Статті в журналах: "SVM AND GABOR FILTER"

1

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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11

Wijaya, Intan Raharni. "Analisis dan Implementasi Metode Gabor Filter dan Support Vector Machine pada Klasifikasi Sidik Jari." Indonesian Journal on Computing (Indo-JC) 2, no. 2 (November 20, 2017): 37. http://dx.doi.org/10.21108/indojc.2017.2.2.176.

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Pengolahan citra digital semakin diminati, salah satunya pada sistem biometrik. Sistem biometrik merupakan sistem dalam pengenalan berdasarkan pola atau ciri khusus yang dimiliki makhluk hidup terutama manusia. Jenis identifikasi biometrik yang umum digunakan adalah pengenalan sidik jari. Sidik jari banyak digunakan dalam kehidupan sehari-hari selama lebih dari 100 tahun karena penerimaan yang tinggi, permanen, akurat, dan keunikan. Kelebihan sidik jari tersebut disebabkan oleh minutiae yang merupakan garis atau guratan pada sidik jari yang berbeda-beda setiap individu. Klasifikasi sidik jari secara umum terbagi menjadi dua tahap yakni ekstraksi fitur serta klasifikasi fitur. <br /> <br /> Ektraksi fitur dapat dilakukan dengan cara filter seperti gabor filter dengan empat sudut orientasi yang berkisar 0, 45, 90 dan 135 derajat. Hasil dari ekstraksi ciri akan klasifikasi dengan tujutan identifikasi. Metode Support Vector Machine (SVM) dapat digunakan sebagai classifier untuk sistem biometrik sidik jari. SVM memiliki kernel trick yang berpengaruh pada akurasi yang dihasilkan. Digunakan SVM multiclass metode one-against-all dalam klasfikasi sidik jari untuk 25 kelas. Akurasi terbesar diperoleh oleh kernel Radial Basis Function (RBF) sebesar 73% untuk data awal dan 76% untuk penambahan data augmentasi

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Ullah, Sajeed, Mehran Ahmad, Shahzad Anwar, and Muhammad Irfan Khattak. "An Intelligent Hybrid Approach for Brain Tumor Detection." Pakistan Journal of Engineering and Technology 6, no. 1 (February 10, 2023): 42–50. http://dx.doi.org/10.51846/vol6iss1pp34-42.

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Brain tumours are quickly increasing in prevalence all over the world. It causes the deaths of thousands of individuals annually. Misdiagnosis of brain tumours often results in unnecessary treatment, further lowering the survival rate of the affected individuals. Prompt medical diagnosis is crucial to improve the prognosis for patients with brain tumours. Positive advancements in deep and machine learning domains have been made due to repeated achievements in supporting medical practitioners in making correct diagnoses utilizing computer-aided diagnostic tools. Deep convolutional layers are superior to conventional methods at extracting unique characteristics from target regions. In this research, initially, Gabor filter and ResNet50 were applied to accurately extract the important features of brain tumours from the MRI images dataset. Firstly, the extracted features of Gabor and ResNet50 were classified individually through SVM, and secondly, the features from both these techniques were combined and then classified through SVM. The Kaggle MRI dataset for a brain tumour was utilized in this research. It includes 7,023 Images and four classes Glioma, Meningioma, No-Tumor, and Pituitary. The results from every system were outstanding, but the best results were shown by the combined features of Gabor and ResNet50, an advanced hybrid approach with 95.73% accuracy, 95.90% precision, and 95.72% f1 score.

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Wang, Qing Wei, and Zi Lu Ying. "Facial Expression Recognition Algorithm Based on Gabor Texture Features and Adaboost Feature Selection via Sparse Representation." Applied Mechanics and Materials 511-512 (February 2014): 433–36. http://dx.doi.org/10.4028/www.scientific.net/amm.511-512.433.

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This paper proposed a new facial expression recognition algorithm based on gabor texture features and Adaboost feature selection via SRC(sparse representation classification). Five scales and eight orientations of Gabor wavelet filters were used in this paper to extract gabor features. For an image of size , the number of gabor features is 163840, In order to extract the most effective features for FER(facial expression recognition), Adaboost algorithm is used for feature selection. This paper divided 7 facial expressions into two categories, where the neutral expression as the first class and the remaining six expressions as the second class. In each size and orientation 110 features are selected. At last 4400 features are selected combined SRC algorithm for FER. Test experiments were performed on Japanese female JAFFE facial expression database. Compared with the traditional expression recognition algorithms such as 2DPCA+SVM, LDA+SVM, the new algorithm achieved a better recognition rate, which shows the effectiveness of the proposed new algorithm.

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Zhang, Zhonghua, Xuecai Yu, Feng You, George Siedel, Wenqiang He, and Lifang Yang. "A Front Vehicle Detection Algorithm for Intelligent Vehicle Based on Improved Gabor Filter and SVM." Recent Patents on Computer Science 8, no. 1 (May 5, 2015): 32–40. http://dx.doi.org/10.2174/2213275907666141023220519.

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15

Deng, Liwei, Yangang Guo, and Borong Chai. "Defect Detection on a Wind Turbine Blade Based on Digital Image Processing." Processes 9, no. 8 (August 20, 2021): 1452. http://dx.doi.org/10.3390/pr9081452.

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Wind power generation is a widely used power generation technology. Among these, the wind turbine blade is an important part of a wind turbine. If the wind turbine blade is damaged, it will cause serious consequences. The traditional methods of defect detection for wind turbine blades are mainly manual detection and acoustic nondestructive detection, which are unsafe and time-consuming, and have low accuracy. In order to detect the defects on wind turbine blades more safely, conveniently, and accurately, this paper studied a defect detection method for wind turbine blades based on digital image processing. Because the log-Gabor filter used needed to extract features through multiple filter templates, the number of output images was large. Firstly, this paper used the Lévy flight strategy to improve the PSO algorithm to create the LPSO algorithm. The improved LPSO algorithm could successfully solve the PSO algorithm’s problem of falling into the local optimal solution. Then, the LPSO algorithm and log-Gabor filter were used to generate an adaptive filter, which could directly output the optimal results in multiple feature extraction images. Finally, a classifier based on HOG + SVM was used to identify and classify the defect types. The method extracted and identified the scratch-type, crack-type, sand-hole-type, and spot-type defects, and the recognition rate was more than 92%.

