanajuato, Guanajuato, Mexico
Interests: computer vision; feature extraction; texture; colour; computational intelligence

Special Issue Information

Dear Colleagues,

        Colour and texture are among the visual properties that mostly determine the appearance of objects, materials and scenes. As a consequence, the analysis of colour and texture plays a fundamental role in a wide range of applications, for instance, object recognition, scene understanding, materials classification, defect detection, biometric identification, content-based multimedia retrieval, remote sensing and computer-assisted diagnosis.
        The field is undergoing rapid changes. While the “hand-designed” paradigm was the leading approach until not too long ago, during the last few years, research has been shifting towards data-driven models, where the visual features are no longer designed “a priori”, but learned from the data (deep learning).
        This Special issue wants to provide a forum to discuss strategies, challenges and perspectives in this field of research. We are soliciting original contributions as well as thorough reviews and comprehensive comparative evaluations. We particularly encourage the submission of theoretical works investigating the mathematical underpinnings of colour and texture analysis.

Prof. Francesco Bianconi
Prof. Dr. Antonio Fernández
Prof. Raúl E. Sánchez-Yáñez
Guest Editors

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Keywords

Mathematics of colour and texture
Perceptual models of colour and texture
Hand-designed image descriptors
Convolutional networks and deep learning
Datasets, comparative evaluations and benchmarks
Materials classification
Biomedical image analysis
Colour and texture in the arts and cultural heritage
Remote sensing

Published Papers (17 papers)

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Editorial

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4 pages, 172 KiB

Open AccessEditorial

Special Issue Texture and Color in Image Analysis

Francesco Bianconi

Antonio Fernández

and

Raúl E. Sánchez-Yáñez

Appl. Sci. 2021, 11(9), 3801; https://doi.org/10.3390/app11093801 - 22 Apr 2021

Cited by 2 | Viewed by 1217

Abstract

Texture and color are two types of visual stimuli that determine, to a great extent, the appearance of objects, materials, and scenes [...] Full article

(This article belongs to the Special Issue Texture and Colour in Image Analysis)

Research

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17 pages, 713 KiB

Open AccessArticle

Spectrogram Classification Using Dissimilarity Space

and

Appl. Sci. 2020, 10(12), 4176; https://doi.org/10.3390/app10124176 - 17 Jun 2020

Cited by 22 | Viewed by 3867

Abstract

In this work, we combine a Siamese neural network and different clustering techniques to generate a dissimilarity space that is then used to train an SVM for automated animal audio classification. The animal audio datasets used are (i) birds and (ii) cat sounds, which are freely available. We exploit different clustering methods to reduce the spectrograms in the dataset to a number of centroids that are used to generate the dissimilarity space through the Siamese network. Once computed, we use the dissimilarity space to generate a vector space representation of each pattern, which is then fed into an support vector machine (SVM) to classify a spectrogram by its dissimilarity vector. Our study shows that the proposed approach based on dissimilarity space performs well on both classification problems without ad-hoc optimization of the clustering methods. Moreover, results show that the fusion of CNN-based approaches applied to the animal audio classification problem works better than the stand-alone CNNs. Full article

(This article belongs to the Special Issue Texture and Colour in Image Analysis)

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18 pages, 14219 KiB

Open AccessArticle

Segmentation of River Scenes Based on Water Surface Reflection Mechanism

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Appl. Sci. 2020, 10(7), 2471; https://doi.org/10.3390/app10072471 - 03 Apr 2020

Cited by 8 | Viewed by 3242

Abstract

Segmentation of a river scene is a representative case of complex image segmentation. Different from road segmentation, river scenes often have unstructured boundaries and contain complex light and shadow on the water’s surface. According to the imaging mechanism of water pixels, this paper designed a water description feature based on a multi-block local binary pattern (MB-LBP) and Hue variance in HSI color space to detect the water region in the image. The improved Local Binary Pattern (LBP) feature was used to recognize the water region and the local texture descriptor in HSI color space using Hue variance was used to detect the shadow area of the river surface. Tested on two data sets including simple and complex river scenes, the proposed method has better segmentation performance and consumes less time than those of two other widely used methods. Full article

(This article belongs to the Special Issue Texture and Colour in Image Analysis)

