Deep learning-assisted common temperature measurement based on visible light imaging

Real-time,contact-free temperature monitoring of low to medium range(30℃-150℃)has been extensively used in industry and agriculture,which is usually realized by costly infrared temperature detection methods.This paper proposes an alternative approach of extracting temperature information in real time from the visible light images of the monitoring target using a convolutional neural network(CNN).A mean-square error of<1.119℃was reached in the temperature measurements of low to medium range using the CNN and the visible light images.Imaging angle and imaging distance do not affect the temperature detection using visible optical images by the CNN.Moreover,the CNN has a certain illuminance generalization ability capable of detection temperature information from the images which were collected under different illuminance and were not used for training.Compared to the conventional machine learning algorithms mentioned in the recent literatures,this real-time,contact-free temperature measurement approach that does not require any further image processing operations facilitates temperature monitoring applications in the industrial and civil fields.

Similarity Measures of Satellite Images Using an Adaptive Feature Contrast Model

Similarity measurement is one of key operations to retrieve “desired” images from an image database. As a famous psychological similarity measure approach, the Feature Contrast (FC) model is defined as a linear combination of both common and distinct features. In this paper, an adaptive feature contrast (AdaFC) model is proposed to measure similarity between satellite images for image retrieval. In the AdaFC, an adaptive function is used to model a variable role of distinct features in the similarity measurement. Specifically, given some distinct features in a satellite image, e.g., a COAST image, they might play a significant role when the image is compared with an image including different semantics, e.g., a SEA image, and might be trivial when it is compared with a third image including same semantics, e.g., another COAST image. Experimental results on satellite images show that the proposed model can consistently improve similarity retrieval effectiveness of satellite images including multiple geo-objects, for example COAST images.

An Experimental System Development for Head Posture Estimation Based on 3-D Images Measurement

Although automobile is an indispensable vehicle to modern life, it also serves as a social problem with a big traffic accident. Among the reasons of traffic accidents, careless driving accounts for the largest part. So in order to avoid the careless driving, a system which can measure the posture of a driver and warns driver to drive carefully in the case of looking aside is necessary. Although the image measurement method is used broadly, there is a problem on which measurement accuracy is influenced by environment light, makeup of the driver, etc. in the general method based on the two-dimensional image. Therefore, in this study, we propose an image measurement method to obtain the head posture of driver. First we use three-dimensional measurement method which based on the infrared pattern projection to get 3-D information of head, and then we calculate the angle for faces. In this paper, we explain the composition method of an experiment system, and the results of head posture measurement experiment.

TWO-DIMENSIONAL PARTICLE IMAGE VELOCIMETRY(PIV) MEASUREMENTS IN A TRANSPARENT CENTRIFUGAL PUMP

A special transparent centrifugal pump is designed. Detailed opticalmeasurements of the flow inside the rotating passages of a five-bladed shroud centrifugal pumpimpeller have been performed by using two-dimensional particle image velocimetry (PIV). The flow issurveyed at three load conditions q_V/q_(Vd) = 0.4, q_V/q_(Vd) = 1.0, q_V/q_(Vd) = 1.5,respectively. As a result, phase averaged PIV velocity vector maps on three planes between hub andshroud of the impeller are presented. At design load, the mean field of relative velocity ispredominantly vane congruent, showing well-behaved flow without separation. The distributions of therelative velocity on different plane along the pump shaft are very different and there is always alow velocity zone near the pressure-side of the blade at both low and design flow rate, but thelow-velocity-zone at the low flow rate is much larger than that at the design one. The studydemonstrates that the PIV technique is efficient in providing reliable and detailed velocity dataover a full impeller passage.