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Yinka Ogundepo, Oludare, Isaac Ozovehe Avazi Omeiza, and Jonathan Ponmile Oguntoye. "Optimized textural features for mass classification in digital mammography using a weighted average gravitational search algorithm." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 5 (October 1, 2022): 5001. http://dx.doi.org/10.11591/ijece.v12i5.pp5001-5013.

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Early detection of breast cancer cells can be predicted through a precise feature extraction technique that can produce efficient features. The application of Gabor filters, gray level co-occurrence matrices (GLCM) and other textural feature extraction techniques have proven to achieve promising results but were often characterized by a high false-positive rate (FPR) and false-negative rate (FNR) with high computational complexities. This study optimized textural features for mass classification in digital mammography using the weighted average gravitational search algorithm (WA-GSA). The Gabor and GLCM features were fused and optimized using WA-GSA to overcome the weakness of the textural feature techniques. With support vector machine (SVM) used as the classifier, the proposed algorithm was compared with commonly applied techniques. Experimental results show that the SVM with WA-GSA features achieved FPR, FNR and accuracy of 1.60%, 9.68% and 95.71% at 271.83 s, respectively. Meanwhile, SVM with Gabor features achieved FPR, FNR and accuracy of 3.21%, 12.90% and 93.57% at 2351.29 s, respectively, while SVM with GLCM features achieved FPR, FNR and accuracy of 4.28%, 18.28% and 91.07% at 384.54 s, respectively. The obtained results show the prevalence of the proposed algorithm, WA-GSA, in the classification of breast cancer tumor detection.

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Yadlapalli, Priyanka, Madhavi K. Reddy, Sunitha Gurram, J. Avanija, K. Meenakshi, and Padmavathi Kora. "Breast Thermograms Asymmetry Analysis using Gabor filters." E3S Web of Conferences 309 (2021): 01109. http://dx.doi.org/10.1051/e3sconf/202130901109.

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Women are far more likely than males to acquire breast cancer, and current research indicates that this is entirely avoidable. It is also to blame for higher death rates among younger women compared to older women in nearly all developing nations. Medical imaging modalities are continuously in need of development. A variety of medical techniques have been employed to detect breast cancer in women. The most recent studies support mammography for breast cancer screening, although its sensitivity and specificity remain suboptimal, particularly in individuals with thick breast tissue, such as young women. As a result, alternative modalities, such as thermography, are required. Digital Infrared Thermal Imaging (DITI), as it is known, detects and records temperature changes on the skin’s surface. Thermography is well-known for its non-invasive, painless, cost-effective, and high recovery rates, as well as its potential to identify breast cancer at an early stage. Gabor filters are used to extract the textural characteristics of the left and right breasts. Using a support vector machine, the thermograms are then classified as normal or malignant based on textural asymmetry between the breasts (SVM). The accuracy achieved by combining Gabor features with an SVM classifier is around 84.5 percent. The early diagnosis of cancer with thermography enhances the patient’s chances of survival significantly since it may detect the disease in its early stages.

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Ayad, Hayder, Loay Edwar George, Mamoun Jassim Mohammed, Raad Ahmed Hadi, and Siti Norul Huda Sheikh Abdullah. "An Efficient Approach for Visual Object Categorization based on Enhanced Generalized Gabor Filter and SVM Classifier." Journal of Engineering and Applied Sciences 14, no. 16 (November 20, 2019): 5753–61. http://dx.doi.org/10.36478/jeasci.2019.5753.5761.

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19

Shanthi, A. S., and M. Karthikeyan. "Improving Gabor Filter Bank Design and SVM Optimization Using Cuckoo Search for Mild Cognitive Impairment Classification." Journal of Medical Imaging and Health Informatics 6, no. 3 (June 1, 2016): 784–87. http://dx.doi.org/10.1166/jmihi.2016.1759.

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20

Lin, Chuan, Xiao Feng Lv, Yi Jun Cao, Jiang Hua Wei, and Cong Lin. "Research on Solder Joints Quality Detection of Auto Parts Based on Biological Vision Feature." Advanced Materials Research 712-715 (June 2013): 2385–88. http://dx.doi.org/10.4028/www.scientific.net/amr.712-715.2385.

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Detecting of the auto parts solder joints quality is always a technological difficulty in the production line, the key point is how to exact the parts feature which is tested. New quality detection method for the auto parts soldered joints is presented based on biology vision feature. First the Gabor filter banks which have the emulation of biological vision is used to filter the detected image with eight different directions. Then the biggest powers of all pixel points are chosen to be the solder joints feature and classified by support vector machine (SVM). The experimental results show that the algorithm has higher accuracy as an effective quality detection method. It can be a useful reference to the auto parts solder joints quality testing of engineering application.

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Sriraam, N., D. Nithyashri, L. Vinodashri, and P. Manoj Niranjan. "A SVM Based Automated Detection of Uterine Fibroids Using Gabor and Wavelet Features." International Journal of Biomedical and Clinical Engineering 1, no. 1 (January 2012): 77–85. http://dx.doi.org/10.4018/ijbce.2012010106.

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This paper suggests an automated detection procedure for identification of ultrasonic imaging based uterine fibroids using Gabor filters and wavelet features with support vector machines as classifier. A classification accuracy of 100% is achieved for 86 test images using wavelet packet features, which indicates the potential suitability of the proposed scheme for clinical diagnosis.

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Zhang, Chuanwei, Xiangyang Yue, Rui Wang, Niuniu Li, and Yupeng Ding. "Study on Traffic Sign Recognition by Optimized Lenet-5 Algorithm." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 01 (June 12, 2019): 2055003. http://dx.doi.org/10.1142/s0218001420550034.

