Wood Type Classification Based on Hybrid Feature Integration with Optimized Bagging Ensemble Approach

This study aims to compare the results of four feature extraction models in the case of early recognition of disease attacks on cocoa fruits. The image extraction models used in this study are Local Binary Pattern (LBP), Gray Level Co-occurrence Matrix (GLCM), Hue Saturation Value (HSV), and Gray-level Co-occurrence Histograms (GLCH). In addition, the Support Vector Machine (SVM) model was used for the classification technique to evaluate the extraction results from the cocoa image dataset. The classification results using SVM showed the best performance on feature extraction HSV in all types of Kernel SVM used (Linear, RBF, and Polynomial), with the highest accuracy of 80.95% on RBF Kernel. Furthermore, the HSV performance in recognizing disease attacks on cocoa fruits, based on Precision, Recall, and F1-Score values, showed that, on average, HSV had a better value than other feature extraction methods.

With huge and ever-growing products in the factory, image retrieval can help the worker retrieve the same, or similar, existing products rapidly and accurately to guide production. In this paper, an effective method based on Fourier transform and local binary pattern is proposed to improve the retrieval efficiency of wool fabric. After capturing the fabric image, histogram equalization was implemented on the value of the Hue, Saturation, Value (HSV) mode to enhance the contrast. Subsequently, Fourier transform together with local binary pattern operator were performed to obtain the frequency spectrum and the local binary pattern, respectively. Each frequency spectrum was divided into 22 rings with the same width, and the standard deviation of the frequencies in each ring was calculated as a Fourier feature. Distinct output values of each local binary pattern were counted and normalized as local binary pattern features. Finally, Euclidean distance was adopted to measure the similarity based on the Fourier feature and local binary pattern feature. Twenty thousand wool fabric images were captured to demonstrate the efficacy of the proposed method. Experimental results indicate that the framework is effective and superior for image retrieval of wool fabric, providing referential assistance for the worker in the factory and improving retrieval efficiency.

Aim of study: This study was carried out to determine the surface hardness values of some varnishes applied to the surface of naturally aged wood material.
 Material and methods: Acrylic, polyurethane and water-based varnish was applied to the surface of the experimental samples prepared from naturally aged and fresh Scots pine (Pinus sylvestris L.), sessile oak (Quercus petrea L.) and chestnut (Castanea sativa M.) wood materials. A total of 360 test samples, 10 for each wood type, cutting direction, age period, and varnish type were prepared then the surface hardness values of these samples were determined.
 Main results: As a result of the experiments, the effect of the age period on the surface hardness of the varnish layer was found to be insignificant. The highest surface hardness value in wood material was obtained in the tangential section of oak wood and the difference between oak wood and chestnut wood was statistically insignificant. As a varnish type, the highest surface hardness value was obtained in polyurethane varnish, followed by acrylic varnish and water-based varnish, respectively. In addition, the surface hardness of the varnish layer in the tangential section of the wood material was found to be higher than that of the radial section.
 Highlights: In cases where high surface hardness is required in the varnish layer, polyurethane varnish can be preferred. As a result of the experiments, water-based varnish with a low surface hardness value will be able to give a working performance compatible with wood material with a hygroscopic structure, thanks to its high elasticity. For this reason, wood with a high amount of dimensional change can be used to protect the material against external influences.

Objectives: A novel video based fire detection algorithm based on rule base technique using RGB and HSV color space and spatial analysis based on wavelet analysis is proposed. Methods/Statistical Analysis: Rule base technique utilizing RGB and HSV color space for extraction of fire pixels in the frame was used. Threshold based spatial energy methodology is used for differentiating fire and fire like objects. Wavelet analysis is performed for calculation of spatial energy. Texture analysis using Local Binary Pattern (LBP) is also performed when fire or fire like candidate having spatial energy near to threshold of fire pixel. Findings: The usage of RGB color space alone for identification of fire in the video frames is not sufficient as they suffer from false detection. Two novel rules based on HSV plane is proposed which have improved the detection ability of system when compare to previous studies. But still suffers from false detection. Spatial energy methodology based on differentiating fire and fire like objects performs well and has achieved greater efficiency with low false detection rate of 4% on standard datasets. Texture analysis using Local Binary Pattern (LBP) is also performed in rare case when fire candidate is having spatial energy near to that of fire like object category. This has helped in reducing the computational complexity of the system. The system shows 100% accurate results. Improvements: The results obtained for different standard datasets using the proposed hybrid spatial and texture base analysis shows 100% accuracy with zero false positive and false negative rates which is not observed in any of the present articles. Keywords: Fire Detection, Local Binary Pattern (LBP), Spatial Analysis, Texture Analysis, Wavelet Analysis

In the Content-base animation Image Retrieval System, the Opponent Color Local Binary Pattern (OCLBP) algorithm is a typical multi-feature extraction solution, but it has the problems of high dimension of feature and complexity computational. For the defect of Opponent Color Local Binary Patterns, this dissertation presents a new Color-texture feature extraction method. By using the Local Binary Patterns operator in HSV color space, center pixel and neighborhood pixel color feature were extracted from three independent channels, and Local Binary Patterns texture feature was extracted at the same time. Synthesize the color feature vector for a one-dimensional vector, joint the color feature and Local Binary Patterns texture feature. Experiments show that the method is simple calculation and low dimension of animation image feature.

