Relevant bibliographies by topics / Local Ternary Pattern (LTP)

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Academic literature on the topic 'Local Ternary Pattern (LTP)'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 July 2025

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Journal articles on the topic "Local Ternary Pattern (LTP)"

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Narayanan, Vaasudev, and Bhargav Parsi. "Center Symmetric Local Descriptors for Image Classification." International Journal of Natural Computing Research 7, no. 4 (2018): 56–70. http://dx.doi.org/10.4018/ijncr.2018100104.

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Local feature description forms an integral part of texture classification, image recognition, and face recognition. In this paper, the authors propose Center Symmetric Local Ternary Mapped Patterns (CS-LTMP) and eXtended Center Symmetric Local Ternary Mapped Patterns (XCS-LTMP) for local description of images. They combine the strengths of Center Symmetric Local Ternary Pattern (CS-LTP) which uses ternary codes and Center Symmetric Local Mapped Pattern (CS-LMP) which captures the nuances between images to make the CS-LTMP. Similarly, the auhtors combined CS-LTP and eXtended Center Symmetric L
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Rassem, Taha H., and Bee Ee Khoo. "Completed Local Ternary Pattern for Rotation Invariant Texture Classification." Scientific World Journal 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/373254.

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Despite the fact that the two texture descriptors, the completed modeling of Local Binary Pattern (CLBP) and the Completed Local Binary Count (CLBC), have achieved a remarkable accuracy for invariant rotation texture classification, they inherit some Local Binary Pattern (LBP) drawbacks. The LBP is sensitive to noise, and different patterns of LBP may be classified into the same class that reduces its discriminating property. Although, the Local Ternary Pattern (LTP) is proposed to be more robust to noise than LBP, however, the latter’s weakness may appear with the LTP as well as with LBP. In
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Kumar B L, Sunil, Suchetha N V, and Sharmila Kumari M. "Enhanced Local Ternary Pattern method for Face Recognition." Journal of Scientific Research 66, no. 02 (2022): 139–43. http://dx.doi.org/10.37398/jsr.2022.660218.

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Biometrics is a term used to determine an individual's identification based on physiological or behavioral traits. Such physiological or behavioral characteristics differ from person to person. For this reason, it is more secure and popular to authenticate the person using biological characteristics than other conventional authentication methods. The Local Binary Pattern (LBP) face recognition system is widely used but is noise sensitive. For the purpose of improving the performance, a descriptor of a local texture called Local Ternary Pattern (LTP) is introduced, which is more discriminating
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Rabidas, Rinku, Abhishek Midya, Jayasree Chakraborty, and Wasim Arif. "Multi-Resolution Analysis of Edge-Texture Features for Mammographic Mass Classification." Journal of Circuits, Systems and Computers 29, no. 10 (2019): 2050156. http://dx.doi.org/10.1142/s021812662050156x.

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In this paper, multi-resolution analysis of two edge-texture based descriptors, Discriminative Robust Local Binary Pattern (DRlbp) and Discriminative Robust Local Ternary Pattern (DRltp), are proposed for the determination of mammographic masses as benign or malignant. As an extension of Local Binary Pattern (LBP) and Local Ternary Pattern (LTP), DRlbp and LTP-based features overcome the drawbacks of these features preserving the edge information along with texture. With the hypothesis that multi-resolution analysis of these features for different regions related to mammaographic masses with w
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Abbas, Faycel, Abdeljalil Gattal, and Rafik Menassel. "Local binary pattern and its derivatives to handwriting-based gender classification." Bulletin of Electrical Engineering and Informatics 12, no. 6 (2023): 3571–83. http://dx.doi.org/10.11591/eei.v12i6.5488.

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Several studies by psychologists and computer scientists have verified the link between handwriting and writer gender. The texture of the writing image is a major indicator of whether it is male or female writing. This paper conducts a comparison analysis to examine the effectiveness of various local binary patterns (LBPs) techniques in detecting gender from scanned images of handwriting. We study different LBP variants, including complete local binary pattern (CLBP), local ternary pattern (LTP), local configuration pattern (LCP), rotated local binary pattern (RLBP), local binary pattern varia
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Vinoja and Krishnaveni. "An Effective Face Recognition in Various Lighting Conditions Using LBP/LTP Techniques." Asian Journal of Computer Science and Technology 1, no. 1 (2012): 80–83. http://dx.doi.org/10.51983/ajcst-2012.1.1.1674.

