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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Classification of PolSAR Images Based on SVM with Self-Paced Learning Optimization
摘要： A novel classification method for polarimetric synthetic aperture radar (PolSAR) images using support vector machine (SVM) with self-paced learning (SPL) optimization is proposed in the paper. In our method, Cloude-Pottier polarimetric decomposition components and the eigenvalues of coherency matrix are used as features. Classification is carried out using SVM, and SPL is used to improve the classifier and achieve a stronger generalization capacity. Under SPL paradigm, the classifier learns the easier samples first and gradually involves more difficult samples into the training process. The proposed method achieves the overall classification accuracies of 89.79% on the Flevoland dataset. Such results are comparable with the compared algorithms.
关键词： SVM,PolSAR,SPL,classification
更新于2025-09-10 09:29:36
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[IEEE 2018 2nd International Conference on Engineering Innovation (ICEI) - Bangkok (2018.7.5-2018.7.6)] 2018 2nd International Conference on Engineering Innovation (ICEI) - Diabetic retinopathy fundus image classification using discrete wavelet transform
摘要： Diabetes is an incurable disease which erodes away body slowly, this disease in becoming common and becoming a cause of social distress. The only solution to this problem is early detection of disease and take precautionary measure to keep its effects to minimum. Since it affects various parts of body, the affected organ also includes eye which is very sensitive to any kind of distress. Diabetic Retinopathy effects of diabetes on eye retina, which includes rupturing of retina blood vessels and abnormal growth of blood vessels in retina, which ultimately causes blindness. Diabetic Retinopathy can be identified by examining the retinoscopy images. In this paper, retinoscopy images were processed using wavelet transform. Wavelet coefficients extracted from the images were obtained to identify Diabetic Retinopathy. KNN and SVM were used to classify the retinoscopy images. This papers have shown remarkable improvement as compared to previous studies, with KNN at 98.16 % accuracy and SVM at 97.85 % accuracy.
关键词： sensitivity,specificity,Discrete Wavelet Transform (DWT),accuracy,KNN,Diabetic Retinopathy (DR),histogram equalization,SVM
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Evaluation of Dimensional Reduction Methods on Urban Vegegation Classification Performance Using Hyperspectral Data
摘要： In the context of urban vegetation, hyperspectral imagery allows to discriminate biochemical properties of land surfaces. In this study, we test several dimension reductions to evaluate capacities of hyperspectral sensors to characterize tree families. The goal is to evaluate if a selection of differentiated and uncorrelated vegetation indices is an efficient method to reduce the dimension of hyperspectral images. This method is compared with conventional MNF and ACP approaches, and assessed on tree vegetation classifications performed using SVM classifier on two datasets at 4m and 8m spatial resolution. Results show that MNF combined with SVM classification is the better method to reduce hyperspectral dimension.
关键词： Urban vegetation,dimension reduction,SVM,hyperspectral data
更新于2025-09-09 09:28:46
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Robust automatic classification of benign and malignant microcalcification and mass in digital mammography
摘要： Breast cancer is the most dangerous cancer among women and second mortality among them. Mammography is the efficient methodology used in early finding of breast cancer. However, mammograms requires high amount of skill and there is a possibility of radiologist to misunderstand it. Hence, computer aided diagnosis are used for finding the abnormalities in mammograms. Automated classification of mass and microcalcification system is proposed in this work using NSCT and SVM. The classification of abnormalities is achieved by extracting the microcalcification and mass features from the contourlet coefficients of the image and the results are used as an input to the SVM. The proposed automated system classifies the mammogram as normal or abnormal and result is abnormal, then it classifies the abnormal severity as benign or malignant. The evaluation of the proposed system is conceded on MIAS database. The experimentation result shows that the proposed system contributes improved classification rate.
关键词： mammogram,mass,non-subsampled contourlet transform,SVM,support vector machine,NSCT,benign,microcalcifications,malignant
更新于2025-09-09 09:28:46
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A Portable, Wireless Photoplethysomography Sensor for Assessing Health of Arteriovenous Fistula Using Class-Weighted Support Vector Machine
摘要： A portable, wireless photoplethysomography (PPG) sensor for assessing arteriovenous fistula (AVF) by using class-weighted support vector machines (SVM) was presented in this study. Nowadays, in hospital, AVF are assessed by ultrasound Doppler machines, which are bulky, expensive, complicated-to-operate, and time-consuming. In this study, new PPG sensors were proposed and developed successfully to provide portable and inexpensive solutions for AVF assessments. To develop the sensor, at first, by combining the dimensionless number analysis and the optical Beer Lambert’s law, five input features were derived for the SVM classifier. In the next step, to increase the signal-noise ratio (SNR) of PPG signals, the front-end readout circuitries were designed to fully use the dynamic range of analog-digital converter (ADC) by controlling the circuitries gain and the light intensity of light emitted diode (LED). Digital signal processing algorithms were proposed next to check and fix signal anomalies. Finally, the class-weighted SVM classifiers employed five different kernel functions to assess AVF quality. The assessment results were provided to doctors for diagonosis and detemining ensuing proper treatments. The experimental results showed that the proposed PPG sensors successfully achieved an accuracy of 89.11% in assessing health of AVF and with a type II error of only 9.59%.