In this paper, we present a robust feature set to detect human hands in still images having simple as well as complex backgrounds. Our method relies on using a blend of existing and new shape-based, color-based and texture-based features. First, we identify the shortcomings of two existing features: Histograms of Oriented Gradient (HOG) and Color Name (CN). For HOG, we investigate the scenarios where the traditional block normalization schemes generate noisy results in near uniform regions in the image background and impede the accurate detection of human hands. We offer a more effective block normalization scheme for our new shape-based feature, αHOG, which results in considerably improved detection. Our new color-based feature, Clipped Color Name (CCN), caters for the noise induced color labels encountered in the CN feature, by modifying the probability assignment method for the basic colors in each pixel. For capturing the texture cues, we employ Local Binary Patterns (LBP) and Local Trinary Patterns (LTP). We compare the relative performance of the individual features in isolation and in different feature sets. For feature sets' comparison, the issue of high dimensional feature space generated as a result of feature fusion is addressed by using Partial Least Squares (PLS) for dimensionality reduction. Subsequently, we employ the non-linear Radial Basis Function Support Vector Machine (RBF SVM) classifier on PLS reduced feature sets. In our experiments, we use two different image datasets, namely the benchmark Cambridge Gesture Dataset (having simple backgrounds) and our own dataset (having a wider variety of complex backgrounds). Based on the experimental results, we find that out of the four feature sets we use, the feature set consisting of αHOG, CCN and LTP gives the best results in terms of the combined criteria of classification accuracy and computation time, and also offers improvement over the feature set proposed by Hussain and Triggs [1].

The local binary pattern (LBP) operator and its variants extract the textural information of an image by considering the neighboring pixel values. A single or joined histogram can be derived from the LBP code which can be used as an image feature descriptor in some applications. However, the LBP-based feature is not a good candidate in capturing the color information of an image, making it less suitable for measuring the similarity of color images with rich color information. To overcome this problem, we propose a fast and efficient indexing and image search system based on color and texture features. The color features are represented by combining 2D histogram and statistical moments, and texture features are represented by the local binary pattern (LBP). To assess and validate our results, many experiments were held in color space HSV. Detailed experimental analysis is carried out using precision and recall on colored Brodatz, KTH TIPS, Stex, and USPTex image databases. Experimental results show that the presented retrieval method yields about 8% better performance in precision versus recall and about 0.2 in average normalized modified retrieval rank (ANMRR) than the method using wavelet moments.

Copy-move forgery is a type of image tampering that involves copying a portion of an image and pasting it to another part of the same image with the intention of deceiving the viewer. In recent years, many approaches have been proposed to detect copy-move forgery, including those based on local binary patterns (LBP). In this paper, we perform a comprehensive evaluation of LBP-based methods for copy-move forgery detection using a dataset of 50 digital images. We compare the performance of four LBP-based methods, namely LBP, SIFT and SURF using metrics such as accuracy, precision, recall, and F1-score. Our results show that LBP outperforms the other methods in terms of accuracy and F1-score, while SIFT has the highest precision and recall. We also investigate the effect of various parameters, such as patch size and threshold values, on the performance of LBP. Our study provides valuable insights into the strengths and weaknesses of LBP-based methods for copy-move forgery detection, which can guide future research in this area. This study evaluates the performance of Local Binary Patterns (LBP) for detecting copy-move forgery in digital images. LBP is a widely used feature extraction technique in image processing and has been applied to various computer vision tasks, including forgery detection. The comparative study involves analyzing the accuracy, precision, recall, and F1-score of LBP and other popular forgery detection techniques, including SIFT and SURF, using a dataset of 50 digital images. The results show that LBP performs better than the other techniques, achieving an accuracy of 96.6%, precision of 94.0%, recall of 100%, and F1-score of 96.9%. This study provides useful insights for researchers and practitioners in the field of forgery detection, particularly for those interested in using LBP as a feature extraction technique.

Most of the image processing techniques are based on the image enhancement, but the dodgy problem in the enhancement of color images is to enhance images without changing their original hue. The present research aims to introduce one of the solutions to this problem. Based on the study of the characteristics of RGB color space, the HSV color space, and mathematical morphology image processing technique, it addresses the problem by extending grey-level image contrast enhancement algorithm to the color image in HSV color space. The experiment results indicate that this algorithm can keep the original hue and enhance the quality of images while maintaining the adaptive contrast enhancement in local parts.

Face detection system is challenging area of research in the field of security surveillance. Preprocessing of facial image data is very important part of face detection system. Now days various method of facial image data preprocessing are available, but all these method are not up to mark. Now we have a novel approach for preprocessing of face detection system based on HSV color space and integer wavelet packet transform method. And finally in this paper we have used LBP (local binary pattern) and SVM classification process are used.

