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Автор: Grafiati
Опубліковано: 11 вересня 2023

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Статті в журналах з теми "SVM AND GABOR FILTER"

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AMIN, M. ASHRAFUL, and HONG YAN. "AN EMPIRICAL STUDY ON THE CHARACTERISTICS OF GABOR REPRESENTATIONS FOR FACE RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 23, no. 03 (May 2009): 401–31. http://dx.doi.org/10.1142/s0218001409007181.

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This paper examines the classification capability of different Gabor representations for human face recognition. Usually, Gabor filter responses for eight orientations and five scales for each orientation are calculated and all 40 basic feature vectors are concatenated to assemble the Gabor feature vector. This work explores 70 different Gabor feature vector extraction techniques for face recognition. The main goal is to determine the characteristics of the 40 basic Gabor feature vectors and to devise a faster Gabor feature extraction method. Among all the 40 basic Gabor feature representations the filter responses acquired from the largest scale at smallest relative orientation change (with respect to face) shows the highest discriminating ability for face recognition while classification is performed using three classification methods: probabilistic neural networks (PNN), support vector machines (SVM) and decision trees (DT). A 40 times faster summation based Gabor representation shows about 98% recognition rate while classification is performed using SVM. In this representation all 40 basic Gabor feature vectors are summed to form the summation based Gabor feature vector. In the experiment, a sixth order data tensor containing the basic Gabor feature vectors is constructed, for all the operations.
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Habibullah, Muhamad, Hisyam Fahmi, and Erna Herawati. "Penerapan Metode Segmentasi Gabor Filter Dan Algoritma Support Vector Machine Untuk Pendeteksian Penyakit Daun Tomat." Jurnal Riset Mahasiswa Matematika 2, no. 6 (September 1, 2023): 221–32. http://dx.doi.org/10.18860/jrmm.v2i6.22023.

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This research discusses about processing a formulation that we can give to diseased tomato leaves. Gabor Filter is a method used to detect textures using frequency and orientation parameters. The Support Vector Machine (SVM) algorithm is an algorithm that can be used classifying tomato leaf diseases. The purpose of this research is to determine the accuracy of the Gabor Filter segmentation and the Support Vector Machine Algorithm for detecting tomato leaf disease to facilitate farmers in analyzing diseases on tomato leaves. The input will go through pre-processing of RGB pixels to Greyscale ones before being processed using Gabor Filter. This Gabor Filter process segments the image to produce a magnitude value. The results of the image magnitude values here will be seen and will enter the classification process using SVM. The SVM algorithm aims to find the best hyperlane on tomato leaves that have been segmented to separate classes in the input space. The application of the SVM method with class classification of tomato leaves by calculating the energy value and entropy of the extraction results, assisted by 12 features, namely: CiriR, Feature G, FeatureB, Standard DeviationR, Standard DeviationG, Standard DeviationB, SkewnessR, SkewnessG, SkewnessB, Mean, Energy, Entropy are used to the simplity classification process with a high degree of accuracy. The process of classification of tomato leaf disease with test data of 600 images managed to get an accuracy value of 74.1667%. In order to facilitate the performance of farmers in predicting tomato leaf disease.
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Jain, Manali, and Amit Sinha. "Classification of Satellite Images through Gabor Filter using SVM." International Journal of Computer Applications 116, no. 7 (April 22, 2015): 18–21. http://dx.doi.org/10.5120/20348-2534.

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Ding, Shu Min, Chun Lei Li, and Zhou Feng Liu. "Fabric Defect Detection Scheme Based on Gabor Filter and PCA." Advanced Materials Research 482-484 (February 2012): 159–63. http://dx.doi.org/10.4028/www.scientific.net/amr.482-484.159.

