- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Survey of Object-Based Data Reduction Techniques in Observational Astronomy
摘要： Dealing with astronomical observations represents one of the most challenging areas of big data analytics. Besides huge variety of data types, dynamics related to continuous data flow from multiple sources, handling enormous volumes of data is essential. This paper provides an overview of methods aimed at reducing both the number of features/attributes as well as data instances. It concentrates on data mining approaches not related to instruments and observation tools instead working on processed object-based data. The main goal of this article is to describe existing datasets on which algorithms are frequently tested, to characterize and classify available data reduction algorithms and identify promising solutions capable of addressing present and future challenges in astronomy.
关键词： feature extraction,astronomy,dimensionality reduction,big data,data condensation
更新于2025-09-23 15:23:52
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[IEEE 2017 International Conference on Current Trends in Computer, Electrical, Electronics and Communication (CTCEEC) - Mysore, India (2017.9.8-2017.9.9)] 2017 International Conference on Current Trends in Computer, Electrical, Electronics and Communication (CTCEEC) - An Approach of Abnormality Detection for Diabetic Retinopathy using ANN SVM
摘要： Glaucoma is the diagnosis given to a group of ocular conditions that contribute to the loss of retinal nerve fibers with a corresponding loss of vision. Glaucoma is the major cause of blindness in people above the age of 40. The Intra Ocular Pressure (IOP) increases because of the malfunction of the drainage structure of the eyes leading to Glaucoma. In this paper three methods-Gray Level Difference Method using ANN classifier, Stochastic watershed method using SVM classifier and Pearson R Correlation Method are proposed, which automatically detect Glaucoma disease in the human eye from the fundus database images. The three strategies utilized for retinal abnormality identification are looked at in light of the execution measurements- exactness and blunder rate. The SVM classifier gives more precise outcomes contrasted with other two procedures.
关键词： Stochastic Watershed,GLDM feature extraction,Pearson R Correlation
更新于2025-09-23 15:23:52
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[IEEE 2018 25th IEEE International Conference on Image Processing (ICIP) - Athens, Greece (2018.10.7-2018.10.10)] 2018 25th IEEE International Conference on Image Processing (ICIP) - Tree-Shaped Sampling Based Hybrid Multi-Scale Feature Extraction for Texture Classification
摘要： Efficiency, distinctiveness and robustness are three main goals for feature extractors in application of texture classification. In this paper, a new feature extractor is designed which aims to achieve these three goals simultaneously. The contributions are threefold. Firstly, a tree-shaped multi-scale sampling structure is proposed to acquire points distributed along two circles and one octagon. Secondly, four histogram vectors are obtained by quantizing the sampling values through a hybrid strategy. In order to suppress the noise, mean filtering is used as a preprocessing step and the four vectors are concatenated to form the discriminant vector. Thirdly, experiments are conducted on different datasets with several well-known feature extractors. The results show that the proposed method improves the classification accuracy effectively and robustly, while has a moderate complexity. The source code is available at: https://github.com/madd2014/TSSHM.
关键词： texture classification,Feature extraction,multi-scale structure,tree-shaped sampling
更新于2025-09-23 15:23:52
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Quaternion-Based Multiscale Analysis for Feature Extraction of Hyperspectral Images
摘要： This paper proposes a new method called multiscale quaternion Weber local descriptor histogram (MQWLDH) for feature extraction of hyperspectral images (HSIs), which is used to model spatial information based on the corresponding spectral features. The proposed method first transforms spectral data into an orthogonal space using principal component analysis, and extracts the first three principal components (PCs) based on the maximum variance theory. Then construct the MQWLDH to extract spatial features based on those first three PCs. The proposed method uses the algebraic structure of quaternions to unify the process of processing the first three PCs, which reduces the computational cost and the dimensionality of the extracted spatial feature vector. Moreover, the constructed quaternion Weber local descriptor effectively characterizes the variations of each pixel neighborhood and detects the edges of HSIs. To capture more intrinsic spatial information contained in homogeneous regions of different sizes and shapes, multiscale feature histograms are constructed. Finally, a feature fusion framework is proposed to fuse spectral and spatial features, so that spectral information can be fully utilized. The experimental results on three HSI data sets demonstrate that the proposed method provides effective features to different classifiers and achieves excellent classification performance.
关键词： multiscale feature histograms.,principal component analysis (PCA),Feature extraction,quaternion Weber local descriptor (QWLD)
更新于2025-09-23 15:22:29
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[IEEE 2018 International Conference on Control, Power, Communication and Computing Technologies (ICCPCCT) - Kannur (2018.3.23-2018.3.24)] 2018 International Conference on Control, Power, Communication and Computing Technologies (ICCPCCT) - Identification of Melanoma in Dermoscopy Images Using Image Processing Algorithms
摘要： Skin cancer is the most common of all human cancers and is always misunderstood with other kind of skin diseases, so accurate early detection of skin cancer is essential. The main objective of this paper is to segment the lesion and identify melanoma from dermoscopy images. A total of 170 dermoscopy images are used in this research. Firstly, the input images are enhanced for better processing then, the lesion portion is segmented from the enhanced image by two methods 1.Otsu thresholding 2.Morphological operations. The descriptive features are extracted from the segmented lesion. The extracted feature values are used to compute the Total Dermatascopy Score (TDS), which is used to find the presence or absence of melanoma in dermoscopy images. Classification accuracy is calculated to assess the performance of the proposed algorithm.