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13 pages, 2474 KiB

Open AccessFeature PaperArticle

Partial Order Rank Features in Colour Space

and

Appl. Sci. 2020, 10(2), 499; https://doi.org/10.3390/app10020499 - 10 Jan 2020

Cited by 3 | Viewed by 2192

Abstract

Partial orders are the natural mathematical structure for comparing multivariate data that, like colours, lack a natural order. We introduce a novel, general approach to defining rank features in colour spaces based on partial orders, and show that it is possible to generalise existing rank based descriptors by replacing the order relation over intensity values by suitable partial orders in colour space. In particular, we extend a classical descriptor (the Texture Spectrum) to work with partial orders. The effectiveness of the generalised descriptor is demonstrated through a set of image classification experiments on 10 datasets of colour texture images. The results show that the partial-order version in colour space outperforms the grey-scale classic descriptor while maintaining the same number of features. Full article

(This article belongs to the Special Issue Texture and Colour in Image Analysis)

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13 pages, 2809 KiB

Open AccessArticle

Applications of Capacitive Imaging in Human Skin Texture and Hair Analysis

Christos Bontozoglou

and

Perry Xiao

Appl. Sci. 2020, 10(1), 256; https://doi.org/10.3390/app10010256 - 29 Dec 2019

Cited by 6 | Viewed by 3661

Abstract

This article focuses on the extraction of information from human skin and scalp hair for evaluation of a subject’s condition in the cosmetic and pharmaceutical industries. It uses capacitive images from existing hand-held research equipment and it applies image processing algorithms to expand their possible applications. The literature review introduces the readers into the field of skin research, and it highlights pieces of information that can be extracted by in vivo skin and ex vivo hair measurements. Then, the selected scientific equipment is presented, and Maxwell-based electrostatic simulations are employed to evaluate the measurement apparatus. Image analysis algorithms are suggested for (a) the detection of polygons on the human skin texture, (b) the estimation of wrinkles length and (c) the observation of hair water sorption capabilities by capacitive imaging systems. Finally, experiments are conducted to evaluate the performance of the presented algorithms and the results are compared with the literature. The results indicate that capacitive imaging systems can be used for skin age classification, detection and tracking of skin artifacts (e.g., wrinkles, moles or scars) and calculation of water content in hair samples. Full article

(This article belongs to the Special Issue Texture and Colour in Image Analysis)

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16 pages, 2252 KiB

Open AccessArticle

A Novel Bio-Inspired Method for Early Diagnosis of Breast Cancer through Mammographic Image Analysis

David González-Patiño

Yenny Villuendas-Rey

Amadeo-José Argüelles-Cruz

and

Fakhri Karray

Appl. Sci. 2019, 9(21), 4492; https://doi.org/10.3390/app9214492 - 23 Oct 2019

Cited by 8 | Viewed by 2337

Abstract

Breast cancer is a current problem that causes the death of many women. In this work, we test meta-heuristics applied to the segmentation of mammographic images. Traditionally, the application of these algorithms has a direct relationship with optimization problems; however, in this study, its implementation is oriented to the segmentation of mammograms using the Dunn index as an optimization function, and the grey levels to represent each individual. The update of grey levels during the process results in the maximization of the Dunn’s index function; the higher the index, the better the segmentation will be. The results showed a lower error rate using these meta-heuristics for segmentation compared to a well-adopted classical approach known as the Otsu method. Full article

(This article belongs to the Special Issue Texture and Colour in Image Analysis)

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14 pages, 8893 KiB

Open AccessArticle

An Improved MB-LBP Defect Recognition Approach for the Surface of Steel Plates

Yang Liu

Ke Xu

and

Jinwu Xu

Appl. Sci. 2019, 9(20), 4222; https://doi.org/10.3390/app9204222 - 10 Oct 2019

Cited by 31 | Viewed by 3342

Abstract

The detection of surface defects is very important for the quality improvement of steel plates. In actual production, as the steel plate production line runs faster, the steel surface defect detection algorithm is required to meet the requirements of real-time detection (less than 100 ms/image), and the detection accuracy is improved (at least 90%). In this paper, an improved multi-block local binary pattern (LBP) algorithm is proposed. This algorithm not only has the simplicity and efficiency of the LBP algorithm, but also finds a suitable scale to describe the defect features by changing the block sizes, thus ensuring high recognition accuracy. The experiment proves that the method satisfies the requirements of online real-time detection in terms of speed (63 ms/image), and surpasses the widely-used scale invariant feature transform (SIFT), speeded up robust features (SURF), gray-level co-occurrence matrix (GLCM), and LBP algorithms in recognition accuracy (94.30%), which prove that the MB-LBP has practical application value in an online real-time detection system. Full article

(This article belongs to the Special Issue Texture and Colour in Image Analysis)

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14 pages, 10814 KiB

Open AccessArticle

Texture Segmentation: An Objective Comparison between Five Traditional Algorithms and a Deep-Learning U-Net Architecture