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Making recognition is more reliable under uncontrolled lighting conditions is one of the most important challenges for practical face recognition systems. We tackle this by combining following methods. The first step is simple and efficient preprocessing chain. The preprocessing is used to avoid the unwanted illumination effects such as Non-uniform illumination, Shadowing & highlights, aliasing, blurring and noise. Second step includes local binary pattern (LBP) and local ternary pattern methods (LTP). LBP is possible to describe the texture and shape of a digital image. LTP is a generaliz
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Abrahim, Araz R., Mohd Sh Mohd Rahim, and Ahmed S. Sami. "Image Splicing Forgery Detection Scheme Using New Local Binary Pattern Varient." Academic Journal of Nawroz University 9, no. 3 (2020): 208. http://dx.doi.org/10.25007/ajnu.v9n3a780.

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In this research develop passive image splicing detection method based on a new descriptor called Adaptive Threshold Mean Ternary Pattern (ATMTP). It was developed based on strength and weaknesses of both Local Binary Pattern (LBP) and Local Ternary Pattern (LTP). ATMTP extraction feature is normally achieved by using proposed mean based thresholding and adaptive ternary thresholding, the former is robust to noise while the latter is robust to noise and other photometric attacks. It is designed to withstand against photometric manipulations, be it single or double attacks. In this research the
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Giveki, Davar, Mohammad Ali Soltanshahi, and Gholam Ali Montazer. "A new image feature descriptor for content based image retrieval using scale invariant feature transform and local derivative pattern." Optik - International Journal for Light and Electron Optics 131 (June 7, 2016): 242–54. https://doi.org/10.5281/zenodo.13998082.

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This paper presents a new methodology to retrieve images of different scenes by introducing a novel image descriptor.‎ The proposed descriptor works with Scale Invariant Feature Transform (SIFT), Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP), Local Derivative Pattern (LDP), Local Ternary Pattern (LTP) and any other feature descriptor that can be applied on the image pixels.‎ As the proposed descriptor considers a group of pixels together, higher level of semantic is achieved.‎ In this work, a new image descriptor using SIFT and LDP is introduced that is able to
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K. Naji, Saba, and Muthana H. Hamd. "HUMAN IDENTIFICATION BASED ON FACE RECOGNITION SYSTEM." Journal of Engineering and Sustainable Development 25, no. 01 (2021): 80–91. http://dx.doi.org/10.31272/jeasd.25.1.7.

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Due to, the great electronic development, which reinforced the need to define people's identities, different methods, and databases to identification people's identities have emerged. In this paper, we compare the results of two texture analysis methods: Local Binary Pattern (LBP) and Local Ternary Pattern (LTP). The comparison based on comparing the extracting facial texture features of 40 and 401 subjects taken from ORL and UFI databases respectively. As well, the comparison has taken in the account using three distance measurements such as; Manhattan Distance (MD), Euclidean Distance (ED),
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Onay, Funda Kutlu, and Cemal Köse. "Assessment of CSP-based two-stage channel selection approach and local transformation-based feature extraction for classification of motor imagery/movement EEG data." Biomedical Engineering / Biomedizinische Technik 64, no. 6 (2019): 643–53. http://dx.doi.org/10.1515/bmt-2018-0201.

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Abstract The main idea of brain-computer interfaces (BCIs) is to facilitate the lives of patients having difficulties to move their muscles due to a disorder of their motor nervous systems but healthy cognitive functions. BCIs are usually electroencephalography (EEG)-based, and the success of the BCIs relies on the precision of signal preprocessing, detection of distinctive features, usage of suitable classifiers and selection of effective channels. In this study, a two-stage channel selection and local transformation-based feature extraction are proposed for the classification of motor imager
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Dissertations / Theses on the topic "Local Ternary Pattern (LTP)"

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Hussain, Sibt Ul. "Apprentissage machine pour la détection des objets." Phd thesis, Université de Grenoble, 2011. http://tel.archives-ouvertes.fr/tel-00722632.

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Le but de cette thèse est de développer des méthodes pratiques plus performantes pour la détection d'instances de classes d'objets de la vie quotidienne dans les images. Nous présentons une famille de détecteurs qui incorporent trois types d'indices visuelles performantes - histogrammes de gradients orientés (Histograms of Oriented Gradients, HOG), motifs locaux binaires (Local Binary Patterns, LBP) et motifs locaux ternaires (Local Ternary Patterns, LTP) - dans des méthodes de discrimination efficaces de type machine à vecteur de support latent (Latent SVM), sous deux régimes de réduction de
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Naik, Narmada. "Real Time Face Recognition on GPU using OPENCL." Thesis, 2017. http://etd.iisc.ac.in/handle/2005/3596.

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Face recognition finds various applications in surveillance, Law enforcement etc. These applications require fast image processing in real time. Modern GPUs have evolved fully programmable parallel stream processors. The problem of face recognition in real time system is benefited by parallelism. With the aim of fulfilling both speed and accuracy criteria we present a GPU accelerated Face Recognition system. OpenCL is a heterogeneous computing language that allows extracting parallelism on different platforms like DSP processors, FPGAs, GPUs. The proposed kernel on GPU exploits coarse grain par
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Naik, Narmada. "Real Time Face Recognition on GPU using OPENCL." Thesis, 2017. http://etd.iisc.ernet.in/2005/3596.

