Probabilistic retrieval: thresholding for automatic filtering

1999 ◽  
Author(s):  
S. Robertson
Keyword(s):  
Probabilistic Retrieval ◽  
Automatic Filtering

scholarly journals SPARSE POINT CLOUD FILTERING BASED ON COVARIANCE FEATURES

2019 ◽  
Vol XLII-2/W15 ◽  
pp. 465-472 ◽  
Author(s):  
E. M. Farella ◽  
A. Torresani ◽  
F. Remondino
Keyword(s):  
Point Cloud ◽  
Bundle Adjustment ◽  
Geometric Features ◽  
Local Behaviour ◽  
High Entropy ◽  
3D Data ◽  
Automatic Filtering ◽  
Noisy 3D Data ◽  
Low Entropy ◽  
Filtering Procedure

<p><strong>Abstract.</strong> This work presents an extended photogrammetric pipeline aimed to improve 3D reconstruction results. Standard photogrammetric pipelines can produce noisy 3D data, especially when images are acquired with various sensors featuring different properties. In this paper, we propose an automatic filtering procedure based on some geometric features computed on the sparse point cloud created within the bundle adjustment phase. Bad 3D tie points and outliers are detected and removed, relying on micro and macro-clusters analyses. Clusters are built according to the prevalent dimensionality class (1D, 2D, 3D) assigned to low-entropy points, and corresponding to the main linear, planar o scatter local behaviour of the point cloud. While the macro-clusters analysis removes smallsized clusters and high-entropy points, in the micro-clusters investigation covariance features are used to verify the inner coherence of each point to the assigned class. Results on heritage scenarios are presented and discussed.</p>

FRACTAL ANALYSIS AS A TOOL FOR AUTOMATIC DETECTION OF ARTIFACTS IN ELECTROENCEPHALOGRAM RECORDS

2021 ◽  
pp. 72-76
Author(s):  
Л.Д. Егорова ◽  
Л.А. Казаковцев
Keyword(s):  
Data Processing ◽  
Fractal Analysis ◽  
Hurst Exponent ◽  
Automatic Detection ◽  
Eeg Signal ◽  
Informative Feature ◽  
Processing Algorithms ◽  
Automatic Filtering ◽  
Intelligent Data Processing ◽  
Data Processing Algorithms

В статье обсуждается применение методов фрактального анализа для решения задачи автоматической фильтрации сигнала ЭЭГ от артефактов различной природы. Изучается возможность использования показателя Херста в качестве информативного признака для алгоритмов интеллектуальной обработки данных. The article discusses the possibility of using fractal analysis to solve the problem of automatic filtering of the EEG signal from artifacts of various nature. The possibility of using the Hurst exponent as an informative feature for intelligent data processing algorithms is investigated

scholarly journals Smart filtering of phase residues in noisy wrapped holograms

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Behnam Tayebi ◽  
Farnaz Sharif ◽  
Jae-Ho Han
Keyword(s):  
Iterative Algorithm ◽  
Intensity Distribution ◽  
Three Dimensional ◽  
Phase Unwrapping ◽  
Spatial Frequencies ◽  
Phase Map ◽  
Filtering Technique ◽  
Automatic Filtering ◽  
Simple Phase

Abstract Phase unwrapping is one of the major challenges in multiple branches of science that extract three-dimensional information of objects from wrapped signals. In several applications, it is important to extract the unwrapped information with minimal signal resolution degradation. However, most of the denoising techniques for unwrapping are designed to operate on the entire phase map to remove a limited number of phase residues, and therefore they significantly degrade critical information contained in the image. In this paper, we present a novel, smart, and automatic filtering technique for locally minimizing the number of phase residues in noisy wrapped holograms, based on the phasor average filtering (PAF) of patches around each residue point. Both patch sizes and PAF filters are increased in an iterative algorithm to minimize the number of residues and locally restrict the artifacts caused by filtering to the pixels around the residue pixels. Then, the improved wrapped phase can be unwrapped using a simple phase unwrapping technique. The feasibility of our method is confirmed by filtering, unwrapping, and enhancing the quality of a noisy hologram of neurons; the intensity distribution of the spatial frequencies demonstrates a 40-fold improvement, with respect to previous techniques, in preserving the higher frequencies.

Automatic filtering of ERT monitoring data in mountain permafrost

2013 ◽  
Vol 11 (4) ◽  
pp. 423-434 ◽  
Author(s):  
Etienne Rosset ◽  
Christin Hilbich ◽  
Sina Schneider ◽  
Christian Hauck
Keyword(s):  
Monitoring Data ◽  
Mountain Permafrost ◽  
Automatic Filtering
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