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Gabor feature is one of the features which have been used for texture classification. In this paper, we propose a novel fabric detect detection scheme based on Gabor filter and PCA. The fabric image is split into image blocks, and then different Gabor filter banks are applied into each image blocks. A feature vector is generated by concatenating all the Gabor features with different directions and scales for each image block. Principal component analysis (PCA) is adopted to reduce the dimension of the Gabor feature vector. In the end, SVM can classify each image block as non-defective and defective. Experimental results demonstrate the efficiency of our proposed algorithm. Because of its simplicity, online implementation is possible as well.
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SUMAN, S. BHUJBAL, and R. PATIL SHUBHANGI. "IMAGE SEARCH BY COMPARING GABOR FILTER WITH SVM AND SIFT." i-manager's Journal on Information Technology 7, no. 3 (2018): 10. http://dx.doi.org/10.26634/jit.7.3.14403.

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Yan, Jianqiang, Jie Li, and Xinbo Gao. "Chinese text location under complex background using Gabor filter and SVM." Neurocomputing 74, no. 17 (October 2011): 2998–3008. http://dx.doi.org/10.1016/j.neucom.2011.04.031.

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Lahmiri, Salim, and Mounir Boukadoum. "Hybrid Discrete Wavelet Transform and Gabor Filter Banks Processing for Features Extraction from Biomedical Images." Journal of Medical Engineering 2013 (April 15, 2013): 1–13. http://dx.doi.org/10.1155/2013/104684.

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A new methodology for automatic feature extraction from biomedical images and subsequent classification is presented. The approach exploits the spatial orientation of high-frequency textural features of the processed image as determined by a two-step process. First, the two-dimensional discrete wavelet transform (DWT) is applied to obtain the HH high-frequency subband image. Then, a Gabor filter bank is applied to the latter at different frequencies and spatial orientations to obtain new Gabor-filtered image whose entropy and uniformity are computed. Finally, the obtained statistics are fed to a support vector machine (SVM) binary classifier. The approach was validated on mammograms, retina, and brain magnetic resonance (MR) images. The obtained classification accuracies show better performance in comparison to common approaches that use only the DWT or Gabor filter banks for feature extraction.
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Gao, Xiaojing, Heru Xue, Xin Pan, Xinhua Jiang, Yanqing Zhou, and Xiaoling Luo. "Somatic Cells Recognition by Application of Gabor Feature-Based (2D)2PCA." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 12 (September 17, 2017): 1757009. http://dx.doi.org/10.1142/s0218001417570099.

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In this paper, we propose a novel approach of Gabor feature based on bi-directional two-dimensional principal component analysis ((2D)2PCA) for somatic cells recognition. Firstly, Gabor features of different orientations and scales are extracted by the convolution of Gabor filter bank. Secondly, dimensionality reduction of the feature space applies (2D)2PCA in both row and column. Finally, the classifier uses Support Vector Machine (SVM) to achieve our goal. The experimental results are obtained using a large set of images from different sources. The results of our proposed method are not only efficient in accuracy and speed, but also robust to illumination in bovine mastitis via optical microscopy.
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Im, Sang mi, Hye yeon Cho, and TaeYong Kim. "Age Estimation based on Facial Wrinkles by using the Gabor filter and SVM." TECHART: Journal of Arts and Imaging Science 3, no. 4 (November 30, 2016): 24. http://dx.doi.org/10.15323/techart.2016.11.3.4.24.

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Muchtar, Mutmainnah, and Laili Cahyani. "Klasifikasi Citra Daun dengan Metode Gabor Co-Occurence." Jurnal ULTIMA Computing 7, no. 2 (August 1, 2016): 39–47. http://dx.doi.org/10.31937/sk.v7i2.231.

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Plant takes a crucial part in mankind existences. The development of digital image processing technique made the plant classification task become a lot of easier. Leaf is a part of plant that can be used for plant classification where texture of the leaf is a common feature that been used for classification process. Texture offers a unique feature and able to work even when the leaf is damaged or overly big in size which sometimes made the acquisition process become more difficult. This study offers a combination of Gabor filter methods and co-occurrence matrices to produce the most representative features for leaf classification. Classification using SVM with 5-fold cross validation system shows that the proposed Gabor Co-Occurence methods was able to reach average accuracy up to 89.83%. Terms: Leaf, Gabor Co-occurence, Support Vector Machine, Texture
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Дисертації з теми "SVM AND GABOR FILTER"

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Shrestha, Ujjwal. "Automatic Liver and Tumor Segmentation from CT Scan Images using Gabor Feature and Machine Learning Algorithms." University of Toledo / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1522411364001198.