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Face recognition finds various applications in surveillance, Law enforcement etc. These applications require fast image processing in real time. Modern GPUs have evolved fully programmable parallel stream processors. The problem of face recognition in real time system is benefited by parallelism. With the aim of fulfilling both speed and accuracy criteria we present a GPU accelerated Face Recognition system. OpenCL is a heterogeneous computing language that allows extracting parallelism on different platforms like DSP processors, FPGAs, GPUs. The proposed kernel on GPU exploits coarse grain par
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Fu, Jr-Chien, and 傅之謙. "Synchronized Rotation Local Ternary Pattern for Clothing Texture Categorization." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/2s8625.

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碩士<br>國立中央大學<br>資訊工程學系<br>105<br>Texture classification and recognition is an important topic in computer vision research area. In this work, we aim at studying classification of the evenly distributed geometrical pattern printed on the fabrics. This research is helpful to get the clothing information rapidly when performing image segmentation and making the subsequent clothing-style analysis more convenient. Traditional texture analysis features include Local Binary Patterns、Histogram of Oriented Gradients, and other transformation methods. Convolutional neural networks are popular methods in
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Wu, Szu-Han, and 伍思翰. "Gait Recognition Using Three-Patch Center-Symmetric Local Ternary Pattern." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/33062472428812528508.

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碩士<br>國立高雄大學<br>電機工程學系碩士班<br>104<br>Gait recognition is a newly merged biometrics which utilizes the manner of walking to recognize an individual. Compared with other biometrics, it is more difficult to disguise. In addition, gait can be captured in a distance by using low-resolution capturing devices. In this paper, we present a novel texture descriptor called patch-based center-symmetric local ternary pattern for feature extraction to recognize a person's identity of a gait image. The experimental results demonstrate that our proposed approach outperforms other gait recognition methods in bo
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Chen, Chung-Ming, and 陳宗明. "A Novel Local Ternary Pattern and Its Application to Face Recognition." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/26297141870909596010.

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碩士<br>中原大學<br>電子工程研究所<br>103<br>The operator of local binary pattern (LBP) mainly thresholds pixels in a predetermined window based on the gray value of the central pixel of that window. The LBP is quite sensitive to noise. To deal with this problem, a 3-valued operator called local ternary pattern (LTP) was proposed. The operator of extended local ternary pattern (ELTP) does not use a fixed threshold rather its threshold is determined by the local statistics of the pixels in the window. With this adaptive threshold, the noise-resistant capability is improved. In this thesis, we introduce a ne
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Chao, Fang-Yi, and 趙芳儀. "Moving Object Detection and Recognition using Local Ternary Pattern in Surveillance Video." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/43xc5h.

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碩士<br>國立臺灣大學<br>電信工程學研究所<br>105<br>We propose a method to distinguish moving people in surveillance video by moving object detection, skin detection to extract face area then do face recognition. Local Ternary Pattern (LTP), which is a derivative of Local Binary Pattern, is proven to be a strong yet simple texture descriptor because of its good properties on illumination robustness, rotation invariance and simplicity computation. It can be used in a wide variety of applications such as texture classification, moving object detection and face recognition. By integrating these applications which
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LIN, YUAN-RUEI, and 林沅睿. "Early stage gastric cancer detection in endoscopy NBI images by using local ternary pattern features and support vector machine." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/c2y4z8.

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碩士<br>國立雲林科技大學<br>電機工程系<br>107<br>In this thesis, we purpose a method to detect the early gastric cancer in the narrow band imaging (NBI) images of stomach. We determine the local ternary pattern (LTP) features of the image blocks and then design the classifier based on the support vector machine (SVM) to classify whether the image blocks belong to the normal or abnormal regions. First, we pre-process the original image to make the characteristic more visible. Second, we segment the pre-processed image into several blocks. Third, we compute LTP feature from each cell. Finally, we use an SVM mo
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Book chapters on the topic "Local Ternary Pattern (LTP)"

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Yuan, Jing-Hua, Hao-Dong Zhu, Yong Gan, and Li Shang. "Enhanced Local Ternary Pattern for Texture Classification." In Intelligent Computing Theory. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09333-8_48.

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Raghavendra, R. J., and R. Sanjeev Kunte. "Extended Local Ternary Pattern for Face Anti-spoofing." In Lecture Notes in Electrical Engineering. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3125-5_24.

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Zhang, Hu, Xianliang Wang, and Zhixiang He. "Finger Vein Recognition via Local Multilayer Ternary Pattern." In Biometric Recognition. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46654-5_30.

