scholarly journals A New Model of RGB-D Camera Calibration Based On 3D Control Field

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5082 ◽  
Author(s):  
Zhang ◽  
Huang ◽  
Zhao
Keyword(s):  
3D Reconstruction ◽  
State Of The Art ◽  
Calibration Model ◽  
Control Field ◽  
New Model ◽  
Depth Images ◽  
Extrinsic Parameters ◽  
Relationship Model ◽  
Relative Pose ◽  
Calibration Parameters

With extensive application of RGB-D cameras in robotics, computer vision, and many other fields, accurate calibration becomes more and more critical to the sensors. However, most existing models for calibrating depth and the relative pose between a depth camera and an RGB camera are not universally applicable to many different kinds of RGB-D cameras. In this paper, by using the collinear equation and space resection of photogrammetry, we present a new model to correct the depth and calibrate the relative pose between depth and RGB cameras based on a 3D control field. We establish a rigorous relationship model between the two cameras; then, we optimize the relative parameters of two cameras by least-squares iteration. For depth correction, based on the extrinsic parameters related to object space, the reference depths are calculated by using a collinear equation. Then, we calibrate the depth measurements with consideration of the distortion of pixels in depth images. We apply Kinect-2 to verify the calibration parameters by registering depth and color images. We test the effect of depth correction based on 3D reconstruction. Compared to the registration results from a state-of-the-art calibration model, the registration results obtained with our calibration parameters improve dramatically. Likewise, the performances of 3D reconstruction demonstrate obvious improvements after depth correction.

scholarly journals Learning Feature Interactions with Lorentzian Factorization Machine

2020 ◽  
Vol 34 (04) ◽  
pp. 6470-6477
Author(s):  
Canran Xu ◽  
Ming Wu
Keyword(s):  
Deep Learning ◽  
Hyperbolic Space ◽  
Recommendation System ◽  
Triangle Inequality ◽  
State Of The Art ◽  
Learning Methods ◽  
New Model ◽  
User Behaviors ◽  
Feature Interactions ◽  
Factorization Machine

Learning representations for feature interactions to model user behaviors is critical for recommendation system and click-trough rate (CTR) predictions. Recent advances in this area are empowered by deep learning methods which could learn sophisticated feature interactions and achieve the state-of-the-art result in an end-to-end manner. These approaches require large number of training parameters integrated with the low-level representations, and thus are memory and computational inefficient. In this paper, we propose a new model named “LorentzFM” that can learn feature interactions embedded in a hyperbolic space in which the violation of triangle inequality for Lorentz distances is available. To this end, the learned representation is benefited by the peculiar geometric properties of hyperbolic triangles, and result in a significant reduction in the number of parameters (20% to 80%) because all the top deep learning layers are not required. With such a lightweight architecture, LorentzFM achieves comparable and even materially better results than the deep learning methods such as DeepFM, xDeepFM and Deep & Cross in both recommendation and CTR prediction tasks.

scholarly journals Modeling Topical Relevance for Multi-Turn Dialogue Generation

2020 ◽  
Author(s):  
Hainan Zhang ◽  
Yanyan Lan ◽  
Liang Pang ◽  
Hongshen Chen ◽  
Zhuoye Ding ◽  
...  
Keyword(s):  
Topic Model ◽  
State Of The Art ◽  
Training Dataset ◽  
Common Phenomenon ◽  
New Model ◽  
Sentence Level ◽  
Topic Distribution ◽  
Topic Drift ◽  
Relevant Context ◽  
Customer Services

Topic drift is a common phenomenon in multi-turn dialogue. Therefore, an ideal dialogue generation models should be able to capture the topic information of each context, detect the relevant context, and produce appropriate responses accordingly. However, existing models usually use word or sentence level similarities to detect the relevant contexts, which fail to well capture the topical level relevance. In this paper, we propose a new model, named STAR-BTM, to tackle this problem. Firstly, the Biterm Topic Model is pre-trained on the whole training dataset. Then, the topic level attention weights are computed based on the topic representation of each context. Finally, the attention weights and the topic distribution are utilized in the decoding process to generate the corresponding responses. Experimental results on both Chinese customer services data and English Ubuntu dialogue data show that STAR-BTM significantly outperforms several state-of-the-art methods, in terms of both metric-based and human evaluations.

scholarly journals Identifying Ethnics of People through Face Recognition: A Deep CNN Approach

