Ground-truth information agnostic deep dehazing network for C919 aircraft image

2021 ◽  
Author(s):  
Wenjun Wang ◽  
Yunhao Zhang ◽  
Ting-Bing Xu ◽  
Zhenzhong Wei
Keyword(s):  
Ground Truth ◽  
Ground Truth Information

BYANJON: A Ground Truth Preparation System for Online Handwritten Bangla Documents

2021 ◽  
Vol 20 (6) ◽  
pp. 1-16
Author(s):  
Shibaprasad Sen ◽  
Ankan Bhattacharyya ◽  
Ram Sarkar ◽  
Kaushik Roy
Keyword(s):  
Extraction Procedure ◽  
Ground Truth ◽  
Word Segmentation ◽  
Text Line ◽  
Line Extraction ◽  
Manual Intervention ◽  
Ground Truth Generation ◽  
Class Labels ◽  
Text Line Extraction ◽  
Ground Truth Information

The work reported in this article deals with the ground truth generation scheme for online handwritten Bangla documents at text-line, word, and stroke levels. The aim of the proposed scheme is twofold: firstly, to build a document level database so that future researchers can use the database to do research in this field. Secondly, the ground truth information will help other researchers to evaluate the performance of their algorithms developed for text-line extraction, word extraction, word segmentation, stroke recognition, and word recognition. The reported ground truth generation scheme starts with text-line extraction from the online handwritten Bangla documents, then words extraction from the text-lines, and finally segmentation of those words into basic strokes. After word segmentation, the basic strokes are assigned appropriate class labels by using modified distance-based feature extraction procedure and the MLP ( Multi-layer Perceptron ) classifier. The Unicode for the words are then generated from the sequence of stroke labels. XML files are used to store the stroke, word, and text-line levels ground truth information for the corresponding documents. The proposed system is semi-automatic and each step such as text-line extraction, word extraction, word segmentation, and stroke recognition has been implemented by using different algorithms. Thus, the proposed ground truth generation procedure minimizes huge manual intervention by reducing the number of mouse clicks required to extract text-lines, words from the document, and segment the words into basic strokes. The integrated stroke recognition module also helps to minimize the manual labor needed to assign appropriate stroke labels. The freely available and can be accessed at https://byanjon.herokuapp.com/ .

scholarly journals Identifying Doppler Velocity Contamination Caused by Migrating Birds. Part II: Bayes Identification and Probability Tests

2005 ◽  
Vol 22 (8) ◽  
pp. 1114-1121 ◽  
Author(s):  
Shun Liu ◽  
Qin Xu ◽  
Pengfei Zhang
Keyword(s):  
Elevation Angle ◽  
Ground Truth ◽  
Single Parameter ◽  
Statistical Decision Theory ◽  
Doppler Velocity ◽  
Statistical Decision ◽  
Wrong Decision ◽  
Migrating Birds ◽  
Bayesian Statistical Decision Theory ◽  
Ground Truth Information

Abstract Based on the Bayesian statistical decision theory, a probabilistic quality control (QC) technique is developed to identify and flag migrating-bird-contaminated sweeps of level II velocity scans at the lowest elevation angle using the QC parameters presented in Part I. The QC technique can use either each single QC parameter or all three in combination. The single-parameter QC technique is shown to be useful for evaluating the effectiveness of each QC parameter based on the smallness of the tested percentages of wrong decision by using the ground truth information (if available) or based on the smallness of the estimated probabilities of wrong decision (if there is no ground truth information). The multiparameter QC technique is demonstrated to be much better than any of the three single-parameter QC techniques, as indicated by the very small value of the tested percentages of wrong decision for no-flag decisions (not contaminated by migrating birds). Since the averages of the estimated probabilities of wrong decision are quite close to the tested percentages of wrong decision, they can provide useful information about the probability of wrong decision when the multiparameter QC technique is used for real applications (with no ground truth information).

scholarly journals Gaussian Processes for Vegetation Parameter Estimation from Hyperspectral Data with Limited Ground Truth