Computed tomography vs liver stiffness measurement and magnetic resonance imaging in evaluating esophageal varices in cirrhotic patients:A systematic review and meta-analysis

BACKGROUND Computed tomography(CT),liver stiffness measurement(LSM),and magnetic resonance imaging(MRI)are non-invasive diagnostic methods for esophageal varices(EV)and for the prediction of high-bleeding-risk EV(HREV)in cirrhotic patients.However,the clinical use of these methods is controversial.AIM To evaluate the accuracy of LSM,CT,and MRI in diagnosing EV and predicting HREV in cirrhotic patients.METHODS We performed literature searches in multiple databases,including Pub Med,Embase,Cochrane,CNKI,and Wanfang databases,for articles that evaluated the accuracy of LSM,CT,and MRI as candidates for the diagnosis of EV and prediction of HREV in cirrhotic patients.Summary sensitivity and specificity,positive likelihood ratio and negative likelihood ratio,diagnostic odds ratio,and the areas under the summary receiver operating characteristic curves were analyzed.The quality of the articles was assessed using the quality assessment of diagnostic accuracy studies-2 tool.Heterogeneity was examined by Q-statistic test and I2 index,and sources of heterogeneity were explored using metaregression and subgroup analysis.Publication bias was evaluated using Deek’s funnel plot.All statistical analyses were conducted using Stata12.0,Meta Disc1.4,and Rev Man5.3.RESULTS Overall,18,17,and 7 relevant articles on the accuracy of LSM,CT,and MRI in evaluating EV and HREV were retrieved.A significant heterogeneity was observed in all analyses(P<0.05).The areas under the summary receiver operating characteristic curves of LSM,CT,and MRI in diagnosing EV and predicting HREV were 0.86(95%confidence interval[CI]:0.83-0.89),0.91(95%CI:0.88-0.93),and 0.86(95%CI:0.83-0.89),and 0.85(95%CI:0.81-0.88),0.94(95%CI:0.91-0.96),and 0.83(95%CI:0.79-0.86),respectively,with sensitivities of 0.84(95%CI:0.78-0.89),0.91(95%CI:0.87-0.94),and 0.81(95%CI:0.76-0.86),and 0.81(95%CI:0.75-0.86),0.88(95%CI:0.82-0.92),and 0.80(95%CI:0.72-0.86),and specificities of 0.71(95%CI:0.60-0.80),0.75(95%CI:0.68-0.82),and 0.82(95%CI:0.70-0.89),and 0.73(95%CI:0.66-0.80),0.87(95%CI:0.81-0.92),and 0.72(95%CI:0.62-0.80),respectively.The corresponding positive likelihood ratios were 2.91,3.67,and 4.44,and 3.04,6.90,and2.83;the negative likelihood ratios were 0.22,0.12,and 0.23,and 0.26,0.14,and 0.28;the diagnostic odds ratios were 13.01,30.98,and 19.58,and 11.93,49.99,and 10.00.CT scanner is the source of heterogeneity.There was no significant difference in diagnostic threshold effects(P>0.05)or publication bias(P>0.05).CONCLUSION Based on the meta-analysis of observational studies,it is suggested that CT imaging,a non-invasive diagnostic method,is the best choice for the diagnosis of EV and prediction of HREV in cirrhotic patients compared with LSM and MRI.

Measuring the Condition of Parking Lot by Image Processing

Aim To study the parking management in the condition of vehicles' increasing. Methods The methods of pattern recognition and image processing were used to analyze the eigenvalues of parking lot images. Results The automatic identification of every parking place in the parking plot was realized. The automatic measuring of parked vehicle count and parking lot utilization was completed. Conclusion It can complete the real time recognition, and has some practicabilities.

IMAGE PROFILE AREA CALCULATION BASED ON CIRCULAR SAMPLE MEASUREMENT CALIBRATION

A practical approach of measurement calibration is presented for obtaining the true area of the photographed objects projected in the 2-D image scene. The calibration is performed using three circular samples with given diameters. The process is first to obtain the ratio mm/pixel in two orthogonal directions, and then use the obtained ratios with the total number of pixels scanned within projected area of the object of interest to compute the desired area. Compared the optically measured areas with their corresponding true areas, the results show that the proposed method is quite encouraging and the relevant application also proves the approach adequately accurate.

Monitoring of vegetation coverage based on high-resolution images

Measurement of vegetation coverage on a small scale is the foundation for the monitoring of changes in vegetation coverage and of the inversion model of monitoring vegetation coverage on a large scale by remote sensing. Using the object-oriented analytical software, Definiens Professional 5, a new method for calculating vegetation coverage based on high-resolution images （aerial photographs or near-surface photography） is proposed. Our research supplies references to remote sensing measurements of vegetation coverage on a small scale and accurate fundamental data for the inversion model of vegetation coverage on a large and intermediate scale to improve the accuracy of remote sensing monitoring of changes in vegetation coverage.