Cefa Karabağ

Jo Verhoeven

Naomi Rachel Miller

and

Constantino Carlos Reyes-Aldasoro

Appl. Sci. 2019, 9(18), 3900; https://doi.org/10.3390/app9183900 - 17 Sep 2019

Cited by 16 | Viewed by 8082

Abstract

This paper compares a series of traditional and deep learning methodologies for the segmentation of textures. Six well-known texture composites first published by Randen and Husøy were used to compare traditional segmentation techniques (co-occurrence, filtering, local binary patterns, watershed, multiresolution sub-band filtering) against a deep-learning approach based on the U-Net architecture. For the latter, the effects of depth of the network, number of epochs and different optimisation algorithms were investigated. Overall, the best results were provided by the deep-learning approach. However, the best results were distributed within the parameters, and many configurations provided results well below the traditional techniques. Full article

(This article belongs to the Special Issue Texture and Colour in Image Analysis)

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16 pages, 1452 KiB

Open AccessArticle

A Bounded Scheduling Method for Adaptive Gradient Methods

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Appl. Sci. 2019, 9(17), 3569; https://doi.org/10.3390/app9173569 - 01 Sep 2019

Cited by 6 | Viewed by 2356

Abstract

Many adaptive gradient methods have been successfully applied to train deep neural networks, such as Adagrad, Adadelta, RMSprop and Adam. These methods perform local optimization with an element-wise scaling learning rate based on past gradients. Although these methods can achieve an advantageous training loss, some researchers have pointed out that their generalization capability tends to be poor as compared to stochastic gradient descent (SGD) in many applications. These methods obtain a rapid initial training process but fail to converge to an optimal solution due to the unstable and extreme learning rates. In this paper, we investigate the adaptive gradient methods and get the insights on various factors that may lead to poor performance of Adam. To overcome that, we propose a bounded scheduling algorithm for Adam, which can not only improve the generalization capability but also ensure the convergence. To validate our claims, we carry out a series of experiments on the image classification and the language modeling tasks on several standard benchmarks such as ResNet, DenseNet, SENet and LSTM on typical data sets such as CIFAR-10, CIFAR-100 and Penn Treebank. Experimental results show that our method can eliminate the generalization gap between Adam and SGD, meanwhile maintaining a relative high convergence rate during training. Full article

(This article belongs to the Special Issue Texture and Colour in Image Analysis)

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15 pages, 6139 KiB

Open AccessArticle

Intelligent Identification of Maceral Components of Coal Based on Image Segmentation and Classification

and

Appl. Sci. 2019, 9(16), 3245; https://doi.org/10.3390/app9163245 - 08 Aug 2019

Cited by 21 | Viewed by 3190

Abstract

An intelligent analytical technique which is able to accurately identify maceral components is highly desired in the fields of mining and geology. However, currently available methods based on fixed-size window neglect the shape information, and thus do not work in identifying maceral composition from one entire photomicrograph. To address these concerns, we propose a novel Maceral Identification strategy based on image Segmentation and Classification (MISC). Considering the complex and heterogeneous nature of coal, a two-level coarse-to-fine clustering method based on K-means is employed to divide microscopic images into a sequence of regions with similar attributes (i.e., binder, vitrinite, liptinite and inertinite). Furthermore, comprehensive features along with random forest are utilized to automatically classify binder and seven types of maceral components, including vitrinite, fusinite, semifusinite, cutinite, sporinite, inertodetrinite and micrinite. Evaluations on 39 microscopic images show that the proposed method achieves the state-of-the-art accuracy of 90.44% and serves as the baseline for future research on maceral analysis. In addition, to support the decisions of petrologists during maceral analysis, we developed a standalone software, which is freely available at https:/github.com/GuyooGu/MISC-Master. Full article

(This article belongs to the Special Issue Texture and Colour in Image Analysis)

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20 pages, 3188 KiB

Open AccessArticle

Color–Texture Pattern Classification Using Global–Local Feature Extraction, an SVM Classifier, with Bagging Ensemble Post-Processing

Carlos F. Navarro

and

Claudio A. Perez

Appl. Sci. 2019, 9(15), 3130; https://doi.org/10.3390/app9153130 - 01 Aug 2019

Cited by 11 | Viewed by 5094

Abstract

Many applications in image analysis require the accurate classification of complex patterns including both color and texture, e.g., in content image retrieval, biometrics, and the inspection of fabrics, wood, steel, ceramics, and fruits, among others. A new method for pattern classification using both color and texture information is proposed in this paper. The proposed method includes the following steps: division of each image into global and local samples, texture and color feature extraction from samples using a Haralick statistics and binary quaternion-moment-preserving method, a classification stage using support vector machine, and a final stage of post-processing employing a bagging ensemble. One of the main contributions of this method is the image partition, allowing image representation into global and local features. This partition captures most of the information present in the image for colored texture classification allowing improved results. The proposed method was tested on four databases extensively used in color–texture classification: the Brodatz, VisTex, Outex, and KTH-TIPS2b databases, yielding correct classification rates of 97.63%, 97.13%, 90.78%, and 92.90%, respectively. The use of the post-processing stage improved those results to 99.88%, 100%, 98.97%, and 95.75%, respectively. We compared our results to the best previously published results on the same databases finding significant improvements in all cases. Full article