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Ahmed Jawad A. AlBdairi ◽  
Zhu Xiao ◽  
Mohammed Alghaili
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Research Community ◽  
Facial Features ◽  
New Model ◽  
Image Dataset ◽  
Deep Cnn

The interest in face recognition studies has grown rapidly in the last decade. One of the most important problems in face recognition is the identification of ethnics of people. In this study, a new deep learning convolutional neural network is designed to create a new model that can recognize the ethnics of people through their facial features. The new dataset for ethnics of people consists of 3141 images collected from three different nationalities. To the best of our knowledge, this is the first image dataset collected for the ethnics of people and that dataset will be available for the research community. The new model was compared with two state-of-the-art models, VGG and Inception V3, and the validation accuracy was calculated for each convolutional neural network. The generated models have been tested through several images of people, and the results show that the best performance was achieved by our model with a verification accuracy of 96.9%.

Evaluation of the Kodak Ektachem Glucose and Urea Methods

1980 ◽  
Vol 17 (6) ◽  
pp. 293-300 ◽  
Author(s):  
Penelope M S Clark ◽  
Larry J Kricka ◽  
Thomas P Whitehead
Keyword(s):  
Cerebrospinal Fluid ◽  
Analytical Methods ◽  
External Quality Assessment ◽  
Calibration Model ◽  
Tetraacetic Acid ◽  
Pleural Effusions ◽  
Serum Samples ◽  
External Quality ◽  
Control Serum ◽  
Calibration Parameters

An evaluation of the Kodak Ektachem glucose and urea methods is described. Aspects evaluated included precision, linearity, accuracy, correlation with routine methods, interferences (haemoglobin, bilirubin, protein, dextran, lipaemia, ethylene-diamine tetraacetic acid, fluoride/oxalate, and heparin), and carryover. Stability and batch-to-batch variation in glucose and urea reagents were also investigated. The performance of the Ektachem glucose and urea methods was shown to be as satisfactory as conventional analytical methods. The requirement to reconstitute control serum samples in a bicarbonate diluent in order to obtain accurate results presents problems for the analysis of lyophilised specimens circulated by external quality assessment schemes. The complex calibration model and the significance of variation in the calibration parameters need further explanation. The Ektachem methods are designed specifically for use with human serum. However, the methods performed satisfactorily with cerebrospinal fluid, pleural effusions, and animal serum but not with urine.

scholarly journals CALIBRATION OF SMARTPHONE’S REAR DUAL CAMERA SYSTEM

2021 ◽  
Vol 47 (4) ◽  
pp. 162-169
Author(s):  
Mohammed Aldelgawy ◽  
Isam Abu-Qasmieh
Keyword(s):  
Lens Distortion ◽  
Stereo Pair ◽  
Calibration Model ◽  
Wide Angle ◽  
Camera System ◽  
Check Points ◽  
Extrinsic Parameters ◽  
Wide Angle Lens ◽  
Chessboard Pattern ◽  
Distortion Parameters

This paper aims to calibrate smartphone’s rear dual camera system which is composed of two lenses, namely; wide-angle lens and telephoto lens. The proposed approach handles large sized images. Calibration was done by capturing 13 photos for a chessboard pattern from different exposure positions. First, photos were captured in dual camera mode. Then, for both wide-angle and telephoto lenses, image coordinates for node points of the chessboard were extracted. Afterwards, intrinsic, extrinsic, and lens distortion parameters for each lens were calculated. In order to enhance the accuracy of the calibration model, a constrained least-squares solution was applied. The applied constraint was that the relative extrinsic parameters of both wide-angle and telephoto lenses were set as constant regardless of the exposure position. Moreover, photos were rectified in order to eliminate the effect of lens distortion. For results evaluation, two oriented photos were chosen to perform a stereo-pair intersection. Then, the node points of the chessboard pattern were used as check points.

scholarly journals HEAT SOURCE FOR TIG WELDING MODELLING

2021 ◽  
Vol 3 ◽  
pp. 348-356
Author(s):  
Manahil Tongov
Keyword(s):  
Heat Source ◽  
Concentration Factor ◽  
Tig Welding ◽  
Experimental Results ◽  
New Model ◽  
Heating Spot ◽  
Spot Radius ◽  
Calibration Parameters ◽  
Source Concentration

A new model of heat source applicable to TIG welding is proposed. The model uses three calibration parameters - efficiency, effective heating spot radius and heat source concentration factor. Based on the experimental results, the model was calibrated and the results obtained for the form of penetration were compared with the experimental ones.

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