scholarly journals Image data retrieval algorithm based on indexing with spatial features extracted from the image data.

2000 ◽  
Vol 39 (3) ◽  
pp. 14-20
Author(s):  
Hirofumi Etoh ◽  
Takahiro Yamamoto ◽  
Kohei Arai
Keyword(s):  
Image Data ◽  
Data Retrieval ◽  
Retrieval Algorithm ◽  
Spatial Features

scholarly journals Research on Clothing Image Database Retrieval Algorithm Based on Wavelet Transform

2022 ◽  
Vol 2022 ◽  
pp. 1-8
Author(s):  
Xiaoyue Cui
Keyword(s):  
Wavelet Transform ◽  
Image Data ◽  
Data Retrieval ◽  
Image Database ◽  
Fair Value ◽  
Visual Features ◽  
Retrieval Algorithm ◽  
Wavelet Filter ◽  
Database Retrieval ◽  
Retrieval Accuracy

Aiming at the problems of low image data retrieval accuracy and slow retrieval speed in the existing image database retrieval algorithms, this paper designs a clothing image database retrieval algorithm based on wavelet transform. Firstly, it represents the color consistency vector of clothing image, reflects the composition and distribution of image color through color histogram, quantifies the visual features of clothing image, aggregates them into a fixed size representation vector, and uses the Fair Value (FV) model to complete the collection of clothing image data. Then, the size of the clothing image is adjusted by using the size transformation technology, and the clothing pattern is divided into four moments with the same size. On this basis, the clothing image is discretized with the help of Hu invariant moment to complete the preprocessing of clothing image data. Finally, the generating function of wavelet transform is determined, and a cluster of functions is obtained through translation and expansion. The wavelet filter is decomposed into basic modules, and then, the wavelet transform is studied step by step. The clothing image data are regarded as a signal, split, predicted, and updated and input into the wavelet model, and the retrieval research of clothing image database is completed. The experimental results show that the design of the retrieval algorithm is reasonable, the retrieval data accuracy is high, and the retrieval speed is fast.

Tangent height registration method for the Version 14 data retrieval algorithm of the solar occultation sensor ILAS-II

2007 ◽  
Vol 46 (29) ◽  
pp. 7196 ◽  
Author(s):  
Tomoaki Tanaka ◽  
Hideaki Nakajima ◽  
Takafumi Sugita ◽  
Mitsumu K. Ejiri ◽  
Hitoshi Irie ◽  
...  
Keyword(s):  
Data Retrieval ◽  
Retrieval Algorithm ◽  
Registration Method ◽  
Solar Occultation

Validation of TROPOMI nadir ozone profile retrievals: Methodology and first results

2021 ◽  
Author(s):  
Arno Keppens ◽  
Jean-Christopher Lambert ◽  
Daan Hubert ◽  
Steven Compernolle ◽  
Tijl Verhoelst ◽  
...  
Keyword(s):  
Near Infrared ◽  
Stratospheric Ozone ◽  
Estimation Method ◽  
Optimal Estimation ◽  
Data Retrieval ◽  
Atmospheric Composition ◽  
Retrieval Algorithm ◽  
Validation Data ◽  
Ozone Profile ◽  
Early Afternoon

<p>Part of the space segment of EU’s Copernicus Earth Observation programme, the Sentinel-5 Precursor (S5P) mission is dedicated to global and European atmospheric composition measurements of air quality, climate and the stratospheric ozone layer. On board of the S5P early afternoon polar satellite, the imaging spectrometer TROPOMI (TROPOspheric Monitoring Instrument) performs nadir measurements of the Earth radiance within the UV-visible and near-infrared spectral ranges, from which atmospheric ozone profile data are retrieved. Developed at the Royal Netherlands Meteorological Institute (KNMI) and based on the optimal estimation method, TROPOMI’s operational ozone profile retrieval algorithm has recently been upgraded. With respect to early retrieval attempts, accuracy is expected to have improved significantly, also thanks to recent updates of the TROPOMI Level-1b data product. This work reports on the initial validation of the improved TROPOMI height-resolved ozone data in the troposphere and stratosphere, as collected both from the operational S5P Mission Performance Centre/Validation Data Analysis Facility (MPC/VDAF) and from the S5PVT scientific project CHEOPS-5p. Based on the same validation best practices as developed for and applied to heritage sensors like GOME-2, OMI and IASI (Keppens et al., 2015, 2018), the validation methodology relies on the analysis of data retrieval diagnostics – like the averaging kernels’ information content – and on comparisons of TROPOMI data with reference ozone profile measurements. The latter are acquired by ozonesonde, stratospheric lidar, and tropospheric lidar stations performing network operation in the context of WMO's Global Atmosphere Watch and its contributing networks NDACC and SHADOZ. The dependence of TROPOMI’s ozone profile uncertainty on several influence quantities like cloud fraction and measurement parameters like sun and scan angles is examined and discussed. This work concludes with a set of quality indicators, enabling users to verify the fitness-for-purpose of the S5P data.</p>