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Gasslander, Maja. "Segmentation of Clouds in Satellite Images." Thesis, Linköpings universitet, Datorseende, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-128802.

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The usage of 3D modelling is increasing fast, both for civilian and military areas, such as navigation, targeting and urban planning. When creating a 3D model from satellite images, clouds canbe problematic. Thus, automatic detection ofclouds inthe imagesis ofgreat use. This master thesis was carried out at Vricon, who produces 3D models of the earth from satellite images.This thesis aimed to investigate if Support Vector Machines could classify pixels into cloud or non-cloud, with a combination of texture and color as features. To solve the stated goal, the task was divided into several subproblems, where the first part was to extract features from the images. Then the images were preprocessed before fed to the classifier. After that, the classifier was trained, and finally evaluated.The two methods that gave the best results in this thesis had approximately 95 % correctly classified pixels. This result is better than the existing cloud segmentation method at Vricon, for the tested terrain and cloud types.
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Jamborová, Soňa. "Segmentace obrazu pomocí neuronové sítě." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2011. http://www.nusl.cz/ntk/nusl-236925.

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This work is about suggestion of the software for neural network based image segmentation. It defines basic terms for this topics. It is focusing mainly at preperation imaging information for image segmentation using neural network. It describes and compares different aproaches for image segmentation.
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önder, gül, and aydın kayacık. "Multiview Face Detection Using Gabor Filter and Support Vector Machines." Thesis, Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-2152.

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Face detection is a preprocessing step for face recognition algorithms. It is the localization of face/faces in an image or image sequence. Once the face(s) are localized, other computer vision algorithms such as face recognition, image compression, camera auto focusing etc are

applied. Because of the multiple usage areas, there are many research efforts in face processing. Face detection is a challenging computer vision problem because of lighting conditions, a high degree of variability in size, shape, background, color, etc. To build fully

automated systems, robust and efficient face detection algorithms are required.

Numerous techniques have been developed to detect faces in a single image; in this project we have used a classification-based face detection method using Gabor filter features. We have designed five frequencies corresponding to eight orientations channels for extracting facial features from local images. The feature vector based on Gabor filter is used as the input of the face/non-face classifier, which is a Support Vector Machine (SVM) on a reduced feature

subspace extracted by using principal component analysis (PCA).

Experimental results show promising performance especially on single face images where 78% accuracy is achieved with 0 false acceptances.

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Almeida, Osvaldo Cesar Pinheiro de. "Técnicas de processamento de imagens para localização e reconhecimento de faces." Universidade de São Paulo, 2006. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-22012007-160023/.