(This article belongs to the Special Issue Texture and Colour in Image Analysis)

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16 pages, 9253 KiB

Open AccessArticle

Measurement of Period Length and Skew Angle Patterns of Textile Cutting Pieces Based on Faster R-CNN

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Appl. Sci. 2019, 9(15), 3026; https://doi.org/10.3390/app9153026 - 26 Jul 2019

Cited by 5 | Viewed by 2372

Abstract

The skew angle and period length of the multi-period pattern are two critical parameters for evaluating the quality of textile cutting pieces. In this paper, a new measurement method of the skew angle and period length is proposed based on Faster region convolutional neural network (R-CNN). First, a dataset containing approximately 5000 unique pattern images was established and annotated in the format of PASCAL VOC 2007. Second, the Faster R-CNN model was used to detect the pattern to determine the approximate location of the pattern (the position of the whole pattern). Third, precise position of the pattern (geometric center points of pattern) are processed based on the approximate position results using the automatic threshold segmentation method. Finally, the four-neighbor method was used to fill the missing center points to obtain a complete center point map, and the skew angle and period length can be measured by the detected center points. The experimental results show that the mean average position (mAP) of the pattern detection reached 84%, the average error of the proposed algorithm was less than 5% compared with the error of the manual measurement. Full article

(This article belongs to the Special Issue Texture and Colour in Image Analysis)

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10 pages, 5632 KiB

Open AccessArticle

Detection of Tampering by Image Resizing Using Local Tchebichef Moments

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Appl. Sci. 2019, 9(15), 3007; https://doi.org/10.3390/app9153007 - 26 Jul 2019

Cited by 6 | Viewed by 2198

Abstract

There are many image resizing techniques, which include scaling, scale-and-stretch, seam carving, and so on. They have their own advantages and are suitable for different application scenarios. Therefore, a universal detection of tampering by image resizing is more practical. By preliminary experiments, we found that no matter which image resizing technique is adopted, it will destroy local texture and spatial correlations among adjacent pixels to some extent. Due to the excellent performance of local Tchebichef moments (LTM) in texture classification, we are motivated to present a detection method of tampering by image resizing using LTM in this paper. The tampered images are obtained by removing the pixels from original images using image resizing (scaling, scale-and-stretch and seam carving). Firstly, the residual is obtained by image pre-processing. Then, the histogram features of LTM are extracted from the residual. Finally, an error-correcting output code strategy is adopted by ensemble learning, which turns a multi-class classification problem into binary classification sub-problems. Experimental results show that the proposed approach can obtain an acceptable detection accuracies for the three content-aware image re-targeting techniques. Full article

(This article belongs to the Special Issue Texture and Colour in Image Analysis)

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17 pages, 3823 KiB

Open AccessArticle

Quantitative Analysis of Benign and Malignant Tumors in Histopathology: Predicting Prostate Cancer Grading Using SVM

Subrata Bhattacharjee

and

Appl. Sci. 2019, 9(15), 2969; https://doi.org/10.3390/app9152969 - 24 Jul 2019

Cited by 22 | Viewed by 6927

Abstract

An adenocarcinoma is a type of malignant cancerous tissue that forms from a glandular structure in epithelial tissue. Analyzed stained microscopic biopsy images were used to perform image manipulation and extract significant features for support vector machine (SVM) classification, to predict the Gleason grading of prostate cancer (PCa) based on the morphological features of the cell nucleus and lumen. Histopathology biopsy tissue images were used and categorized into four Gleason grade groups, namely Grade 3, Grade 4, Grade 5, and benign. The first three grades are considered malignant. K-means and watershed algorithms were used for color-based segmentation and separation of overlapping cell nuclei, respectively. In total, 400 images, divided equally among the four groups, were collected for SVM classification. To classify the proposed morphological features, SVM classification based on binary learning was performed using linear and Gaussian classifiers. The prediction model yielded an accuracy of 88.7% for malignant vs. benign, 85.0% for Grade 3 vs. Grade 4, 5, and 92.5% for Grade 4 vs. Grade 5. The SVM, based on biopsy-derived image features, consistently and accurately classified the Gleason grading of prostate cancer. All results are comparatively better than those reported in the literature. Full article