Measuring loblolly pine crowns with drone imagery through deep learning

In modeling forest stand growth and yield,crown width,a measure for stand density,is among the parameters that allows for estimating stand timber volumes.However,accurately measuring tree crown size in the field,in particular for mature trees,is challenging.This study demonstrated a novel method of applying machine learning algorithms to aerial imagery acquired by an unmanned aerial vehicle(UAV)to identify tree crowns and their widths in two loblolly pine plantations in eastern Texas,USA.An ortho mosaic image derived from UAV-captured aerial photos was acquired for each plantation(a young stand before canopy closure,a mature stand with a closed canopy).For each site,the images were split into two subsets:one for training and one for validation purposes.Three widely used object detection methods in deep learning,the Faster region-based convolutional neural network(Faster R-CNN),You Only Look Once version 3(YOLOv3),and single shot detection(SSD),were applied to the training data,respectively.Each was used to train the model for performing crown recognition and crown extraction.Each model output was evaluated using an independent test data set.All three models were successful in detecting tree crowns with an accuracy greater than 93%,except the Faster R-CNN model that failed on the mature site.On the young site,the SSD model performed the best for crown extraction with a coefficient of determination(R^(2))of 0.92,followed by Faster R-CNN(0.88)and YOLOv3(0.62).As to the mature site,the SSD model achieved a R^(2)as high as 0.94,follow by YOLOv3(0.69).These deep leaning algorithms,in particular the SSD model,proved to be successfully in identifying tree crowns and estimating crown widths with satisfactory accuracy.For the purpose of forest inventory on loblolly pine plantations,using UAV-captured imagery paired with the SSD object detention application is a cost-effective alternative to traditional ground measurement.

Image-based Water Level Measurement Method under Stained Ruler

This paper propoes the water level measuring method based on the image, while the ruler used to indicate the water level is stained. The contamination of the ruler weakens or eliminates many features which are required for the image processing. However, the feature of the color difference between the ruler and the water surface are firmer on the environmental change compare to the other features. As the color differeaces are embossed, only the region of the ruler is limited to eliminate the noise, and the average image is produced by using several continuous frames. A histogram is then produced on the height axis of the produced intensity average image. Local peaks and local valleys are detected, and the section between the peak and valley which have the greatest change is looked for. The valley point at this very moment is used to detect the water level. The detected water level is then converted to the actual water level by using the mapping table. The proposed method is compared to the ultrasonic based method to evaluate its accuracy and efficiency on the various contaminated environments.

Comparison of satellite images with different spatial resolutions to estimate stand structural diversity in urban forests

The structural diversity in urban forests is highly important to protect biodiversity. In particular, fruit trees and bush species, cavity-bearing trees and coarse, woody debris provide habitats for animals to feed, nest and hide. Improper silvicultural practices, intensive recreational use and illegal harvesting lead to a decline in the structural diversity in forests within larger metropolitan cities. It is important to monitor the structural diversity at definite time intervals using effective technologies with a view to instituting the necessary conservation measures. The use of satellite images seems to be appropriate to this end. Here we aimed to identify the associations between the textural features derived from the satellite images with different spatial resolutions and the structural diversity indices in urban forest stands (Shannon-Wiener index, complexity index, dominance index and density of wildlife trees). RapidEye images with a spatial resolution of 5 m × 5 m, ASTER images with a spatial resolution of 15 m × 15 m and Landsat-8 ETM satellite images with a spatial resolution of 30 m × 30 m were used in this study. The first-order (standard deviation of gray levels) and second order (GLCM entropy, GLCM contrast and GLCM correlation) textural features were calculated from the satellite images. When associations between textural features in the images and the structural diversity indices were assessed using the Pearson correlation coefficient, very high associations were found between the image textural features and the diversity indices. The highest association was found between the standard deviation of gray levels (SDGLRAP) derived from RVIRAP of RapidEye image and the Shannon-Wiener index (H h) calculated on the basis of tree height (R 2 = 0.64). The findings revealed that RapidEye satellite images with a spatial resolution of 5 m × 5 m are most suitable for estimating the structural diversity in urban forests.