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Traffic sign recognition (TSR) is a key technology of intelligent vehicles, which is based on visual perception for road information. In view of the fact that the traditional computer vision identification technology cannot meet the requirements of real-time accuracy, the TSR algorithm has been proposed on the basis of improved Lenet-5 algorithm. Firstly, we performed picture noise elimination and image enhancement on selected traffic sign images. Secondly, we used Gabor filter kernel in the convolution layer for convolution operation. In the convolution process, we added normalization layer Batch Normality (BN) after each convolution layer and reduced the data dimension. In the down-sampling layer, we replaced Sigmoid with the Relu activator. Finally, we selected the expanded GTSRB traffic sign database for the comparison experiment on the Caff platform. The experimental results showed that the proposed improved Lenet-5 network test set had the recognition accuracy of 96%, which was better than the method that combined Gabor with Support Vector Machine (SVM) in terms of recognition accuracy and real-time performance.

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23

Luo, Jian, Jin Tang, and Xiaoming Xiao. "Abnormal Gait Behavior Detection for Elderly Based on Enhanced Wigner-Ville Analysis and Cloud Incremental SVM Learning." Journal of Sensors 2016 (2016): 1–18. http://dx.doi.org/10.1155/2016/5869238.

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A cloud based health care system is proposed in this paper for the elderly by providing abnormal gait behavior detection, classification, online diagnosis, and remote aid service. Intelligent mobile terminals with triaxial acceleration sensor embedded are used to capture the movement and ambulation information of elderly. The collected signals are first enhanced by a Kalman filter. And the magnitude of signal vector features is then extracted and decomposed into a linear combination of enhanced Gabor atoms. The Wigner-Ville analysis method is introduced and the problem is studied by joint time-frequency analysis. In order to solve the large-scale abnormal behavior data lacking problem in training process, a cloud based incremental SVM (CI-SVM) learning method is proposed. The original abnormal behavior data are first used to get the initial SVM classifier. And the larger abnormal behavior data of elderly collected by mobile devices are then gathered in cloud platform to conduct incremental training and get the new SVM classifier. By the CI-SVM learning method, the knowledge of SVM classifier could be accumulated due to the dynamic incremental learning. Experimental results demonstrate that the proposed method is feasible and can be applied to aged care, emergency aid, and related fields.

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Hoang, Nhat-Duc, and Quoc-Lam Nguyen. "A Novel Approach for Automatic Detection of Concrete Surface Voids Using Image Texture Analysis and History-Based Adaptive Differential Evolution Optimized Support Vector Machine." Advances in Civil Engineering 2020 (July 28, 2020): 1–15. http://dx.doi.org/10.1155/2020/4190682.

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To inspect the quality of concrete structures, surface voids or bugholes existing on a concrete surface after the casting process needs to be detected. To improve the productivity of the inspection work, this study develops a hybrid intelligence approach that combines image texture analysis, machine learning, and metaheuristic optimization. Image texture computations employ the Gabor filter and gray-level run lengths to characterize the condition of a concrete surface. Based on features of image texture, Support Vector Machines (SVM) establish a decision boundary that separates collected image samples into two categories of no surface void (negative class) and surface void (positive class). Furthermore, to assist the SVM model training phase, the state-of-the-art history-based adaptive differential evolution with linear population size reduction (L-SHADE) is utilized. The hybrid intelligence approach, named as L-SHADE-SVM-SVD, has been developed and complied in Visual C#.NET framework. Experiments with 1000 image samples show that the L-SHADE-SVM-SVD can obtain a high prediction accuracy of roughly 93%. Therefore, the newly developed model can be a promising alternative for construction inspectors in concrete quality assessment.

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Gupta, Sandeep Kumar, Seid Hassen Yesuf, and Neeta Nain. "Real-Time Gender Recognition for Juvenile and Adult Faces." Computational Intelligence and Neuroscience 2022 (March 17, 2022): 1–15. http://dx.doi.org/10.1155/2022/1503188.

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Facial gender recognition is a crucial research topic due to its comprehensive use cases, including a demographic gender survey, visitor profile identification, targeted advertisement, access control, security, and surveillance from CCTV. For these real-time applications, the face of a person can be oriented to any angle from the camera axis, and the person can be of any age group, including juveniles. A child’s face consists of immature craniofacial feature points in texture and edge compared to an adult face, making it very hard to recognize gender using the child’s face. Real-word faces captured in an unconstrained environment make the gender prediction system more complex to identify correctly due to orientation. These factors reduce the accuracy of the existing state-of-the-art models developed so far for real-time facial gender prediction. This paper presents the novelty of facial gender recognition for juveniles, adults, and unconstrained-oriented faces. The progressive calibration network (PCN) detects rotation-invariant faces in the proposed model. Then, a Gabor filter is applied to extract unique edge and texture features from the detected face. The Gabor filter is invariant to illumination and produces texture and edge features with redundant feature coefficients in large dimensions. Gabor has drawbacks such as redundancy and a large dimension resolved by the proposed meanDWT feature optimization method, which optimizes the system’s accuracy, the size of the model, and computational timing. The proposed feature engineering model is classified with different classifiers such as Naïve Bayes, Logistic Regression, SVM with linear, and RBF kernel. Its results are compared with the state-of-the-art techniques; detailed experimental analysis is presented and concluded to support the argument. We also present a review of approaches based on conventional and deep learning methods with their pros and cons for facial gender recognition on different datasets available for facial gender recognition.

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El-Sayed, Mohamed A., M. Hassaballah, and Mohammed A. Abdel-Latif. "Identity Verification of Individuals Based on Retinal Features Using Gabor Filters and SVM." Journal of Signal and Information Processing 07, no. 01 (2016): 49–59. http://dx.doi.org/10.4236/jsip.2016.71007.

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Appati, Justice Kwame, Winfred Yaokumah, Ebenezer Owusu, and Paul Nii Tackie Ammah. "Primary Mobile Image Analysis of Human Intestinal Worm Detection." International Journal of System Dynamics Applications 11, no. 1 (January 1, 2022): 1–16. http://dx.doi.org/10.4018/ijsda.302631.