2019 ◽  
Vol 11 (13) ◽  
pp. 1614 ◽  
Author(s):  
Utsav B. Gewali ◽  
Sildomar T. Monteiro ◽  
Eli Saber
Keyword(s):  
Gaussian Processes ◽  
Ground Truth ◽  
Joint Modeling ◽  
Hyperspectral Data ◽  
Sample Collection ◽  
Spectral Correlation ◽  
Covariance Functions ◽  
Vegetation Parameter ◽  
Vegetation Parameters ◽  
Ground Truth Information

An important application of airborne- and satellite-based hyperspectral imaging is the mapping of the spatial distribution of vegetation biophysical and biochemical parameters in an environment. Statistical models, such as Gaussian processes, have been very successful for modeling vegetation parameters from captured spectra, however their performance is highly dependent on the amount of available ground truth. This is a problem because it is generally expensive to obtain ground truth information due to difficulties and costs associated with sample collection and analysis. In this paper, we present two Gaussian processes based approaches for improving the accuracy of vegetation parameter retrieval when ground truth is limited. The first is the adoption of covariance functions based on well-established metrics, such as, spectral angle and spectral correlation, which are known to be better measures of similarity for spectral data owing to their resilience to spectral variabilities. The second is the joint modeling of related vegetation parameters by multitask Gaussian processes so that the prediction accuracy of the vegetation parameter of interest can be improved with the aid of related vegetation parameters for which a larger set of ground truth is available. We experimentally demonstrate the efficacy of the proposed methods against existing approaches on three real-world hyperspectral datasets and one synthetic dataset.

scholarly journals Outliers in Smartphone Sensor Data Reveal Outliers in Daily Happiness

2021 ◽  
Vol 5 (1) ◽  
pp. 1-19
Author(s):  
Teodora Sandra Buda ◽  
Mohammed Khwaja ◽  
Aleksandar Matic
Keyword(s):  
Emotional State ◽  
Extreme Values ◽  
Ground Truth ◽  
Well Being ◽  
Sensor Data ◽  
New Approach ◽  
Performance Improvements ◽  
Limited Sensing ◽  
Smartphone Sensor ◽  
Ground Truth Information

Enabling smartphones to understand our emotional well-being provides the potential to create personalised applications and highly responsive interfaces. However, this is by no means a trivial task - subjectivity in reporting emotions impacts the reliability of ground-truth information whereas smartphones, unlike specialised wearables, have limited sensing capabilities. In this paper, we propose a new approach that advances emotional state prediction by extracting outlier-based features and by mitigating the subjectivity in capturing ground-truth information. We utilised this approach in a distinctive and challenging use case - happiness detection - and we demonstrated prediction performance improvements of up to 13% in AUC and 27% in F-score compared to the traditional modelling approaches. The results indicate that extreme values (i.e. outliers) of sensor readings mirror extreme values in the reported happiness levels. Furthermore, we showed that this approach is more robust in replicating the prediction model in completely new experimental settings.

scholarly journals Performance Evaluation of the Highway Radar Occupancy Grid

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2177
Author(s):  
Jakub Porębski ◽  
Krzysztof Kogut
Keyword(s):  
Ground Truth ◽  
The Novel ◽  
Analysis Method ◽  
Mapping Algorithm ◽  
Multiple Sensors ◽  
Occupancy Grid ◽  
Occupancy Grid Mapping ◽  
Ground Truth Information ◽  
Real Vehicle

The quality of environmental perception is crucial for automated vehicle capabilities. In order to ensure the required accuracy, the occupancy grid mapping algorithm is often utilised to fuse data from multiple sensors. This paper focuses on the radar-based occupancy grid for highway applications and describes how to measure effectively the quality of the occupancy map. The evaluation was performed using the novel grid pole-like object analysis method. The proposed assessment is versatile and can be applied without detailed ground truth information. The evaluation was tested with a simulation and real vehicle experiments on the highway.

scholarly journals Digital In Situ Data Collection in Earth Observation, Monitoring and Agriculture—Progress towards Digital Agriculture