Capsule endoscopy (CE) has been widely used as a new technology to diagnose gastrointestinal tract diseases, especially for small intestine. However, the large number of images in each test is a great burden for physicians. As such, computer aided detection (CAD) scheme is needed to relieve the workload of clinicians. In this paper, automatic differentiation of tumor CE image and normal CE image is investigated through comparative textural feature analysis. Four different color textures are studied in this work, i.e., texture spectrum histogram, color wavelet covariance, rotation invariant uniform local binary pattern and curvelet based local binary pattern. With support vector machine being the classifier, the discrimination ability of these four different color textures for tumor detection in CE images is extensively compared in RGB, Lab and HSI color space through ten-fold cross-validation experiments on our CE image data. It is found that HSI color space is the most suitable color space for all these texture based CAD systems. Moreover, the best performance achieved is 83.50% in terms of average accuracy, which is obtained by the scheme based on rotation invariant uniform local binary pattern.

Наведені результати обстеження насаджень сосни чорної австрійської в Кіровоградській, Харківській і Сумській областях у 2017 та 2018 рр. В умовах Лісостепу та Степу досліджено 4 деревостани віком 27–117 років, лісові культури 27-річного віку є потомством найстарішого з обстежених деревостанів. Оцінювання насаджень проведено за ростом, якістю стовбурів, станом, а також показниками репродукції. Для комплексного оцінювання деревостанів використано 3-балову шкалу. В усіх обстежених насадженнях сосна чорна поступається сосні звичайній за висотою, але у 2 із 5 випадків перевищує її за діаметром; характеризується стовбурами більшої якості та кращім станом. За результатами комплексного оцінювання сосна чорна австрійська набрала більшу або таку саму кількість балів, як і сосна звичайна. Підтверджено перспективність застосування сосни чорної для створення лісових і захисних насаджень на еродованих ґрунтах. У разі помірного рекреаційного навантаження вона спроможна виконувати меліоративні функції, а завдяки своїй високій декоративності придатна для садово-паркового будівництва.

This paper presents a stereoscopic imaging technique for fire detection. A low-cost binocular CCD camera is used to acquire the stereoscopic images of a controlled gas fire. Computer algorithms are developed to segment the fire zone in the HSV (Hue, Saturation, Value) color space and to determine the correspondence points of image pairs. The location, size and propagation of the fire are then determined from the perspective relationship between the fire and the camera. Experimental results under laboratory conditions show the effectiveness of the proposed approach for fire detection.

Opportunity research of using neural networks and computer vision to analyze images of skin lesion and identify features of various pathologies, including oncological neoplasms. A methodology has been developed that makes it possible to evaluate the significance of combinations of color components and spaces in feature extraction using local binary patterns (LBP) and histogram of oriented gradients (HOG) computer vision technologies to extract features of skin changes binary classification of human skin lesions. Optimization of extracted feature makes it possible to more effectively solve the problem of data separability in classification. Research reveals an accessible way to classify skin lesions on a small dataset (less than 1000 images). Research is supposed to be applied to data sequences obtained using a new unique method of multispectral processing of skin lesions. In the course of the work, data from the ISIC-19 and ISIC-20 datasets were used. Samples were formed with a limit of 1000 images for training and validating the models. Additionally, a test sample of 250 images was formed. All images were reduced to 128 × 128 pixels and converted to YCrCb, BGR, Grayscale, HSV color spaces. Features were extracted for each color channel using the HOG and LBP methods. Mathematical models, including neural networks have been used for data classification. The effectiveness of features combinations by color channels and feature extraction methods was evaluated. The preprocessed images were divided into training and validation subsets in a 70/30 ratio. The accuracy, recall, precision and f1-score metrics were used to evaluate the models. The models were evaluated using stratified cross-validation and a test dataset. Optimization of model parameters was carried out based on the loss function represented by the average of cross-validation and evaluation on the validation set. In the process of research, more than 15 000 different optimizations of model parameters were executed. The most stable results on the validation dataset were achieved using ensemble of models, which were trained on a combination of features using local binary patterns (LBP) and histogram of oriented gradients (HOG) technologies. Models which used only local binary patterns technology had the best metrics values, but these models are not recommended to be used in practice without ensemble with stronger models. The results gained can be applied for usage with an ensemble of state-of-the-art convolutional and recurrent neural networks. The proposed approach is universal and applicable both for the analysis of individual images of skin neoplasms and for the analysis of their sequences obtained by the method of multispectral image processing. The technique can be applied to datasets with a limited amount of data. The results obtained will be of interest to specialists in the fields of computer vision and medical images analysis.

A lot of methods are used to develop on image research. Image detection to relay back new information, widely used in various research field, such as health, agriculture or other field research. Various methods are used and developed to get better results. A combination of several methods is performed for testing as part of the research contribution. In this study will perform the combination results of the process color feature extraction with texture features. In color feature extraction using HSV color space method that gets 72 feature extraction and on texture feature extraction using local binary pattern that gets 256 feature extraction. The process of merging the two extracted results gets 328 new feature extractions. The result of combining color feature extraction and texture feature extraction is further classified. Results from image classification of butterflies get an accuracy score of 72%. The results obtained will be tested performance. The results obtained from performance testing get precision value, recall and f-measure respectively 76%, 72% and 74%