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One among a lot of public health concerns in rural and tropical areas is the human intestinal parasite. Traditionally, diagnosis of these parasites is by visual analysis of stool specimens, which is usually tedious and time-consuming. In this study, the authors combine techniques in the Laplacian pyramid, Gabor filter, and wavelet to build a feature vector for the discrimination of intestinal worm in a low-resolution image captured with mobile devices. The dimension of the feature vector is reduced using principal component analysis, and the resultant vector is considered as input to the SVM classifier. The proposed framework was applied to the Makerere intestinal dataset. At its preliminary stage, the results demonstrate satisfactory classification with an accuracy rate of 65.22% with possible extension in future work.

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Ruan, Jin Xin, Li Ying Xie, and Jun Xun Yin. "Facial Expression Recognition Based on Improved Dimension Reduction of Gabor Feature and Two-against-Two Multi-Class SVM Classification." Applied Mechanics and Materials 373-375 (August 2013): 654–59. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.654.

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The facial expression recognition technology has been widespread concerned and researched, and many methods have been presented. This paper focuses on studying and analyzing the feature extraction, feature dimension reduction and two-against-two multi-class Support Vector Machine (SVM) method, and an algorithm is proposed for recognition of six basic facial expressions. According to expression feature information in the different face region, the algorithm adopts local nonuniform feature point extraction to reduce the feature dimension. After transforming the feature points with Gabor filters, the Gabor expression features are obtained. And the feature dimension is further reduced by discrete wavelet transform (DWT) and discrete cosine transform (DCT). At last, the tow-against-two classification method and an optimum decision scheme are used to realize quick and accurate expression classification. Experimental results show the algorithm can achieve higher recognition rate, recognition speed and stronger robust.

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Chen, Yulin, Hailing Sun, Guofu Zhou, and Bao Peng. "Fruit Classification Model Based on Residual Filtering Network for Smart Community Robot." Wireless Communications and Mobile Computing 2021 (March 27, 2021): 1–9. http://dx.doi.org/10.1155/2021/5541665.

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With the rapid development of computer vision and robot technology, smart community robots based on artificial intelligence technology have been widely used in smart cities. Considering the process of feature extraction in fruit classification is very complicated. And manual feature extraction has low reliability and high randomness. Therefore, a method of residual filtering network (RFN) and support vector machine (SVM) for fruit classification is proposed in this paper. The classification of fruits includes two stages. In the first stage, RFN is used to extract features. The network consists of Gabor filter and residual block. In the second stage, SVM is used to classify fruit features extracted by RFN. In addition, a performance estimate for the training process carried out by the K -fold cross-validation method. The performance of this method is assessed with the accuracy, recall, F1 score, and precision. The accuracy of this method on the Fruits-360 dataset is 99.955%. The experimental results and comparative analyses with similar methods testify the efficacy of the proposed method over existing systems on fruit classification.

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Thomas, Renjith, and M. J. S. Rangachar. "Fractional Bat and Multi-Kernel-Based Spherical SVM for Low Resolution Face Recognition." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 08 (May 9, 2017): 1756014. http://dx.doi.org/10.1142/s0218001417560146.

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Face recognition is an important aspect of the biometric surveillance system. Generally, face recognition is a type of biometric system that can identify a specific individual by analyzing and comparing patterns in the facial image. Face recognition has distinct advantage over other biometrics is noncontact process. It has a wide variety of applications in both the law enforcement and nonlaw enforcement. While using the low resolution face images, the resolution of the image gets degraded. In this paper, to enhance the performance rate for low resolution image, the fractional Bat algorithm and multi-kernel-based spherical SVM classifier is proposed. Initially, the low resolution image is converted into the high resolution images by the kernel regression method. The GWTM process is utilized for the feature extraction by the Gabor filter, wavelet transform and local binary pattern (texture descriptors). Then, the super resolution images are applied to the feature level fusion by using the fractional Bat algorithm which comprises of fractional theory and Bat algorithm. Finally, the multi-kernel-based spherical SVM classifier is introduced for the recognition of feature images. The experimental results and performance analysis evaluated by the comparison metrics are FAR, FRR and Accuracy with existing systems. Thus, the outcome of our proposed system achieves the highest accuracy of 95% based on the training data samples, stopping criterion and number of draw attempts.

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Zhang, Rong-Hui, Feng You, Fang Chen, and Wen-Qiang He. "Vehicle Detection Method for Intelligent Vehicle at Night Time Based on Video and Laser Information." International Journal of Pattern Recognition and Artificial Intelligence 32, no. 04 (December 13, 2017): 1850009. http://dx.doi.org/10.1142/s021800141850009x.

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Front vehicle detection technology is one of the hot spots in the advanced driver assistance system research field. This paper puts forward a method for front vehicles detection based on video-and-laser-information at night. First of all, video images and laser data are pre-processed with the region growing and threshold area expunction algorithm. Then, the features of front vehicles are extracted by use of a Gabor filter based on the uncertainty principle, and the distances to front vehicles are obtained through laser point cloud. Finally, front vehicles are automatically classified during identification with the improved sequential minimal optimization algorithm, which was based on the support vector machine (SVM) algorithm. According to the experiment results, the method proposed by this text is effective and it is reliable to identify vehicles in front of intelligent vehicles at night.

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N., Sheela, and Basavaraj L. "Analysis of gabor filter based features with PCA and GA for the detection of drusen in fundus images." International Journal of Engineering & Technology 7, no. 1 (January 30, 2018): 115. http://dx.doi.org/10.14419/ijet.v7i1.8969.

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Human eye can be affected by different types of diseases. Age-Related Macular Degeneration (AMD) is one of the such diseases, and it mainly occurs after 50 years of age. This disease is characterized by the occurrence of yellow spots called as Drusen. In this work, an automated method for the detection of drusen in Fundus image has been developed, and it has been tested on 70 images consisting of 30 normal images and 40 images with drusen. Performance of the Support Vector Machine (SVM) and K Nearest Neighbor (KNN) classifier has been evaluated using Data's reduction using Principle Component Analysis (PCA) and Data's selection using Genetic Algorithm (GA).Performance evaluation has been done in terms of accuracy, sensitivity, specificity, misclassification rate, positive predictive rate, negative predictive rate and Youden’s Index. The proposed method has achieved highest accuracy of 98.7% when data selection using Genetic Algorithm has been applied.

