Special Issue "Texture and Colour in Image Analysis"

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

Deadline for manuscript submissions: 31 January 2020.

Special Issue Editors

Prof. Francesco Bianconi
E-Mail Website
Guest Editor
Department of Engineering, Università degli Studi di Perugia, Italy
Tel. +39 075 5853706
Interests: computer vision; machine learning; image processing; colour; texture; biomedical image analysis; radiomics
Prof. Antonio Fernández
E-Mail Website
Guest Editor
Department of Engineering Design, Universidade de Vigo, Spain
Interests: image processing; machine learning; computer vision; texture analysis
Special Issues and Collections in MDPI journals
Prof. Raúl E. Sánchez-Yáñez
E-Mail Website
Guest Editor
Department of Electronics Engineering, Universidad de 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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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 (10 papers)

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Research

Open AccessArticle
An Improved MB-LBP Defect Recognition Approach for the Surface of Steel Plates
Appl. Sci. 2019, 9(20), 4222; https://doi.org/10.3390/app9204222 - 10 Oct 2019
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 [...] Read more.
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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Open AccessArticle
Texture Segmentation: An Objective Comparison between Five Traditional Algorithms and a Deep-Learning U-Net Architecture
Appl. Sci. 2019, 9(18), 3900; https://doi.org/10.3390/app9183900 - 17 Sep 2019
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 [...] Read more.
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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Open AccessArticle
A Bounded Scheduling Method for Adaptive Gradient Methods
Appl. Sci. 2019, 9(17), 3569; https://doi.org/10.3390/app9173569 - 01 Sep 2019
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 [...] Read more.
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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Open AccessArticle
Intelligent Identification of Maceral Components of Coal Based on Image Segmentation and Classification
Appl. Sci. 2019, 9(16), 3245; https://doi.org/10.3390/app9163245 - 08 Aug 2019
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 [...] Read more.
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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Open AccessArticle
Color–Texture Pattern Classification Using Global–Local Feature Extraction, an SVM Classifier, with Bagging Ensemble Post-Processing
Appl. Sci. 2019, 9(15), 3130; https://doi.org/10.3390/app9153130 - 01 Aug 2019
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 [...] Read more.
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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Open AccessArticle
Measurement of Period Length and Skew Angle Patterns of Textile Cutting Pieces Based on Faster R-CNN
Appl. Sci. 2019, 9(15), 3026; https://doi.org/10.3390/app9153026 - 26 Jul 2019
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 [...] Read more.
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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Open AccessArticle
Detection of Tampering by Image Resizing Using Local Tchebichef Moments
Appl. Sci. 2019, 9(15), 3007; https://doi.org/10.3390/app9153007 - 26 Jul 2019
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 [...] Read more.
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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Open AccessArticle
Quantitative Analysis of Benign and Malignant Tumors in Histopathology: Predicting Prostate Cancer Grading Using SVM
Appl. Sci. 2019, 9(15), 2969; https://doi.org/10.3390/app9152969 - 24 Jul 2019
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 [...] Read more.
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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Open AccessArticle
Use of Texture Feature Maps for the Refinement of Information Derived from Digital Intraoral Radiographs of Lytic and Sclerotic Lesions
Appl. Sci. 2019, 9(15), 2968; https://doi.org/10.3390/app9152968 - 24 Jul 2019
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 [...] Read more.
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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Open AccessArticle
Machine Vision System for Counting Small Metal Parts in Electro-Deposition Industry
Appl. Sci. 2019, 9(12), 2418; https://doi.org/10.3390/app9122418 - 13 Jun 2019
Cited by 1
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 [...] Read more.
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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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Planned Paper:

1. Title: Partial order rank features in colour space

    Authors: Fabrizio Smeraldi 1, Francesco Bianconi 2, Antonio Fernández 3 and Elena González 3

    Affiliation: 1. Queen Mary, University of London, United Kingdom
                    2. Università degli Studi di Perugia, Italy
                    3. Universidade de Vigo, Spain

2. Title: Face Liveness Detection by Pulse-based Features and Textural-based CNN   

    Author: Dr. Xiaobai Li

    Affiliation: Faculty of Information Technology and Electrical Engineering

 3. Title: Local Polar Spectral Texture Descriptors for Liver Cancer Tisue Classification

     Author: Jaromír Kukal, Zuzana Krbcová, Mares Jan

     Affiliation: Czech Technical University in Prague

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