关键词： Dermoscopy,Segmentation,Total Dermatoscopy Score(TDS),Feature Extraction,Melanoma,Skin cancer
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Sparse and Smooth Feature Extraction for Hyperspectral Imagery
摘要： In this paper, a hyperspectral feature extraction (FE) method called sparse and smooth low-rank analysis (SSLRA) is proposed. First, we propose a new low-rank model for hyperspectral images (HSIs). In the new model, HSI is decomposed into smooth and sparse unknown features which live in an unknown orthogonal subspace. Then, the sparse and smooth features are simultaneously estimated using a non-convex constrained penalized cost function. In the experiments, SSLRA is applied on a real HSI and the smooth features extracted are used for the HSI classification. The results confirm improvements in classification accuracies compared to state-of-the-art FE methods.
关键词： regularization,Feature extraction,sparsity,low-rank model,total variation,hyperspectral image
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Extraction of Structural and Mineralogical Features from Hyperspectral Drill-Core Scans
摘要： For vein hosted mineralization such as encountered in porphyry systems, the documentation of the main alteration assemblages associated with specific vein generations is essential in understanding the geometry of the mineralized body. Hence, mineralogical and structural information are highly relevant for characterizing the system. In this paper, we present an approach for the extraction of both mineralogical and structural information from hyperspectral scans. We propose a parallel framework which includes a typical mineral mapping technique for the extraction of mineralogical information as well as a ridge detection method, for the extraction of veins, applied on mineral abundance maps. In the proposed framework, the abundance maps are obtained from hyperspectral VNIR-SWIR drill-core scans using a linear spectral unmixing technique. Drill cores hosting porphyry stockwork type mineralization are used for the evaluation of the proposed technique and the experimental results show that the method offers a tool for accurately characterizing the mineralized body.
关键词： Core scanning,feature extraction,hyperspectral imaging,mineral mapping,image segmentation
更新于2025-09-23 15:22:29
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[IEEE 2018 41st International Conference on Telecommunications and Signal Processing (TSP) - Athens, Greece (2018.7.4-2018.7.6)] 2018 41st International Conference on Telecommunications and Signal Processing (TSP) - Improvement of Optic Disc Localization using Gabor Filters
摘要： The paper presents a supervised technique for the detection and localization of the optic disc (OD) in retinal images. The proposed processing technique is based on Discrete Fourier Transform (DFT) and Gabor filters (GFs). The algorithm of image patch processing and classification has two phases: the learning phase for the OD class definition and the testing phase for the patch processing and classification. Two features are used to check if a patch contains the OD: the magnitude and the phase values computed on the result of the convolution between the DFT of the patch and the bank of Gabor filters. Over 100 images from MESSIDOR database were tested and comparing with other similar works. The proposed algorithm gave better results in terms of accuracy of the OD localization for all types of OD.
关键词： Gabor filter,feature extraction,Discrete Fourier Transform,patch decomposition,optic disc localization
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Feature Design for Classification from Tomosar Data
摘要： While previous work primarily focused on using Tomographic Synthetic Aperture Radar (TomoSAR) data to analyze the 3D structure of the imaged scene, we study its potential for the generation of semantic land cover maps in a supervised framework. We extract different features from the covariance matrices of a tomographic image stack as well as from the tomograms computed by tomographic focusing. To assess the impact of our approach, we compare our results to classification maps obtained from a fully polarimetric image. We show that it is possible to outperform classification results from polarimetric data by carefully designing hand-crafted features which can be extracted either from multi-baseline single polarization covariance matrices or from tomograms obtained after tomographic focusing. Our experiments show a significant gain in the classification accuracy, especially on challenging classes such as heterogeneous city and road.
关键词： machine learning,Synthetic Aperture Radar,feature extraction,tomography
更新于2025-09-23 15:22:29
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[IEEE 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) - Chongqing (2018.6.27-2018.6.29)] 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) - Vision-Based Simultaneous Localization and Mapping on Lunar Rover
摘要： With the development of lunar exploration technology, vision-based navigation technology has become a research focus in the field of lunar rover. This paper proposes an image-based method for localization and mapping with a lunar rover. The motion of the camera represents the movement of the lunar rover. Based on the images acquired by the camera, the relative pose of the camera and 3D landmarks are obtained using the multi-view geometry and the bundle adjustment optimization methods. The prior knowledge of the lunar rover movement is not required. In addition, this paper also proposes a grid-based feature extraction method to solve the problem of uneven feature extraction and mis-matching. The algorithm in this paper has been tested in real time in a large image dataset. Finally, the error analysis of the estimated pose obtained from the experiment and the real trajectory proves the excellent performance of the algorithm.
关键词： localization and mapping,lunar exploration,lunar rover,feature extraction,pose estimation
更新于2025-09-23 15:22:29