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ROKITA, JOANNA, ADAM KRZYŻAK, and CHING Y. SUEN. "MULTIMODAL BIOMETRICS BY FACE AND HAND IMAGES TAKEN BY A CELL PHONE CAMERA." International Journal of Pattern Recognition and Artificial Intelligence 22, no. 03 (May 2008): 411–29. http://dx.doi.org/10.1142/s0218001408006302.

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This paper presents a multimodal approach for a biometrics verification system. It is based on face and hand images captured by a cell phone. The algorithm includes all parts that are required for face and hand verification, such as feature extraction, classification and authentication. To find local facial features, such as eyes, mouth and nose, we apply a point distribution model and active shape models. We use the same system to find distinctive points in hand geometry. The face feature vector is constructed by applying a Gabor filter to the image and extracting the key points found by an active shape model. The palm feature vector contains characteristics of the hand geometry features. A support vector machine (SVM) is applied to verify the identity of the user. One SVM machine is built for each person in the database to distinguish that person from others. To test the algorithm we built our own database containing face and hand images taken by a cell phone camera. The database contains 480 frontal face images and 120 hand images of 30 persons (16 face images and 4 hand images per person).

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Biswas, Rubel, Jia Uddin, and Md Junayed Hasan. "A New Approach of Iris Detection and Recognition." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 5 (October 1, 2017): 2530. http://dx.doi.org/10.11591/ijece.v7i5.pp2530-2536.

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This paper proposes an IRIS recognition and detection model for measuring the e-security. This proposed model consists of the following blocks: segmentation and normalization, feature encoding and feature extraction, and classification. In first phase, histogram equalization and canny edge detection is used for object detection. And then, Hough Transformation is utilized for detecting the center of the pupil of an IRIS. In second phase, Daugmen’s Rubber Sheet model and Log Gabor filter is used for normalization and encoding and as a feature extraction method GNS (Global Neighborhood Structure) map is used, finally extracted feature of GNS is feed to the SVM (Support Vector Machine) for training and testing. For our tested dataset, experimental results demonstrate 92% accuracy in real portion and 86% accuracy in imaginary portion for both eyes. In addition, our proposed model outperforms than other two conventional methods exhibiting higher accuracy.

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Booysens, Aimee, and Serestina Viriri. "Ear Biometrics Using Deep Learning: A Survey." Applied Computational Intelligence and Soft Computing 2022 (August 17, 2022): 1–17. http://dx.doi.org/10.1155/2022/9692690.

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This paper explores ear biometrics using a mixture of feature extraction techniques and classifies this feature vector using deep learning with convolutional neural network. This exploration of ear biometrics uses images from 2D facial profiles and facial images. The investigated feature techniques are Zernike Moments, local binary pattern, Gabor filter, and Haralick texture moments. The normalised feature vector is used to examine whether deep learning using convolutional neural network is better at identifying the ear than other commonly used machine learning techniques. The widely used machine learning techniques that were used to compare them are decision tree, naïve Bayes, K-nearest neighbors (KNN), and support vector machine (SVM). This paper proved that using a bag of feature techniques and the classification technique of deep learning using convolutional neural network was better than standard machine learning techniques. The result achieved by the deep learning using convolutional neural network was 92.00% average ear identification rate for both left and right ears.

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36

Huang, Liang, Shenkai Nong, Xiaofeng Wang, Xiaohang Zhao, Chaoran Wen, and Ting Nie. "Combined Spatial-Spectral Hyperspectral Image Classification Based on Adaptive Guided Filtering." Traitement du Signal 39, no. 2 (April 30, 2022): 745–54. http://dx.doi.org/10.18280/ts.390240.

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Hyperspectral image classification has a low accuracy in the face of a small training set. To solve the problem, this paper proposes a combined spatial-spectral hyperspectral image classification approach based on adaptive guided filtering. From coarse to fine classification, the local binary pattern (LBP) histogram features were improved, the spatial contrast description was enhanced, and enhanced spatial-spectral features were prepared through Gabor transform of different scales and directions, combined with super pixel blocks. Then, the pre-classification was completed by the support vector machine (SVM) classifier. To reduce noise interference, the pre-classification results were filtered again by a guided filter based on the adaptive regularization factor. To verify its effectiveness, the proposed approach was compared with the state-of-the-arts approaches through repeated experiments. The comparison shows that our approach achieved a high classification accuracy, while suppressing noise interference. This research provides a new tool for hyperspectral image classification with a small training set.

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Wulandari, Sari Ayu, Rudy Tjahyono, and Dian Retno Sawitri. "PERBANDINGAN TINGKAT PENGENALAN CITRA DIABETIC RETINOPATHY PADA KOMBINASI PRINCIPLE COMPONENT DARI 4 CIRI BERBASIS METODE SVM (SUPPORT VECTOR MACHINE)." Majalah Ilmiah Teknologi Elektro 15, no. 1 (June 25, 2016): 95. http://dx.doi.org/10.24843/mite.2016.v15i01p17.