(This article belongs to the Special Issue Texture and Colour in Image Analysis)

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14 pages, 5031 KiB

Open AccessArticle

Use of Texture Feature Maps for the Refinement of Information Derived from Digital Intraoral Radiographs of Lytic and Sclerotic Lesions

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Appl. Sci. 2019, 9(15), 2968; https://doi.org/10.3390/app9152968 - 24 Jul 2019

Cited by 5 | Viewed by 2905

Abstract

The aim of this study was to examine whether additional digital intraoral radiography (DIR) image preprocessing based on textural description methods improves the recognition and differentiation of periapical lesions. (1) DIR image analysis protocols incorporating clustering with the k-means approach (CLU), texture features derived from co-occurrence matrices, first-order features (FOF), gray-tone difference matrices, run-length matrices (RLM), and local binary patterns, were used to transform DIR images derived from 161 input images into textural feature maps. These maps were used to determine the capacity of the DIR representation technique to yield information about the shape of a structure, its pattern, and adequate tissue contrast. The effectiveness of the textural feature maps with regard to detection of lesions was revealed by two radiologists independently with consecutive interrater agreement. (2) High sensitivity and specificity in the recognition of radiological features of lytic lesions, i.e., radiodensity, border definition, and tissue contrast, was accomplished by CLU, FOF energy, and RLM. Detection of sclerotic lesions was refined with the use of RLM. FOF texture contributed substantially to the high sensitivity of diagnosis of sclerotic lesions. (3) Specific DIR texture-based methods markedly increased the sensitivity of the DIR technique. Therefore, application of textural feature mapping constitutes a promising diagnostic tool for improving recognition of dimension and possibly internal structure of the periapical lesions. Full article

(This article belongs to the Special Issue Texture and Colour in Image Analysis)

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14 pages, 5312 KiB

Open AccessArticle

Machine Vision System for Counting Small Metal Parts in Electro-Deposition Industry

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Appl. Sci. 2019, 9(12), 2418; https://doi.org/10.3390/app9122418 - 13 Jun 2019

Cited by 8 | Viewed by 3620

Abstract

In the fashion field, the use of electroplated small metal parts such as studs, clips and buckles is widespread. The plate is often made of precious metal, such as gold or platinum. Due to the high cost of these materials, it is strategically relevant and of primary importance for manufacturers to avoid any waste by depositing only the strictly necessary amount of material. To this aim, companies need to be aware of the overall number of items to be electroplated so that it is possible to properly set the parameters driving the galvanic process. Accordingly, the present paper describes a simple, yet effective machine vision-based method able to automatically count small metal parts arranged on a galvanic frame. The devised method, which relies on the definition of a rear projection-based acquisition system and on the development of image processing-based routines, is able to properly count the number of items on the galvanic frame. The system is implemented on a counting machine, which is meant to be adopted in the galvanic industrial practice to properly define a suitable set or working parameters (such as the current, voltage, and deposition time) for the electroplating machine and, thereby, assure the desired plate thickness from one side and avoid material waste on the other. Full article

(This article belongs to the Special Issue Texture and Colour in Image Analysis)

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Review

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32 pages, 1916 KiB

Open AccessReview

Recent Advances in Saliency Estimation for Omnidirectional Images, Image Groups, and Video Sequences

Marco Buzzelli

Appl. Sci. 2020, 10(15), 5143; https://doi.org/10.3390/app10155143 - 27 Jul 2020

Cited by 5 | Viewed by 2012

Abstract

We present a review of methods for automatic estimation of visual saliency: the perceptual property that makes specific elements in a scene stand out and grab the attention of the viewer. We focus on domains that are especially recent and relevant, as they make saliency estimation particularly useful and/or effective: omnidirectional images, image groups for co-saliency, and video sequences. For each domain, we perform a selection of recent methods, we highlight their commonalities and differences, and describe their unique approaches. We also report and analyze the datasets involved in the development of such methods, in order to reveal additional peculiarities of each domain, such as the representation used for the ground truth saliency information (scanpaths, saliency maps, or salient object regions). We define domain-specific evaluation measures, and provide quantitative comparisons on the basis of common datasets and evaluation criteria, highlighting the different impact of existing approaches on each domain. We conclude by synthesizing the emerging directions for research in the specialized literature, which include novel representations for omnidirectional images, inter- and intra- image saliency decomposition for co-saliency, and saliency shift for video saliency estimation. Full article

(This article belongs to the Special Issue Texture and Colour in Image Analysis)

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