scholarly journals An Optimization Technique of the 3D Indoor Map Data Based on an Improved Octree Structure

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Xiaomin Yu ◽  
Huiqiang Wang ◽  
Hongwu Lv ◽  
Junqiang Fu
Keyword(s):  
Data Storage ◽  
Optimization Technique ◽  
Data Retrieval ◽  
Retrieval Algorithm ◽  
Storage Structure ◽  
Storage Model ◽  
Indoor Space ◽  
Close Relationship ◽  
Time Requirements ◽  
Map Data

The construction and retrieval of indoor maps are important for indoor positioning and navigation. It is necessary to ensure a good user experience while meeting real-time requirements. Unlike outdoor maps, indoor space is limited, and the relationship between indoor objects is complex which would result in an uneven indoor data distribution and close relationship between the data. A data storage model based on the octree scene segmentation structure was proposed in this paper initially. The traditional octree structure data storage model has been improved so that the data could be backtracked. The proposed method will solve the problem of partition lines within the range of the object data and improve the overall storage efficiency. Moreover, a data retrieval algorithm based on octree storage structure was proposed. The algorithm adopts the idea of “searching for a point, points around the searched point are within the searching range.” Combined with the octree neighbor retrieval methods, the closure constraints are added. Experimental results show that using the improved octree storage structure, the retrieval cost is 1/8 of R-tree. However, by using the neighbor retrieval, it improved the search efficiency by about 27% on average. After adding the closure constraint, the retrieval efficiency increases by 25% on average.

Data Mining Based Intelligent Retrieval Algorithm and its Application

2015 ◽  
Vol 742 ◽  
pp. 340-343
Author(s):  
Chun Ping Wang
Keyword(s):  
Information Retrieval ◽  
Digital Libraries ◽  
Retrieval System ◽  
Data Retrieval ◽  
Retrieval Algorithm ◽  
Accurate Information ◽  
Intelligent Information Retrieval ◽  
Intelligent Information ◽  
Mining Algorithms ◽  
Potential Use

Mathematical model of information retrieval algorithm retrieves digital libraries involved, it is very important to design an algorithm to make the best of the books, in order to extract the required information, including the association rules and classification method for from the database predicting the reader and potential use of fast and accurate information. In this paper, the intelligent data retrieval books mining algorithms to analyze, you can study the books of intelligent retrieval application, until the actual retrieval algorithm to solve the model first developed to meet the requirements, design a library of books intelligent information retrieval system .

Validation of TROPOMI nadir ozone profile retrievals: Methodology and first results

2020 ◽  
Author(s):  
Arno Keppens ◽  
Daan Hubert ◽  
Jean-Christopher Lambert ◽  
Steven Compernolle ◽  
Tijl Verhoelst ◽  
...  
Keyword(s):  
Near Infrared ◽  
Stratospheric Ozone ◽  
Estimation Method ◽  
Optimal Estimation ◽  
Data Retrieval ◽  
Atmospheric Composition ◽  
Retrieval Algorithm ◽  
Validation Data ◽  
Ozone Profile ◽  
Early Afternoon