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Perbedaan pigmentasi mempengaruhi metode pengenalan pola citra retinopati diabetik beserta setting poinnya. Dibutuhkan sebuah perangkat lunak, yang mampu menjadi alat bantu pengenalan citra retinopati diabetik. Telah dilakukan penelitian tentang pengenalan pola citra retinopati diabetik, dengan menggunakan citra kanal kuning (Yellow), dengan menggunakan filter gabor dan ciri yang diambil dari tiap citra adalah ciri rerata (Means), variasi Varians), skewness dan entropy, yang dilanjutkan dengan ekstraksi ciri PCA (Principle Component Analysis). Pada ekstraksi ciri PCA, Matriks hasil PCA merupakan matriks bujur sangkar, yang jumlah kolomnya, sama dengan jumlah ciri. Penelitian menggunakan 4 ciri, dengan demikian, terdapat 4 buah PC (Principle Component), PC1, PC2, PC3 dan PC4. Pada artikel ini akan dibahas mengenai tingkat akurasi tertinggi dari penggunaan pasangan PC. Tingkat akurasi, dihitung dengan menggunakan model linear dari SVM. Model dengan akurasi tertinggi dan tercepat adalah model pasangan PC1 dan PC2, yang mempunyai akurasi citra pembelajaran tertinggi yaitu 100% dan waktu tercepat, yang secara eksplisit diperlihatkan pada jumlah support vektor terkecil, yaitu 2. Pasangan yang mempunyai tingkat akurasi terburuk adalah PC3 dan PC4. Pengenalan turun pada citra pengujian, yaitu hanya 93,75%, hal ini disebabkan oleh pelebaran daerah cakupan. Pelebaran daerah cakupan kemungkinan disebabkan oleh pemilihan nilai rerata pada PCA, sebelum matriks reduksi. Pada penelitian berikutnya, bisa dilakukan dengan menggunakan pencarian nilai standart deviasi atau varians, dengan begitu, akan diketahui matriks reduksi yang mewakili sebaran angka pada matriks. DOI: 10.24843/MITE.1501.17

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38

de Assis, Débora, and Paulo Cesar Cortez. "A Comparative Analysis of Glaucoma Feature Extraction and Classification Techniques in Fundus Images." Journal of Communication and Information Systems 38, no. 1 (April 2023): 47–60. http://dx.doi.org/10.14209/jcis.2023.6.

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Glaucoma is a chronic asymptomatic eye disease that, if not treated in the initial stages, can induce blindness. However, early detection and proper treatment can prevent vision loss. Therefore, this work aims to evaluate the identification of glaucoma by non-invasive methods in fundus images. Initially, we have extracted the characteristics of images from the REFUGE and ACRIMA databases through the descriptors: Local Binary Patterns (LBP), Oriented Gradient Histogram (HOG), Zernike moments, and statistical information after the application of the Gabor filter. Then, we are given these characteristics to Multilayer Perceptron (MLP), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGB) classifiers. The ranking is performed by each classifier individually and using a voting classifier. Additionally, we apply the cut-off threshold to define the predicted output due to the unbalance of the classes. To compare the results, we applied nonparametric tests. The voting classifier results reach an average rate for balanced accuracy equal to 93.29 \%, precision 88.74 \%, recall 92.04 \%, specificity 94.54 \%, and F2 score 91.33 \%. Therefore, using the cut-off threshold is essential for improving the recall results and reducing false negatives.

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39

Pourfard, Mohammadreza, Karim Faez, and S. Hadi Tabaian. "Unsupervised Gabor Filter-Bank Method for Characterization of the Self-Assembled Hexagonal Lattice." Journal of Nano Research 31 (April 2015): 40–61. http://dx.doi.org/10.4028/www.scientific.net/jnanor.31.40.

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In this paper a new robust and precise ordering criterion for the characterization of self-assembled hexagonal lattice like Anodic aluminum Oxide (AAO) has been proposed. In order to unveil the mechanism for the self-organization process and deposition techniques in AAO, it is necessary to be able to have a quantitative objective criterion to evaluate the amount of order through every SEM sample of a material. Most of methods in the literature are only able to characterize the extreme case of highly ordered or lowly ordered texture well. But the real challenge is in characterizing the order of medium-ordered texture which is the dual concept of near-regular texture analysis in image processing. Our method based on more advanced image processing techniques, Gabor filter-bank, are able to characterize medium-ordered AAO textures more precisely. Our idea is also able to define the domain's place of the AAO image.

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40

Alharan, Abbas F. H., Hayder K. Fatlawi, and Nabeel Salih Ali. "A cluster-based feature selection method for image texture classification." Indonesian Journal of Electrical Engineering and Computer Science 14, no. 3 (June 1, 2019): 1433. http://dx.doi.org/10.11591/ijeecs.v14.i3.pp1433-1442.

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<p>Computer vision and pattern recognition applications have been counted serious research trends in engineering technology and scientific research content. These applications such as texture image analysis and its texture feature extraction. Several studies have been done to obtain accurate results in image feature extraction and classifications, but most of the extraction and classification studies have some shortcomings. Thus, it is substantial to amend the accuracy of the classification via minify the dimension of feature sets. In this paper, presents a cluster-based feature selection approach to adopt more discriminative subset texture features based on three different texture image datasets. Multi-step are conducted to implement the proposed approach. These steps involve texture feature extraction via Gray Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP) and Gabor filter. The second step is feature selection by using K-means clustering algorithm based on five feature evaluation metrics which are infogain, Gain ratio, oneR, ReliefF, and symmetric. Finally, K-Nearest Neighbor (KNN), Naive Bayes (NB) and Support Vector Machine (SVM) classifiers are used to evaluate the proposed classification performance and accuracy. Research achieved better classification accuracy and performance using KNN and NB classifiers that were 99.9554% for Kelberg dataset and 99.0625% for SVM in Brodatz-1 and Brodatz-2 datasets consecutively. Conduct a comparison to other studies to give a unified view of the quality of the results and identify the future research directions.</p>

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41

Alomari, Saleh Ali. "An Efficient System for Diagnosis of Human Blindness Using Image-Processing and Machine-Learning Methods." International Journal of Online and Biomedical Engineering (iJOE) 19, no. 10 (August 1, 2023): 82–98. http://dx.doi.org/10.3991/ijoe.v19i10.37681.