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A biometria é a ciência que estuda a mensuração dos seres vivos. Muitos trabalhos exploram as características dos seres humanos tais como, impressão digital, íris e face, a fim de desenvolver sistemas biométricos, utilizados em diversas aplicações (monitoramento de segurança, computação ubíqua, robótica). O reconhecimento de faces é uma das técnicas biométricas mais investigadas, por ser bastante intuitiva e menos invasiva que as demais. Alguns trabalhos envolvendo essa técnica se preocupam apenas em localizar a face de um indivíduo (fazer a contagem de pessoas), enquanto outros tentam identificá-lo a partir de uma imagem. Este trabalho propõe uma abordagem capaz de identificar faces a partir de quadros de vídeo e, posteriormente, reconhecê-las por meio de técnicas de análise de imagens. Pode-se dividir o trabalho em dois módulos principais: (1) - Localização e rastreamento de faces em uma seqüência de imagens ( frames), além de separar a região rastreada da imagem; (2) - Reconhecimento de faces, identificando a qual pessoa pertence. Para a primeira etapa foi implementado um sistema de análise de movimento (baseado em subtração de quadros) que possibilitou localizar, rastrear e captar imagens da face de um indivíduo usando uma câmera de vídeo. Para a segunda etapa foram implementados os módulos de redução de informações (técnica Principal Component Analysis - PCA), de extração de características (transformada wavelet de Gabor), e o de classificação e identificação de face (distância Euclidiana e Support Vector Machine - SVM). Utilizando-se duas bases de dados de faces (FERET e uma própria - Própria), foram realizados testes para avaliar o sistema de reconhecimento implementado. Os resultados encontrados foram satisfatórios, atingindo 91,92% e 100,00% de taxa de acertos para as bases FERET e Própria, respectivamente.
Biometry is the science of measuring and analyzing biomedical data. Many works in this field have explored the characteristics of human beings, such as digital fingerprints, iris, and face to develop biometric systems, employed in various aplications (security monitoring, ubiquitous computation, robotic). Face identification and recognition are very apealing biometric techniques, as it it intuitive and less invasive than others. Many works in this field are only concerned with locating the face of an individual (for counting purposes), while others try to identify people from faces. The objective of this work is to develop a biometric system that could identify and recognize faces. The work can be divided into two major stages: (1) Locate and track in a sequence of images (frames), as well as separating the tracked region from the image; (2) Recognize a face as belonging to a certain individual. In the former, faces are captured from frames of a video camera by a motion analysis system (based on substraction of frames), capable of finding, tracking and croping faces from images of individuals. The later, consists of elements for data reductions (Principal Component Analysis - PCA), feature extraction (Gabor wavelets) and face classification (Euclidean distance and Support Vector Machine - SVM). Two faces databases have been used: FERET and a \"home-made\" one. Tests have been undertaken so as to assess the system\'s recognition capabilities. The experiments have shown that the technique exhibited a satisfactory performance, with success rates of 91.97% and 100% for the FERET and the \"home-made\" databases, respectively.
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Kiernan, Mary. "Implementation and design of the discrete Gabor filter for sonar texture classification." Thesis, Heriot-Watt University, 1995. http://hdl.handle.net/10399/766.

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Konuk, Baris. "Palmprint Recognition Based On 2-d Gabor Filters." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12608138/index.pdf.

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In this thesis work, a detailed analysis of biometric technologies has been done and a new palmprint recognition algorithm has been implemented. The proposed algorithm is based on 2-D Gabor filters. The developed algorithm is first tested on The Hong Kong Polytechnic University Palmprint Database in terms of accuracy, speed and template size. Then a scanner is integrated into the developed algorithm in order to acquire palm images
in this way an online palmprint recognition system has been developed. Then a small palmprint database is formed via this system in Middle East Technical University. Results on this new database have also shown the success of the developed algorithm.
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Davis, Craig Alton Denney Thomas Stewart. "Applications of multi-channel filter banks to textured image segmentation." Auburn, Ala., 2006. http://repo.lib.auburn.edu/2006%20Summer/Theses/DAVIS_CRAIG_12.pdf.

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Ravikumar, Rahul. "Multi-scale texture analysis of remote sensing images using gabor filter banks and wavelet transforms." Thesis, [College Station, Tex. : Texas A&M University, 2008. http://hdl.handle.net/1969.1/ETD-TAMU-3175.

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Mar, Nang Seng Siri. "Vision-based classification of solder joint defects." Thesis, Queensland University of Technology, 2010. https://eprints.qut.edu.au/37273/1/Nang_Mar_Thesis.pdf.