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Zhou, Peng, Yucong Peng, Jifeng Shen, Baochang Zhang, and Wankou Yang. "Local Dual-Cross Ternary Pattern for Feature Representation." In Biometric Recognition. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46654-5_66.

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Saurav, Sumeet, Sanjay Singh, Ravi Saini, and Madhulika Yadav. "Facial Expression Recognition Using Improved Adaptive Local Ternary Pattern." In Proceedings of 3rd International Conference on Computer Vision and Image Processing. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9291-8_4.

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Yee, Sam Yin, Taha H. Rassem, Mohammed Falah Mohammed, and Suryanti Awang. "Face Recognition Using Laplacian Completed Local Ternary Pattern (LapCLTP)." In Lecture Notes in Electrical Engineering. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-1289-6_29.

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Singhal, Amit, and Megha Agarwal. "Gaussian Local Ternary Co-occurrence Pattern for Image Retrieval." In Lecture Notes in Mechanical Engineering. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8025-3_1.

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Rakshit, Rinku Datta, Dakshina Ranjan Kisku, Massimo Tistarelli, and Phalguni Gupta. "Face Identification Using Local Ternary Tree Pattern Based Spatial Structural Components." In Pattern Recognition and Image Analysis. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31321-0_5.

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Kumar, Pullela S. V. V. S. R., D. J. Nagendra Kumar, Nakkella Madhuri, and Ayanavalli Ramadevi. "Local Ternary Pattern Alphabet Shape Features for Stone Texture Classification." In Lecture Notes in Electrical Engineering. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1906-8_44.

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Byun, Jin Young, and Jae Wook Jeon. "FPGA Based Face Detection Using Local Ternary Pattern Under Variant Illumination Condition." In Advances in Computer Science and Ubiquitous Computing. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-7605-3_60.

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Conference papers on the topic "Local Ternary Pattern (LTP)"

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Low, K. B., and U. U. Sheikh. "Gait recognition using Local Ternary Pattern (LTP)." In 2013 IEEE International Conference on Signal and Image Processing Applications (ICSIPA). IEEE, 2013. http://dx.doi.org/10.1109/icsipa.2013.6707997.

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Sitholimela, R., K. Madzima, S. Viriri, and M. Moyo. "Face Recognition using Two Local Ternary Patterns (LTP) Variants: A Performance Analysis using High-and Low-Resolution Images." In 2020 2nd International Multidisciplinary Information Technology and Engineering Conference (IMITEC). IEEE, 2020. http://dx.doi.org/10.1109/imitec50163.2020.9334098.

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Sadat, Rafi Md Najmus, Shyh Wei Teng, and Guojun Lu. "Image registration using modified Local Ternary Pattern." In 2010 25th International Conference of Image and Vision Computing New Zealand (IVCNZ). IEEE, 2010. http://dx.doi.org/10.1109/ivcnz.2010.6148858.

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Ren, Jianfeng, Xudong Jiang, and Junsong Yuan. "Relaxed local ternary pattern for face recognition." In 2013 20th IEEE International Conference on Image Processing (ICIP). IEEE, 2013. http://dx.doi.org/10.1109/icip.2013.6738759.

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Liao, Wen-Hung. "Region Description Using Extended Local Ternary Patterns." In 2010 20th International Conference on Pattern Recognition (ICPR). IEEE, 2010. http://dx.doi.org/10.1109/icpr.2010.251.

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Li, Jian, Hanyi Du, Yingru Liu, Kai Zhang, and Hui Zhou. "Extended Gradient Local Ternary Pattern for Vehicle Detection." In 2014 IEEE 17th International Conference on Computational Science and Engineering (CSE). IEEE, 2014. http://dx.doi.org/10.1109/cse.2014.345.

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Agarwal, Megha, and Amit Singhal. "Haar-like Local Ternary Pattern for Image Retrieval." In 2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS). IEEE, 2018. http://dx.doi.org/10.1109/iciinfs.2018.8721387.

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El khadiri, I., A. Chahi, Y. El-Merabet, Y. Ruichek, and R. Touahni. "Image classification with Local Directional Decoded Ternary Pattern." In 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT). IEEE, 2019. http://dx.doi.org/10.1109/codit.2019.8820373.

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Essa, Almabrok, and Vijayan Asari. "Multi-texture local ternary pattern for face recognition." In SPIE Defense + Security, edited by Mohammad S. Alam. SPIE, 2017. http://dx.doi.org/10.1117/12.2263735.

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Agarwal, Megha, and Amit Singhal. "DoG Based Local Ternary Pattern for Image Retrieval." In 2019 International Conference on Signal Processing and Communication (ICSC). IEEE, 2019. http://dx.doi.org/10.1109/icsc45622.2019.8938361.

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