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The two main causes of blindness are diabetes and glaucoma. Routine diagnosis of blindness is based on the conventional robust mass-screening method. However, despite being cost-effective, this method has some problems as a human eye-disease detection method because there are many types of eye disease that are similar or that result in no visual changes in the eye image. These issues make it highly difficult to recognize blindness and control it. Moreover, the color of the macula of the spot can be very close to that of the affected macula in a variety of eye diseases, which suggests that the color of the macula spot can indicate various possibilities, rather than one. This paper discusses the shortcomings of current blindness-screening and monitoring systems and presents a feature-based blindness diagnosis approach using digital eye fundus images for the purpose of automated diagnosis of eye disorders, considering three conditions: healthy eye, diabetic retinopathy (DR), and glaucoma. As such, this paper develops a computer-aided diagnosis (CAD) method for automated detection of human blindness. The proposed approach integrates Gabor filter features, statistical features, colored features, morphological features, and local binary pattern features, then compares them with features drawn from a standard dataset of 1580 fundus images. Several classification techniques were applied to the extracted-features neural network (NN), support vector machine (SVM), naïve bias (NB). SVM classifiers show the most promising accuracy. They achieved 93.3% over the other classifiers.

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42

Hajraoui, Abdellatif, and Mohamed Sabri. "Generic and Robust Method for Head Pose Estimation." Indonesian Journal of Electrical Engineering and Computer Science 4, no. 2 (November 1, 2016): 439. http://dx.doi.org/10.11591/ijeecs.v4.i2.pp439-446.

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Head pose estimation has fascinated the research community due to its application in facial motion capture, human-computer interaction and video conferencing. It is a pre-requisite to gaze tracking, face recognition, and facial expression analysis. In this paper, we present a generic and robust method for model-based global 2D head pose estimation from single RGB Image. In our approach we use of the one part the Gabor filters to conceive a robust pose descriptor to illumination and facial expression variations, and that target the pose information. Moreover, we ensure the classification of these descriptors using a SVM classifier. The approach has proved effective view the rate for the correct pose estimations that we got.

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43

Dhakal, Parashar, Praveen Damacharla, Ahmad Javaid, and Vijay Devabhaktuni. "A Near Real-Time Automatic Speaker Recognition Architecture for Voice-Based User Interface." Machine Learning and Knowledge Extraction 1, no. 1 (March 19, 2019): 504–20. http://dx.doi.org/10.3390/make1010031.

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In this paper, we present a novel pipelined near real-time speaker recognition architecture that enhances the performance of speaker recognition by exploiting the advantages of hybrid feature extraction techniques that contain the features of Gabor Filter (GF), Convolution Neural Networks (CNN), and statistical parameters as a single matrix set. This architecture has been developed to enable secure access to a voice-based user interface (UI) by enabling speaker-based authentication and integration with an existing Natural Language Processing (NLP) system. Gaining secure access to existing NLP systems also served as motivation. Initially, we identify challenges related to real-time speaker recognition and highlight the recent research in the field. Further, we analyze the functional requirements of a speaker recognition system and introduce the mechanisms that can address these requirements through our novel architecture. Subsequently, the paper discusses the effect of different techniques such as CNN, GF, and statistical parameters in feature extraction. For the classification, standard classifiers such as Support Vector Machine (SVM), Random Forest (RF) and Deep Neural Network (DNN) are investigated. To verify the validity and effectiveness of the proposed architecture, we compared different parameters including accuracy, sensitivity, and specificity with the standard AlexNet architecture.

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Wiharto, Fikri Hashfi Nashrullah, Esti Suryani, Umi Salamah, Nurcahya Pradana Taufik Prakisy, and Sigit Setyawan. "Texture-Based Feature Extraction Using Gabor Filters to Detect Diseases of Tomato Leaves." Revue d'Intelligence Artificielle 35, no. 4 (August 31, 2021): 331–39. http://dx.doi.org/10.18280/ria.350408.

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The disease in tomato plants, especially on tomato leaves will have an impact on the quality and quantity of tomatoes produced. Handling disease on tomato leaves that must be done is to detect the type of disease as early as possible, then determine the treatment that must be done. Detection of its types of tomato plant diseases requires sufficient knowledge and experience. The problem is that many beginner farmers in growing tomatoes do not have much knowledge, so they have failed in growing tomatoes. Based on these cases, this study proposes a model for the early detection of disease in tomato leaves based on image processing. The research method used is divided into 5 stages, namely preprocessing, segmentation, feature extraction, classification, and performance evaluation. The feature extraction stage used is texture-based with Gabor filters and color-based filters. The final decision is determined by the Support Vector Machine (SVM) classification algorithm with the Radial Basis Function (RBF) kernel. The test results of the tomato leaf disease detection system produced an average performance parameter of 98.83% specificity, 90.37% sensitivity, 90.34% F1-score, 90.37% accuracy, and 94.60% area under the curve (AUC). Referring to the resulting of the AUC performance, the tomato leaf disease detection system is in the very good category.

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45

MohammedAbuBasim, N., P. Sathyabalan, and P. Suresh. "Analysis of EEG Signals and Facial Expressions to Detect Drowsiness and Fatigue using Gabor Filters and SVM Linear Classifier." International Journal of Computer Applications 115, no. 11 (April 22, 2015): 9–14. http://dx.doi.org/10.5120/20194-2433.

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46

Maximiano da Silva, Flávio Altinier, and Helio Pedrini. "Geometrical Features and Active Appearance Model Applied to Facial Expression Recognition." International Journal of Image and Graphics 16, no. 04 (October 2016): 1650019. http://dx.doi.org/10.1142/s0219467816500194.

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One of the most effective ways of expressing emotion is through facial expressions. This work proposes and discusses a geometrical descriptor based on the calculation of distances from coordinates of facial fiducial points, which are used as features for training support vector machines (SVM) to classify emotions. Three data sets are studied and six basic emotions are considered in our experiments. In comparison to other approaches available in the literature, the results obtained with our geometrical descriptor demonstrated to be very competitive, achieving high classification F-score rates. Additionally, we evaluate whether the combination of our geometrical descriptor with an appearance feature, the Gabor filter, allows emotions to be even more distinguishable for the classifier. The result is positive for two out of three data sets. Finally, to simulate in-the-wild scenarios, an active appearance model (AAM) is trained to position the fiducial points on the correct facial locations, instead of using the ones provided by the data sets. As the fitting error is considered acceptable, the former experiments are also conducted with the new data generated by the AAM. The results show a small drop on the F-score values when compared to the data originally provided by the data sets,but are still satisfactory.

