Article
Computer Science, Artificial Intelligence
Hoai Nam Vu, Mai Huong Nguyen, Cuong Pham
Summary: This paper proposes a method that combines deep learning and Local Binary Pattern features to recognize masked faces using RetinaFace as an efficient encoder. Experimental results show that this method outperforms other face recognition methods.
APPLIED INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Xin Shu, Hui Pan, Jinlong Shi, Xiaoning Song, Xiao-Jun Wu
Summary: This paper proposes a novel global refined local binary pattern (GRLBP) for texture feature extraction. By analyzing the nature of pixel intensity distribution in local neighborhoods, GRLBP can effectively describe and distinguish local neighborhoods with similar structures but different contrasts or grayscales. Experimental results demonstrate that GRLBP can represent detailed information of texture images and outperforms state-of-the-art LBP variants in terms of classification accuracy, feature dimension, and computational complexity.
PATTERN RECOGNITION
(2022)
Article
Computer Science, Artificial Intelligence
Qiwu Luo, Jiaojiao Su, Chunhua Yang, Olli Silven, Li Liu
Summary: In this paper, a novel image descriptor, called SNELBP, is proposed to address scale transformation and noise interference simultaneously. It achieves competitive results compared to classical LBP variants and typical deep learning methods.
PATTERN RECOGNITION
(2022)
Article
Computer Science, Artificial Intelligence
Nuh Alpaslan
Summary: This paper presents novel hybrid methods based on neutrosophic set and LBP features. By transforming the input image into a neutrosophic domain and combining with grayscale images, the proposed methods can extract more robust features. The methods contribute to the classification performance with reasonable computational cost and achieve satisfactory results in experiments.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Zeng Qiang, Adu Jianhua, Sun Xiaoya, Hong Sunyan
Summary: An extended complete LBP (ELBP) method is proposed for texture classification in this paper, which provides a detailed description and analysis of the composition of local feature vectors. Experimental results show that the algorithm has good scalability and robustness.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Instruments & Instrumentation
Qing An, Tintin Li, Mohammed Mehedi Hassan, Qian Deng, Vincent Drouard
Summary: In this article, an efficient description representation model with the local binary pattern (LBP) strategy was proposed and applied to infrared spectral searching and identification. The experimental results demonstrate the capability of this method in extracting both the local and holistic spectral features and its robustness to noise and baseline interferences.
INFRARED PHYSICS & TECHNOLOGY
(2023)
Article
Engineering, Electrical & Electronic
Xin Shu, Zhigang Song, Jinlong Shi, Shucheng Huang, Xiao-Jun Wu
Summary: The paper introduces a novel method, multiple channels local binary pattern (MCLBP), which combines single-channel texture characteristics with multi-channel color information for color texture representation and classification. Results from comprehensive experiments on benchmark databases show that this method outperforms most existing color texture features in terms of classification accuracy.
SIGNAL PROCESSING-IMAGE COMMUNICATION
(2021)
Article
Computer Science, Information Systems
Jinsheng Wei, Guanming Lu, Jingjie Yan, Huaming Liu
Summary: Micro-expression recognition is of great research value and presents significant challenges. In this paper, a new feature extraction method called LBP-FIP is proposed to address the extraction of dynamic texture features in oblique muscle movement direction in micro-expression videos. Experimental results on CASME II and SMIC databases demonstrate that LBP-FIP provides more effective information and discriminative features for micro-expression recognition compared to LBP-TOP, and it outperforms other LBP-based features, particularly on CASME II.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Safia Boudra, Itheri Yahiaoui, Ali Behloul
Summary: This paper introduces a novel approach for bark texture representation, named a set of statistical radial binary patterns (sSRBP), which achieves excellent performance in tree species identification. By utilizing statistical features in multi-scale neighborhoods, it can capture large bark structure information and enhance the texture representativeness and discriminative power.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Yijie Luo, Jiming Sa, Yuyan Song, He Jiang, Chi Zhang, Zhushanying Zhang
Summary: This paper proposes an improved LBP operator by using a local binary pattern operator based on magnitude ranking and a global threshold segmentation operator, to further improve the performance. This improved LBP achieves excellent texture classification accuracy across six common datasets, with an average of 1% lower than the best LBP variants. Meanwhile, the computational complexity of the proposed improved LBP is several times lower than that of the best LBP variants.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Safia Boudra, Itheri Yahiaoui, Ali Behloul
Summary: This paper proposes an automated plant classification method based on tree trunks, which describes and encodes the bark texture to recognize different bark species. By using the multi-scale Statistical Macro Binary Patterns (ms-SMBP) and ResNet34 model for feature description and learning, significant classification results are achieved.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Information Systems
Abdul Wahab Muzaffar, Farhan Riaz, Tarik Abuain, Waleed Abdel Karim Abu-Ain, Farhan Hussain, Muhammad Umar Farooq, Muhammad Ajmal Azad
Summary: In this study, a novel rotation and scale invariant texture classification method based on Gabor filters is proposed. These filters are designed to capture the visual content of the images by their impulse responses, which are sensitive to rotation and scaling. The proposed method rearranges the filter responses and calculates patterns after binarizing the responses based on a specific threshold. The effectiveness of the proposed feature extraction method is demonstrated through experiments on famous texture datasets, and it is shown to be more robust to noise compared to other state-of-the-art methods considered in the study.