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Inspection of solder joints has been a critical process in the electronic manufacturing industry to reduce manufacturing cost, improve yield, and ensure product quality and reliability. The solder joint inspection problem is more challenging than many other visual inspections because of the variability in the appearance of solder joints. Although many research works and various techniques have been developed to classify defect in solder joints, these methods have complex systems of illumination for image acquisition and complicated classification algorithms. An important stage of the analysis is to select the right method for the classification. Better inspection technologies are needed to fill the gap between available inspection capabilities and industry systems. This dissertation aims to provide a solution that can overcome some of the limitations of current inspection techniques. This research proposes two inspection steps for automatic solder joint classification system. The “front-end” inspection system includes illumination normalisation, localization and segmentation. The illumination normalisation approach can effectively and efficiently eliminate the effect of uneven illumination while keeping the properties of the processed image. The “back-end” inspection involves the classification of solder joints by using Log Gabor filter and classifier fusion. Five different levels of solder quality with respect to the amount of solder paste have been defined. Log Gabor filter has been demonstrated to achieve high recognition rates and is resistant to misalignment. Further testing demonstrates the advantage of Log Gabor filter over both Discrete Wavelet Transform and Discrete Cosine Transform. Classifier score fusion is analysed for improving recognition rate. Experimental results demonstrate that the proposed system improves performance and robustness in terms of classification rates. This proposed system does not need any special illumination system, and the images are acquired by an ordinary digital camera. In fact, the choice of suitable features allows one to overcome the problem given by the use of non complex illumination systems. The new system proposed in this research can be incorporated in the development of an automated non-contact, non-destructive and low cost solder joint quality inspection system.
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Частини книг з теми "SVM AND GABOR FILTER"

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Sabri, Mahdi, and Paul Fieguth. "A New Gabor Filter Based Kernel for Texture Classification with SVM." In Lecture Notes in Computer Science, 314–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30126-4_39.

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Kavya Ashwini, A. K., R. Madhumitha, Mary Ann Sandra, S. Supriya, Ullal Akshatha Nayak, and K. Ranjitha. "Identical Twin Face Recognition Using Gabor Filter, SVM Classifier and SURF Algorithm." In Emerging Research in Computing, Information, Communication and Applications, 11–24. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1342-5_2.

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Vijaya Madhavi, Mantragar, and T. Christy Bobby. "Gabor Filter Based Classification of Mammography Images Using LS-SVM and Random Forest Classifier." In Communications in Computer and Information Science, 69–83. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9184-2_6.

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Bölcskei, Helmut, and Franz Hlawatsch. "Oversampled modulated filter banks." In Gabor Analysis and Algorithms, 295–322. Boston, MA: Birkhäuser Boston, 1998. http://dx.doi.org/10.1007/978-1-4612-2016-9_10.

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Ghandehari, Azadeh, and Reza Safabakhsh. "Palmprint Verification Using Circular Gabor Filter." In Advances in Biometrics, 675–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01793-3_69.

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Zhu, En, Jianping Yin, and Guomin Zhang. "Fingerprint Enhancement Using Circular Gabor Filter." In Lecture Notes in Computer Science, 750–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30126-4_91.

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Nazarkevych, Mariya, Mykola Logoyda, Oksana Troyan, Yaroslav Vozniy, and Zoreslava Shpak. "The Ateb-Gabor Filter for Fingerprinting." In Advances in Intelligent Systems and Computing IV, 247–55. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33695-0_18.

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Nikam, Shankar Bhausaheb, and Suneeta Agarwal. "Gabor Filter-Based Fingerprint Anti-spoofing." In Advanced Concepts for Intelligent Vision Systems, 1103–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-88458-3_100.

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Zhang, Hong, Zhi Liu, Qijun Zhao, Congcong Zhang, and Dandan Fan. "Finger Vein Recognition Based on Gabor Filter." In Lecture Notes in Computer Science, 827–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-42057-3_104.

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Liu, Shuai, Yuanning Liu, Xiaodong Zhu, Guang Huo, Jingwei Cui, and Yihao Chen. "Iris Recognition Based on Adaptive Gabor Filter." In Biometric Recognition, 383–90. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69923-3_41.

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Тези доповідей конференцій з теми "SVM AND GABOR FILTER"

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Laxmi, Vijaya, and Parvataneni Sudhakara Rao. "Eye detection using Gabor Filter and SVM." In 2012 12th International Conference on Intelligent Systems Design and Applications (ISDA 2012). IEEE, 2012. http://dx.doi.org/10.1109/isda.2012.6416654.