<p>Part of the space segment of EU’s Copernicus Earth Observation programme, the Sentinel-5 Precursor (S5P) mission is dedicated to global and European atmospheric composition measurements of air quality, climate and the stratospheric ozone layer. On board of the S5P early afternoon polar satellite, the imaging spectrometer TROPOMI (TROPOspheric Monitoring Instrument) performs nadir measurements of the Earth radiance within the UV-visible and near-infrared spectral ranges, from which atmospheric ozone profile data are retrieved. Developed at the Royal Netherlands Meteorological Institute (KNMI) and based on the optimal estimation method, TROPOMI’s operational ozone profile retrieval algorithm has recently been upgraded. With respect to early retrieval attempts, accuracy is expected to have improved significantly, also thanks to recent updates of the TROPOMI Level-1b data product. This work reports on the initial validation of the improved TROPOMI height-resolved ozone data in the troposphere and stratosphere, as collected both from the operational S5P Mission Performance Centre/Validation Data Analysis Facility (MPC/VDAF) and from the S5PVT scientific project CHEOPS-5p. Based on the same validation best practices as developed for and applied to heritage sensors like GOME-2, OMI and IASI (Keppens et al., 2015, 2018), the validation methodology relies on the analysis of data retrieval diagnostics – like the averaging kernels’ information content – and on comparisons of TROPOMI data with reference ozone profile measurements. The latter are acquired by ozonesonde, stratospheric lidar, and tropospheric lidar stations performing network operation in the context of WMO's Global Atmosphere Watch and its contributing networks NDACC and SHADOZ. The dependence of TROPOMI’s ozone profile uncertainty on several influence quantities like cloud fraction and measurement parameters like sun and scan angles is examined and discussed. This work concludes with a set of quality indicators enabling users to verify the fitness-for-purpose of the S5P data.</p>

scholarly journals Gaze Tracking Based on Concatenating Spatial-Temporal Features

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 545
Author(s):  
Bor-Jiunn Hwang ◽  
Hui-Hui Chen ◽  
Chaur-Heh Hsieh ◽  
Deng-Yu Huang
Keyword(s):  
Short Term Memory ◽  
Image Data ◽  
Training Model ◽  
Point Estimation ◽  
Training Data ◽  
Gaze Tracking ◽  
Spatial Features ◽  
Temporal Features ◽  
Lstm Network ◽  
The Impact

Based on experimental observations, there is a correlation between time and consecutive gaze positions in visual behaviors. Previous studies on gaze point estimation usually use images as the input for model trainings without taking into account the sequence relationship between image data. In addition to the spatial features, the temporal features are considered to improve the accuracy in this paper by using videos instead of images as the input data. To be able to capture spatial and temporal features at the same time, the convolutional neural network (CNN) and long short-term memory (LSTM) network are introduced to build a training model. In this way, CNN is used to extract the spatial features, and LSTM correlates temporal features. This paper presents a CNN Concatenating LSTM network (CCLN) that concatenates spatial and temporal features to improve the performance of gaze estimation in the case of time-series videos as the input training data. In addition, the proposed model can be optimized by exploring the numbers of LSTM layers, the influence of batch normalization (BN) and global average pooling layer (GAP) on CCLN. It is generally believed that larger amounts of training data will lead to better models. To provide data for training and prediction, we propose a method for constructing datasets of video for gaze point estimation. The issues are studied, including the effectiveness of different commonly used general models and the impact of transfer learning. Through exhaustive evaluation, it has been proved that the proposed method achieves a better prediction accuracy than the existing CNN-based methods. Finally, 93.1% of the best model and 92.6% of the general model MobileNet are obtained.

Estimating melt pond bathymetry from aerial images using photogrammetry

2021 ◽  
Author(s):  
Niels Fuchs ◽  
Marcel König ◽  
Gerit Birnbaum
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Experimental Studies ◽  
Image Data ◽  
Arctic Sea Ice ◽  
Global Climate Models ◽  
The Arctic ◽  
Retrieval Algorithm ◽  
Mean Deviation ◽  
Ice Surface

<p>Melt ponds play a key role for the summery energy budget of the Arctic sea-ice surface. Observational data that enable an integrated understanding and improved formulation of the thermodynamic and hydrological pond system in global climate models are spatially and temporally limited.</p><p>Previous studies of shallow water bathymetry of riverbeds and lakes, experimental studies above sea ice and increasing availability of high-resolution aerial sea ice imagery motivated us to investigate the possibilities to derive pond bathymetry from photogrammetric multi-view reconstruction of the summery ice surface topography.</p><p>Based on dedicated flight grids and simple assumptions we were able to obtain pond depth with a mean deviation of 3.5 cm compared to manual in situ observations. The method is independent of pond color and sky conditions, which is an advantage over recently developed radiometric retrieval methods.</p><p>We present the retrieval algorithm, including requirements to the data recording and survey planning, and a correction method for refraction at the air— pond interface. In addition, we show how the retrieved elevation model synergize with the initial image data to retrieve the water level of each individual pond from the visually determined pond exterior.</p><p>The study points out the great potential to derive geometric and radiometric properties of the sea-ice surface emerging from the increasingly available image data recorded from UAVs or aircraft.</p>

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