Article
Computer Science, Information Systems
Gautam Kumar, Sambit Bakshi, Pankaj Kumar Sa, Banshidhar Majhi
Summary: This paper discusses an effective method for periocular recognition using iLBP and PIGP features, and experiments show that the selected histogram feature bins reduce the size of the feature vector and improve performance.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Joao B. Florindo, Konradin Metze
Summary: The proposed method combines cellular automata with local descriptors for texture recognition, achieving good results by introducing a new transition function and controlled deterministic chaos concept. The approach shows great potential in plant species texture recognition.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Ebrahim Al-wajih, Rozaida Ghazali
Summary: In recent years, deep learning approaches have been widely used in handwritten digit recognition. This paper proposes a new model called center-symmetric local binary convolutional neural networks (CS-LBCNN) based on center-symmetric local binary patterns (CS-LBPs). Compared to existing LBCNN models, the CS-LBCNN model achieves better accuracy and classification rate.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Jefferson G. Martins, Luiz E. S. Oliveira, Daniel Weingaertner, Andersson Barison, Gerlon A. R. Oliveira, Luciano M. Liao
Summary: Forests are being exploited disorderly and many species are endangered, prompting the need for a spatial distribution plan. Researchers facing a lack of representative databases can benefit from introducing new databases and proposing selection strategies to improve outcomes.
Article
Forestry
Joielan Xipaia dos Santos, Helena Cristina Vieira, Deivison Venicio Souza, Marlon Costa de Menezes, Graciela Ines Bolzon de Muniz, Patricia Soffiatti, Silvana Nisgoski
Summary: The integration of near infrared spectroscopy (NIR) with machine learning techniques is an effective method for discriminating wood species with commercial value. Analysis of Louros wood samples from the Brazilian Amazon using NIR and machine learning shows that discriminative patterns can be obtained from near infrared spectra across different anatomical sections, with excellent accuracy and F1-Scores obtained using the PLS-DA algorithm.
EUROPEAN JOURNAL OF WOOD AND WOOD PRODUCTS
(2021)
Article
Forestry
Thais A. P. Goncalves, Alexandre G. Navarro, Silvana Nisgoski, Julia Sonsin-Oliveira
Summary: Research conducted in the Encantado archaeological site in Maranhao state, Brazil, revealed that waterlogged wood pillars were mainly constructed using Tabebuia/Handroanthus trees, commonly known as ipe. By utilizing near-infrared spectroscopy (NIR) and principal component analysis (PCA), it was possible to differentiate the wood samples with approximately 80% variation between data, although the PCA did not separate them according to their types. Comparing the NIR spectra of wood pillars with recently sawn ipe wood helped to identify that the latter cannot be used to identify waterlogged wood.
WOOD SCIENCE AND TECHNOLOGY
(2021)
Article
Forestry
Rangel Consalter, Antonio Carlos Vargas Motta, Julierme Zimmer Barbosa, Fabiane Machado Vezzani, Rafael Alejandro Rubilar, Stephen A. Prior, Silvana Nisgoski, Marcos Vinicius Martins Bassaco
Summary: Research in southern Brazil found that mid-rotation application of N and P significantly increased commercial volume of Pinus taeda, while K, lime, and micronutrient applications had no noticeable effects. Nutrient and lime applications increased total litter accumulation, with K omission leading to an increase in total root mass.
EUROPEAN JOURNAL OF FOREST RESEARCH
(2021)
Article
Forestry
Silvana Nisgoski, Thais A. P. Goncalves, Julia Sonsin-Oliveira, Adriano W. Ballarin, Graciela I. B. Muniz
Summary: By analyzing near-infrared spectroscopy, different types of charcoal can be distinguished, aiding regulatory agencies in ensuring the sustainability of charcoal supply. The application of new technologies such as NIR for charcoal identification may improve the efficiency of government agents.
Article
Materials Science, Paper & Wood
Joielan Xipaia dos Santos, Helena Cristina Vieira, Tawani Lorena Naide, Deivison Venicio Souza, Graciela Ines Bolzon de Muniz, Patricia Soffiatti, Silvana Nisgoski
Summary: This study aimed to assess the potential of colorimetry in characterizing six species of the Fabaceae family, with P. suaveolens standing out in the Principal Component Analysis. The colorimetric parameters L*, b* and C* were found to be more important in the color results of the majority of wood samples, indicating the potential of colorimetry spectroscopy in characterizing Fabaceae species.