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Feng, Jun, and Yanhai Zhu. "Text independent writer identification based on Gabor filter and SVM classifier." In Sixth International Symposium on Instrumentation and Control Technology: Signal Analysis, Measurement Theory, Photo-Electronic technology, and Artificial Intelligence, edited by Jiancheng Fang and Zhongyu Wang. SPIE, 2006. http://dx.doi.org/10.1117/12.716914.

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Kumar, Atul, Yen-Yu Wang, Kai-Che Liu, I-Chen Tsai, Ching-Chun Huang, and Nguyen Hung. "Distinguishing normal and pulmonary edema chest x-ray using Gabor filter and SVM." In 2014 International Symposium on Bioelectronics and Bioinformatics (ISBB). IEEE, 2014. http://dx.doi.org/10.1109/isbb.2014.6820918.

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Fan, Yanfeng, and Hongmei Zhang. "Application of Gabor Filter and Multi-class SVM in Baking Bread Quality Classification." In 2006 International Conference on Mechatronics and Automation. IEEE, 2006. http://dx.doi.org/10.1109/icma.2006.257396.

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Bourgeat, Pierrick T., Fabrice Meriaudeau, Patrick Gorria, and Kenneth W. Tobin. "Gabor filters and SVM classifier for pattern wafer segmentation." In Optics East, edited by Frederic Truchetet and Olivier Laligant. SPIE, 2004. http://dx.doi.org/10.1117/12.581242.

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Bin Makhashen, G. M., H. A. Luqman, and E. S. M. El-Alfy. "Using Gabor Filter Bank with Downsampling and SVM for Visual Sign Language Alphabet Recognition." In 2nd Smart Cities Symposium (SCS 2019). Institution of Engineering and Technology, 2019. http://dx.doi.org/10.1049/cp.2019.0188.

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Bojarczak, Piotr, and Waldemar Nowakowski. "Squat detection in railway rails using Gabor filter bank, SVM classifier and Genetic Algorithms." In 2017 15th International Conference on ITS Telecommunications (ITST). IEEE, 2017. http://dx.doi.org/10.1109/itst.2017.7972229.

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Singh, Shalini, Indrajit Das, Md Golam Mohiuddin, Amogh Banerjee, and Sonali Gupta. "Design and Implementation of Gabor Filter and SVM based Authentication system using Machine Learning." In 2019 Devices for Integrated Circuit (DevIC). IEEE, 2019. http://dx.doi.org/10.1109/devic.2019.8783650.

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9

Huang, Deng-Yuan, Wu-Chih Hu, and Sung-Hsiang Chang. "Vision-Based Hand Gesture Recognition Using PCA+Gabor Filters and SVM." In 2009 Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP). IEEE, 2009. http://dx.doi.org/10.1109/iih-msp.2009.96.

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10

Mrinalini, S., N. S. Abinayalakshmi, and C. Vinoth Kumar. "Wavelet feature based SVM and NAIVE BAYES classification of glaucomatous images using PCA and Gabor filter." In 2016 10th International Conference on Intelligent Systems and Control (ISCO). IEEE, 2016. http://dx.doi.org/10.1109/isco.2016.7726898.

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Звіти організацій з теми "SVM AND GABOR FILTER"

1

Li, Hua. Locally Connected Adaptive Gabor Filter for Real-Time Motion Compensation. Fort Belvoir, VA: Defense Technical Information Center, October 1994. http://dx.doi.org/10.21236/ada285726.

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2

Li, Hua H. Locally Connected Adaptive Gabor Filter for Real-Time Motion Compensation. Fort Belvoir, VA: Defense Technical Information Center, April 1995. http://dx.doi.org/10.21236/ada300347.

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3

Li, Hua. Locally Connected Adaptive Gabor Filter for Real-Time Motion Compensation. Fort Belvoir, VA: Defense Technical Information Center, January 1992. http://dx.doi.org/10.21236/ada275175.

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