关键词： support vector machine (SVM),arteriovenous fistula (AVF),photoplethysmography (PPG) sensor
更新于2025-09-09 09:28:46
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[IEEE 2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI) - Tirunelveli, India (2018.5.11-2018.5.12)] 2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI) - Supervised Method for Acute Lymphoblastic Leukemia Segmentation and Classification Using Image Processing
摘要： Leukemia is a cancer of white blood cells. Mainly leukemias are of two types. Acute leukemia and chronic leukemia. It is depend on which type of white blood cells are affected. Acute leukemia occurred when the born marrow produces too many immature white blood cells. Lymphocytes are type of white blood cell. By the research it can be say that there are so many papers available for segmentation but for classification it is less. In this paper we have proposed a technique for classification of leukemia images into their respective categories weather it is ALL-L1, L2 or L3.
关键词： Classification,k means with CmYk-LAB,white blood cells (WBC),Svm,unsupervised
更新于2025-09-09 09:28:46
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Remote Sensing Inversion of Water Quality Parameters in Longquan Lake Based on PSO-SVR Algorithm
摘要： The paper uses the PSO-SVR algorithm to inverse the water quality parameters based on GF-1 remote sensing image in Longquan lake where is located in Chengdu, Sichuan Province. Longquan Lake is a key drinking water source in Chengdu, so its water quality is very critical. Particle swarm optimization (PSO) optimizes the parameters of the support vector regression (SVR) inversion model to establish the new PSO-SVR inversion model, and PSO can effectively improve the efficiency and the accuracy of the SVR inversion model. At the same, the empirical inversion model was established by using the measured hyperspectral data and concentration of water quality parameters. Comparing with SVR inversion model, PSO-SVR inversion model achieves a better result in the application of suspended solids and Chlorophyll concentration inversion.
关键词： GA-SVM,Support vector machine,Water quality inversion,Genetic algorithm,Suspended solids
更新于2025-09-04 15:30:14
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An Adaptive Entropy Based Scale Invariant Face Recognition Face Altered by Plastic Surgery
摘要： Face recognition is one of the challenging problems which suffer from practical issues like the pose, expression, illumination changes, and aging. Plastic surgery is one among the issues that pose great difficulty in recognizing the faces. The literature has been reported with traditional features and classifiers for recognizing the faces after plastic surgery. This paper presents an adaptive feature descriptor and advanced classifier for plastic surgery face recognition. According to the proposed feature descriptor, firstly an adaptive Gaussian transfer function is determined to perform Adaptive Gaussian Filtering (AGF) for images. Secondly, Adaptive Entropy-based SIFT (AEV-SIFT) features are extracted from the filtered images. Unlike traditional SIFT, the proposed AEV-SIFT extracts the key points based on the entropy of the volume information of the pixel intensities. This provides the least effect on uncertain variations in the face because the entropy is the higher order statistical feature. Further, the classification is performed with variations. In the first variation, support vector machine (SVM) is used as a classifier, whereas the second variation exploits the Deep Learning Network (DLN) for recognizing the faces based on the AEV-SIFT features. The proposed method classifies the plastic surgery face images with the accuracy of 80.15%, sensitivity of 19.75% and specificity of 95%, which are obviously better than the traditional features such as SIFT, V-SIFT, and Principal Component Analysis (PCA).
关键词： plastic surgery,face recognition,adaptive Gaussian kernel,EV-SIFT feature,SVM and DLN classification
更新于2025-09-04 15:30:14
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Automated Cell Selection Using Support Vector Machine for Application to Spectral Nanocytology
摘要： Partial wave spectroscopy (PWS) enables quantification of the statistical properties of cell structures at the nanoscale, which has been used to identify patients harboring premalignant tumors by interrogating easily accessible sites distant from location of the lesion. Due to its high sensitivity, cells that are well preserved need to be selected from the smear images for further analysis. To date, such cell selection has been done manually. This is time-consuming, is labor-intensive, is vulnerable to bias, and has considerable inter- and intraoperator variability. In this study, we developed a classification scheme to identify and remove the corrupted cells or debris that are of no diagnostic value from raw smear images. The slide of smear sample is digitized by acquiring and stitching low-magnification transmission. Objects are then extracted from these images through segmentation algorithms. A training-set is created by manually classifying objects as suitable or unsuitable. A feature-set is created by quantifying a large number of features for each object. The training-set and feature-set are used to train a selection algorithm using Support Vector Machine (SVM) classifiers. We show that the selection algorithm achieves an error rate of 93% with a sensitivity of 95%.
关键词： automated classification,cell selection,SVM,Support Vector Machine,PWS,buccal smear,Partial wave spectroscopy,nanocytology
更新于2025-09-04 15:30:14
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[IEEE 2018 15th European Radar Conference (EuRAD) - Madrid, Spain (2018.9.26-2018.9.28)] 2018 15th European Radar Conference (EuRAD) - Efficient Shaped-Beam Reflectarray Design Using Machine Learning Techniques
摘要： This papers introduces the use of machine learning techniques for an ef?cient design of shaped-beam re?ectarrays considerably accelerating the overall process while providing accurate results. The technique is based on the use of Support Vector Machines (SVMs) for the characterization of the re?ection coef?cient matrix, which provides an ef?cient way for deriving the scattering parameters associated with the unit cell dimensions. In this way, the SVMs are used within the design process to obtain a re?ectarray layout instead of a Full-Wave analysis tool based on Local Periodicity (FW-LP). The accuracy of the SVMs is assessed and the in?uence of the discretization of the angle of incidence is studied. Finally, a considerable acceleration is achieved with regard to the FW-LP and other works in the literature employing Arti?cial Neural Networks.
关键词： Machine Learning,Support Vector Machine (SVM),re?ectarray
更新于2025-09-04 15:30:14