INTERNATIONAL WOOD PRODUCTS JOURNAL
(2021)
Article
Materials Science, Paper & Wood
Helena Cristina Vieira, Joielan Xipaia dos Santos, Deivison Venicio Souza, Polliana D'Angelo Rios, Graciela Ines Bolzon de Muniz, Simone Ribeiro Morrone, Silvana Nisgoski
Summary: This study evaluated the potential of colorimetry for differentiation of three species of the Fabaceae family in the Araucaria Forest in southern Brazil. The hue angle (h) showed the highest potential for species discrimination, while the a* and h parameters did not differ significantly among different wood samples.
MADERAS-CIENCIA Y TECNOLOGIA
(2022)
Article
Multidisciplinary Sciences
Joielan Xipaia dos Santos, Helena Cristina Vieira, Deivison Venicio Souza, Graciela Ines Bolzon De Muniz, Patricia Soffiatti, Silvana Nisgoski
Summary: The aim of this study was to verify the potential of colorimetric technique in identifying species marketed as tauari in the Brazilian Amazon. Parameters CIE L* a* b* were used to determine the color of wood samples from the State of Para and scientific collections. Results showed that parameter b* played a significant role in differentiating tauari samples based on color, especially in tangential and radial sections. PCA analysis revealed distinct color patterns between different sources of wood samples, with h and L* parameters providing valuable information for species identification. Therefore, colorimetric technique can be used as an auxiliary tool for wood identification, but it is recommended to be used in combination with anatomical description due to the complexity of species-level separation in the tauari group.
ANAIS DA ACADEMIA BRASILEIRA DE CIENCIAS
(2022)
Article
Forestry
Joielan-Xipaia Santos, Helena-Cristina Vieira, Deivison-Venicio Souza, Paulo-Afonso B. Costa, Graciela-Ines B. Muniz, Patricia Sofatti, Silvana Nisgoski
Summary: The study aimed to evaluate the potential of colorimetry for discriminating wood from the louros group of Brazilian native species. The results suggest that longitudinal surfaces are more suitable for characterizing this group.
Article
Materials Science, Paper & Wood
Elaine Cristina Lengowski, Eraldo Antonio Bonfatti Junior, Silvana Nisgoski, Graciela Ines Bolzon de Muniz, Umberto Klock
Summary: Thermal treatment significantly affects the anatomical structure, chemical composition, physical properties, mechanical properties, and color of teakwood, leading to improved thermal stability but decreased mechanical properties as well as a darker color.
MADERAS-CIENCIA Y TECNOLOGIA
(2021)
Article
Materials Science, Paper & Wood
Elaine Cristina Lengowski, Eraldo Antonio Bonfatti Junior, Rafael Dallo, Silvana Nisgoski, Jorge Luis Monteiro de Mattos, Jose Guilherme Prata
Summary: This study investigated the effects of adding nanocellulose to phenol-formaldehyde adhesive on the physico-mechanical properties of plywood panels, finding that NFC significantly influenced the adhesive properties, especially in mechanical tests.
MADERAS-CIENCIA Y TECNOLOGIA
(2021)
Article
Ecology
Angela Maria Stupp, Helena Cristina Vieira, Polliana D'Angelo Rios, Graciela Ines Bolzon de Muniz, Silvana Nisgoski
Summary: This study aimed to measure and compare anatomical elements of wood and charcoal of three species to support identification of seized materials. Changes in cell dimensions and behavior were observed after carbonization in the Fabaceae species evaluated.
Article
Materials Science, Paper & Wood
Helena Cristina Vieira, Joielan Xipaia dos Santos, Eliane Lopes da Silva, Polliana D'Angelo Rios, Graciela Ines Bolzon de Muniz, Simone Ribeiro Morrone, Silvana Nisgoski
Summary: This study aimed to evaluate the potential of near-infrared spectroscopy for discrimination of wood and charcoal from four Myrtaceae species native of the Araucaria Forest. Results showed that it was possible to separate different species based on anatomical sections and position in trunk for wood, but some more sample dispersion were observed for charcoal. Linear discriminant analysis corroborated the results.
WOOD MATERIAL SCIENCE & ENGINEERING
(2021)
Article
Forestry
Catia Nara Tobaldini Frizon, Silvana Nisgoski
FLORESTA E AMBIENTE
(2020)
Article
Multidisciplinary Sciences
Silvana Nisgoski, Helena Cristina Vieira, Thais Alves Pereira Goncalves, Claudio Manuel Afonso, Graciela Ines Bolzon de Muniz
SN APPLIED SCIENCES
(2020)