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47

Li, Ji, and Zhen Liu. "The Study of Scene Classification in the Multisensor Remote Sensing Image Fusion." Mathematical Problems in Engineering 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/367105.

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We propose a scene classification method for speeding up the multisensor remote sensing image fusion by using the singular value decomposition of quaternion matrix and the kernel principal component analysis (KPCA) to extract features. At first, images are segmented to patches by a regular grid, and for each patch, we extract color features by using quaternion singular value decomposition (QSVD) method, and the grey features are extracted by Gabor filter and then by using orientation histogram to describe the grey information. After that, we combine the color features and the orientation histogram together with the same weight to obtain the descriptor for each patch. All the patch descriptors are clustered to get visual words for each category. Then we apply KPCA to the visual words to get the subspaces of the category. The descriptors of a test image then are projected to the subspaces of all categories to get the projection length to all categories for the test image. Finally, support vector machine (SVM) with linear kernel function is used to get the scene classification performance. We experiment with three classification situations on OT8 dataset and compare our method with the typical scene classification method, probabilistic latent semantic analysis (pLSA), and the results confirm the feasibility of our method.

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Chen, Xinwei, and Weimin Huang. "Texture Features and Unsupervised Learning-Incorporated Rain-Contaminated Region Identification From X-Band Marine Radar Images." Marine Technology Society Journal 54, no. 4 (July 1, 2020): 59–67. http://dx.doi.org/10.4031/mtsj.54.4.7.

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AbstractA novel method is proposed for identifying rain-contaminated regions in X-band marine radar images. Due to the difference of texture between rain-contaminated and rain-free echoes, a Gabor filter bank and discrete wavelet transform (DWT) are introduced to filter marine radar images and generate texture features. Feature vectors extracted from each pixel of the training samples are input into a clustering model, which is trained using unsupervised learning techniques such as k-means and a self-organizing map (SOM). After distinguishing between rain-free and rain-contaminated clusters, the proposed method is able to cluster pixels into rain-free and rain-contaminated types automatically. Images collected from a shipborne marine radar in a sea trial off the east coast of Canada under rain conditions are utilized to validate the proposed method. Identification results obtained from several clustering models with different combinations of cluster number, texture features, and clustering methods show that rain-contaminated pixels are effectively detected, with an overall identification accuracy of 89.1% for both k-means‐based (k = 4) and 2 × 2-neuron SOM-based clustering models.

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49

Alqudah, Ali Mohammad, Hiam Alquraan, Isam Abu-Qasmieh, and Alaa Al-Badarneh. "Employing Image Processing Techniques and Artificial Intelligence for Automated Eye Diagnosis Using Digital Eye Fundus Images." Journal of Biomimetics, Biomaterials and Biomedical Engineering 39 (November 2018): 40–56. http://dx.doi.org/10.4028/www.scientific.net/jbbbe.39.40.

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Blindness usually comes from two main causes, glaucoma and diabetes. Robust mass screening is performed for diagnosing, such as screening that requires a cost-effective method for glaucoma and diabetic retinopathy and integrates well with digital medical imaging, image processing, and administrative processes. For addressing all these issues, we propose a novel low-cost automated glaucoma and diabetic retinopathy diagnosis system, based on features extraction from digital eye fundus images. This paper proposes a diagnosis system for automated identification of healthy, glaucoma, and diabetic retinopathy. Using a combination of local binary pattern features, Gabor filter features, statistical features, and color features which are then fed to an artificial neural network and support vector machine classifiers. In this work, the classifier identifies healthy, glaucoma, and diabetic retinopathy images with an accuracy of 91.1%,92.9%, 92.9%, and 92.3% and sensitivity of 91.06%, 92.6%, 92.66%, and 91.73% and specificity of 89.83%, 91.26%, 91.96%, and 89.16% for ANN, and an accuracy of 90.0%,92.94%, 95.43%, and 97.92% and sensitivity of 89.34%, 93.26%, 95.72%, and 97.93% and specificity of 95.13%, 96.68%, 97.88%, and 99.05% for SVM, based on 5, 10, 15, and 31 number of selected features. The proposed system can detect glaucoma, diabetic retinopathy and normal cases with high accuracy and sensitivity using selected features, the performance of the system is high due to using of a huge fundus database.

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Mohammed Jawad Al_Dujaili, Haider TH Salim ALRikabi, Nisreen Khalil abed, and Ibtihal Razaq Niama ALRubeei. "Gender Recognition of Human from Face Images Using Multi-Class Support Vector Machine (SVM) Classifiers." International Journal of Interactive Mobile Technologies (iJIM) 17, no. 08 (April 26, 2023): 113–34. http://dx.doi.org/10.3991/ijim.v17i08.39163.

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In the realm of robotics and interactive systems, gender recognition is a crucial problem. Considering the several uses it has in security, web search, human-computer interactions, etc., gender recognition from facial photos has garnered a lot of attention. The need to use and enhance gender recognition techniques is felt more strongly today due to a significant development in the design of facial recognition systems. Relatively speaking to other approaches, the progress gained in this area thus far is not exceptional. Thus, a novel method has been adopted in this study to improve accuracy in comparison to earlier research. To create the best rate of accuracy and efficiency in the suggested method of this research, we choose a minimal set of characteristics. Testing on the FERET and UTK-Face datasets reveals that our suggested algorithm has a lower degree of inaccuracy. In this article, the input image of the person's face is pre-processed to extract the right features from the face once the person's face has been recognized. Gender separation is achieved using Multi-class Support Vector Machine (SVM) Classifiers after features from normalized images have been extracted using Histogram Oriented Gradient (HOG), Gabor Filters, and Speeded Up Robust Features (SURF), as well as their combination to select the most appropriate feature from them as input for gender classification. As a feature reduction feature, the Principal Component Analysis (PCA) algorithm is also employed. Using the proposed approach, 98.75% gender recognition precision has been accomplished on the FERET database and a runtime performance of 0.4 Sec. on the UTK-Face database, 97.43% gender recognition accuracy has been accomplished and a runtime performance of 0.5 Sec.

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