Deep image retrieval using artificial neural network interpolation and indexing based on similarity measurement

In content-based image retrieval(CBIR),primitive image signatures are critical because they represent the visual characteristics.Image signatures,which are algorithmically descriptive and accurately recognized visual components,are used to appropriately index and retrieve comparable results.To differentiate an image in the category of qualifying contender,feature vectors must have image information's like colour,objects,shape,spatial viewpoints.Previous methods such as sketch-based image retrieval by salient contour(SBIR)and greedy learning of deep Boltzmann machine(GDBM)used spatial information to distinguish between image categories.This requires interest points and also feature analysis emerged image detection problems.Thus,a proposed model to overcome this issue and predict the repeating pattern as well as series of pixels that conclude similarity has been necessary.In this study,a technique called CBIR-similarity measure via artificial neural network interpolation(CBIR-SMANN)has been presented.By collecting datasets,the images are resized then subject to Gaussian filtering in the pre-processing stage,then by permitting them to the Hessian detector,the interesting points are gathered.Based on Skewness,mean,kurtosis and standard deviation features were extracted then given to ANN for interpolation.Interpolated results are stored in a database for retrieval.In the testing stage,the query image was inputted that is subjected to pre-processing,and feature extraction was then fed to the similarity measurement function.Thus,ANN helps to get similar images from the database.CBIR-SMANN have been implemented in the python tool and then evaluated for its performance.Results show that CBIR-SMANN exhibited a high recall value of 78%with a minimum retrieval time of 980 ms.This showed the supremacy of the proposed model was comparatively greater than the previous ones.

Turbidity analysis using visible and near-infrared light images

The conventional photoelectric detection system requires complex circuitry and spectroscopic systems as well as specialized personnel for its operation.To replace such a system,a method of measuring turbidity using a camera is proposed by combining the imaging characteristics of a digital camera and the high-speed information processing capability of a computer.Two turbidity measurement devices based on visible and near-infrared(NIR)light cameras and a light source driving circuit with constant light intensity were designed.The RGB data in the turbidity images were acquired using a self-developed image processing software and converted to the CIE Lab color space.Based on the relationship between the luminance,chromatic aberration,and turbidity,the turbidity detection models for luminance and chromatic aberration of visible and NIR light devices exhibiting values from 0-1000 NTU,less than 100 NTU,and more than 100 NTU were established.By comparing and analyzing the proposed models,the two measurement models with the best all-around performance were selected and fused to generate new measurement models.The experimental results prove that the correlation between the three models and the commercial turbidity meter measurements exhibite a significance value higher than 0.999.The error of the fusion model is within 1.05%,with a mean square error of 1.14.The visible light device has less error at low turbidity measurements and is less influenced by the color of the image.The NIR light device is more stable and accurate at full range and high turbidity measurements and is therefore more suitable for such measurements.

Digital image stabilization system based on inertial measurement module

In order to stabilize the video module to build digital image stabilization image sequence, a method of using inertial measurement system is proposed. Through applying real-time attitude in- formation of the camera that obtained by high-precision attitude sensor to estimate the image motion vector and then to compensate for image, the purpose of stabilizing the image sequence can be a- chieved. Experiments demonstrate that this method has a high image stabilization precision, and the up to 16 frame/s video output rate completely meets the real-time requirements.

In-situ strain measurement and error analysis of arc welding with 2D digital image correlation

The deformation and residual stress generated by the welding process can seriously affect the use of components.As a result,it is very important to understand the evolution of stress and strain during the welding process.The strain measurement method based on digital image correlation(DIC)is an excellent method to detect welding strain and residual stress.The out-of-plane translation and out-of-plane rotation introduce errors to the two-dimensional DIC.In this paper,the causes of errors are analyzed theoretically,and the formulas of errors caused by the out-of-plane displacement and the out-of-plane rotation are derived.The out-of-plane translation experiment and the out-of-plane rotation experiment were carried out to verify the theory,and the experimental results are consistent with the theoretical analysis results.The error caused by the out-of-plane translation can be reduced by increasing the object distance;the error caused by the out-of-plane rotation is greatly affected by the rotation angle.