2022 ◽  
Vol 14 (2) ◽  
pp. 393
Author(s):  
Mike Teucher ◽  
Detlef Thürkow ◽  
Philipp Alb ◽  
Christopher Conrad
Keyword(s):  
Real Time ◽  
Precision Agriculture ◽  
Multiple Scales ◽  
Expert Knowledge ◽  
Ground Truth ◽  
Agricultural Management ◽  
Digital Workflow ◽  
Web Mapping ◽  
Use Of Resources ◽  
Ground Truth Information

Digital solutions in agricultural management promote food security and support the sustainable use of resources. As a result, remote sensing (RS) can be seen as an innovation for the fast generation of reliable information for agricultural management. Near real-time processed RS data can be used as a tool for decision making on multiple scales, from subplot to the global level. This high potential is not yet fully applied, due to often limited access to ground truth information, which is crucial for the development of transferable applications and acceptance. In this study we present a digital workflow for the acquisition, processing and dissemination of agroecological information based on proprietary and open-source software tools with state-of-the-art web-mapping technologies. Data is processed in near real-time and thus can be used as ground truth information to enhance quality and performance of RS-based products. Data is disseminated by easy-to-understand visualizations and download functionalities for specific application levels to serve specific user needs. It thus can increase expert knowledge and can be used for decision support at the same time. The fully digital workflow underpins the great potential to facilitate quality enhancement of future RS products in the context of precision agriculture by safeguarding data quality. The generated FAIR (findable, accessible, interoperable, reusable) datasets can be used to strengthen the relationship between scientists, initiatives and stakeholders.

scholarly journals IMPACT ASSESSMENT OF IMAGE FEATURE EXTRACTORS ON THE PERFORMANCE OF SLAM SYSTEMS

2015 ◽  
Vol 2 (2) ◽  
pp. 45-50 ◽  
Author(s):  
Taihú Pire ◽  
Thomas Fischer ◽  
Jan Faigl
Keyword(s):  
Ground Truth ◽  
Mapping Method ◽  
Image Feature ◽  
Feature Descriptor ◽  
Visual Slam ◽  
Feature Detector ◽  
Trade Off ◽  
Feature Detectors ◽  
Localization Precision ◽  
Ground Truth Information

This work evaluates an impact of image feature extractors on the performance of a visual SLAM method in terms of pose accuracy and computational requirements. In particular, the S-PTAM (Stereo Parallel Tracking and Mapping) method is considered as the visual SLAM framework for which both the feature detector and feature descriptor are parametrized. The evaluation was performed with a standard dataset with ground-truth information and six feature detectors and four descriptors. The presented results indicate that the combination of the GFTT detector and the BRIEF descriptor provides the best trade-off between the localization precision and computational requirements among the evaluated combinations of the detectors and descriptors.

scholarly journals A simple machine learning based framework for processing the inline inspection data of subsea pipelines

2021 ◽  
Vol 1201 (1) ◽  
pp. 012050
Author(s):  
Z Liu ◽  
S S Dahl ◽  
E S Larsen ◽  
Z Yang
Keyword(s):  
Machine Learning ◽  
Wall Thickness ◽  
Ground Truth ◽  
Grid Size ◽  
Machine Learning Method ◽  
Simple Machine ◽  
Machine Learning Model ◽  
Inspection Data ◽  
Subsea Pipelines ◽  
Ground Truth Information

Abstract This paper presents a simple machine learning based framework for diagnosing the inline inspection data (ILI) of subsea pipelines. ILI data are obtained by intelligent pigging devices operating along subsea pipelines. The wall thickness (WT) and standoff distance (SO) are collected by the sensors installed on the pigging, which are normally in the format of 2D arrays. There are many uncertainties for the ILI data collected from the offshore survey. An attempt was made to apply the machine learning method to diagnose the uncertainties. A convolutional neural network (CNN) is used, the ILI data are discretized and processed in 64x64 grid size. Fabricated training datasets were made for training the machine learning model since the ground truth information (actual corroded wall thickness) is hardly known in this case. The trained model was successfully. It is demonstrated that certain corrosion patterns have been recognized by the trained model. Comparisons were performed between the new method and traditional methods with case studies on real ILI data. The validity of the methodology was discussed.

Sign in / Sign up

Export Citation Format

Share Document