Fusion of Depth Data with Standard Cameras Data

2012 ◽  
pp. 69-91
Author(s):  
Carlo Dal Mutto ◽  
Pietro Zanuttigh ◽  
Guido M. Cortelazzo
Keyword(s):  
Depth Data

scholarly journals Iranian kinect face database (IKFDB): a color-depth based face database collected by kinect v.2 sensor

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Seyed Muhammad Hossein Mousavi ◽  
S. Younes Mirinezhad
Keyword(s):  
Neural Network ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Depth Image ◽  
Sensor Technology ◽  
Support Vector ◽  
Expression Recognition ◽  
Face Database ◽  
Depth Data ◽  
Color Depth

AbstractThis study presents a new color-depth based face database gathered from different genders and age ranges from Iranian subjects. Using suitable databases, it is possible to validate and assess available methods in different research fields. This database has application in different fields such as face recognition, age estimation and Facial Expression Recognition and Facial Micro Expressions Recognition. Image databases based on their size and resolution are mostly large. Color images usually consist of three channels namely Red, Green and Blue. But in the last decade, another aspect of image type has emerged, named “depth image”. Depth images are used in calculating range and distance between objects and the sensor. Depending on the depth sensor technology, it is possible to acquire range data differently. Kinect sensor version 2 is capable of acquiring color and depth data simultaneously. Facial expression recognition is an important field in image processing, which has multiple uses from animation to psychology. Currently, there is a few numbers of color-depth (RGB-D) facial micro expressions recognition databases existing. With adding depth data to color data, the accuracy of final recognition will be increased. Due to the shortage of color-depth based facial expression databases and some weakness in available ones, a new and almost perfect RGB-D face database is presented in this paper, covering Middle-Eastern face type. In the validation section, the database will be compared with some famous benchmark face databases. For evaluation, Histogram Oriented Gradients features are extracted, and classification algorithms such as Support Vector Machine, Multi-Layer Neural Network and a deep learning method, called Convolutional Neural Network or are employed. The results are so promising.

scholarly journals Assimilation of D-InSAR snow depth data by an ensemble Kalman filter

2021 ◽  
Vol 14 (6) ◽  
Author(s):  
Jinming Yang ◽  
Chengzhi Li
Keyword(s):  
Remote Sensing ◽  
Kalman Filter ◽  
Water Resource ◽  
Ensemble Kalman Filter ◽  
Snow Depth ◽  
Resource Assessment ◽  
Measured Data ◽  
Hydrological Process ◽  
Depth Data ◽  
Water Resource Assessment

AbstractSnow depth mirrors regional climate change and is a vital parameter for medium- and long-term numerical climate prediction, numerical simulation of land-surface hydrological process, and water resource assessment. However, the quality of the available snow depth products retrieved from remote sensing is inevitably affected by cloud and mountain shadow, and the spatiotemporal resolution of the snow depth data cannot meet the need of hydrological research and decision-making assistance. Therefore, a method to enhance the accuracy of snow depth data is urgently required. In the present study, three kinds of snow depth data which included the D-InSAR data retrieved from the remote sensing images of Sentinel-1 synthetic aperture radar, the automatically measured data using ultrasonic snow depth detectors, and the manually measured data were assimilated based on ensemble Kalman filter. The assimilated snow depth data were spatiotemporally consecutive and integrated. Under the constraint of the measured data, the accuracy of the assimilated snow depth data was higher and met the need of subsequent research. The development of ultrasonic snow depth detector and the application of D-InSAR technology in snow depth inversion had greatly alleviated the insufficiency of snow depth data in types and quantity. At the same time, the assimilation of multi-source snow depth data by ensemble Kalman filter also provides high-precision data to support remote sensing hydrological research, water resource assessment, and snow disaster prevention and control program.

scholarly journals Deep learning-based single image face depth data enhancement

2021 ◽  
pp. 103247
Author(s):  
Torsten Schlett ◽  
Christian Rathgeb ◽  
Christoph Busch
Keyword(s):  
Deep Learning ◽  
Single Image ◽  
Depth Data

scholarly journals Understanding the Salt-Dependent Outcome of Glycine Polymorphic Nucleation

Pharmaceutics ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 262
Author(s):  
Guangjun Han ◽  
Pui Shan Chow ◽  
Reginald B. H. Tan
Keyword(s):  
Rare Case ◽  
Depth Data ◽  
Data Analyses ◽  
Primary Nucleation ◽  
Bulk Solutions

The salt-dependent polymorphs of glycine crystals formed from bulk solutions have been a longstanding riddle. In this study, in order to shed fresh light, we studied the effects of seven common salts on primary nucleation of the metastable α-glycine and the stable γ-glycine. Our nucleation experiments and in-depth data analyses enabled us to reveal that (NH4)2SO4, NaCl and KNO3, in general, promote γ-glycine primary nucleation very significantly while simultaneously inhibiting α-glycine primary nucleation, thereby explaining why these three salts induce γ-glycine readily. In comparison, Ca(NO3)2 and MgSO4 also promote γ-glycine and inhibit α-glycine primary nucleation but not sufficiently to induce γ-glycine. More interestingly, Na2SO4 and K2SO4 promote not only γ-glycine but also α-glycine primary nucleation, which is unexpected and presents a rare case where a single additive promotes the nucleation of both polymorphs. As a result, the promoting effects of Na2SO4 and K2SO4 on γ-glycine do not enable γ-glycine nucleation to be more competitive than α-glycine nucleation, with γ-glycine failing to appear. These observations help us to better understand salt-governed glycine polymorphic selectivity.

Color and depth data fusion using an RGB-D sensor for inexpensive and contactless dynamic displacement-field measurement

2017 ◽  
Vol 24 (11) ◽  
pp. e2000 ◽  
Author(s):  
Yulu Luke Chen ◽  
Mohamed Abdelbarr ◽  
Mohammad R. Jahanshahi ◽  
Sami F. Masri
Keyword(s):  
Data Fusion ◽  
Displacement Field ◽  
Field Measurement ◽  
Dynamic Displacement ◽  
Depth Data
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