Study and application of monitoring plane displacement of a similarity model based on time-series images

In order to compensate for the deficiency of present methods of monitoring plane displacement in similarity model tests,such as inadequate real-time monitoring and more manual intervention,an effective monitoring method was proposed in this study,and the major steps of the monitoring method include:firstly,time-series images of the similarity model in the test were obtained by a camera,and secondly,measuring points marked as artificial targets were automatically tracked and recognized from time-series images.Finally,the real-time plane displacement field was calculated by the fixed magnification between objects and images under the specific conditions.And then the application device of the method was designed and tested.At the same time,a sub-pixel location method and a distortion error model were used to improve the measuring accuracy.The results indicate that this method may record the entire test,especially the detailed non-uniform deformation and sudden deformation.Compared with traditional methods this method has a number of advantages,such as greater measurement accuracy and reliability,less manual intervention,higher automation,strong practical properties,much more measurement information and so on.

Landslide data mosaicking based on an airborne laser point cloud and multi-beam sonar images

Landslides are one of the most disastrous geological hazards in southwestern China.Once a landslide becomes unstable,it threatens the lives and safety of local residents.However,empirical studies on landslides have predominantly focused on landslides that occur on land.To this end,we aim to investigate ashore and underwater landslide data synchronously.This study proposes an optimized mosaicking method for ashore and underwater landslide data.This method fuses an airborne laser point cloud with multi-beam depth sounder images.Owing to their relatively high efficiency and large coverage area,airborne laser measurement systems are suitable for emergency investigations of landslides.Based on the airborne laser point cloud,the traversal of the point with the lowest elevation value in the point set can be used to perform rapid extraction of the crude channel boundaries.Further meticulous extraction of the channel boundaries is then implemented using the probability mean value optimization method.In addition,synthesis of the integrated ashore and underwater landslide data angle is realized using the spatial guide line between the channel boundaries and the underwater multibeam sonar images.A landslide located on the right bank of the middle reaches of the Yalong River is selected as a case study to demonstrate that the proposed method has higher precision thantraditional methods.The experimental results show that the mosaicking method in this study can meet the basic needs of landslide modeling and provide a basis for qualitative and quantitative analysis and stability prediction of landslides.

AUTOMATED IMAGE MATCHING WITH CODED POINTS IN STEREOVISION MEASUREMENT

A coding-based method to solve the image matching problems in stereovision measurement is presented. The solution is to add and append an identity ID to the retro-reflect point, so it can be identified efficiently under the complicated circumstances and has the characteristics of rotation, zooming, and deformation independence. Its design architecture and implementation process in details based on the theory of stereovision measurement are described. The method is effective on reducing processing data time, improving accuracy of image matching and automation of measuring system through experiments.

Dynamic Contrast-Enhanced Perfusion MR Imaging Measurements of Endothelial Permeability: Differentiation between Atypical and Typical Meningiomas

BACKGROUND: AND PURPOSE: The measurement of relative cerebral blood volume (rCBV) and the volume transfer constant (K(trans)) by means of dynamic contrast-enhanced (DCE) perfusion MR imaging (pMRI) can be useful in characterizing brain tumors. The purpose of our study was to evaluate the utility of these measurements in differentiating typical meningiomas and atypical meningiomas. METHODS: Fifteen patients with pathologically confirmed typical meningiomas and seven with atypical meningiomas underwent conventional imaging and DCE pMRI before resection.rCBV measurements were calculated by using standard intravascular

Measuring Fingerprint Image Quality Using the Fourier Spectrum

The fingerprint image quality has a significant effect on the performance of automatic fingerprint identification system. A method for measure of fingerprint image quality based on Fourier spectrum is proposed. First the band frequency which corresponds to the global average period of ridge is searched. Then the quality score of the fingerprint image is computed by measuring relative magnitude of the band frequency components. The method is verified to have good performance by experiments.