Considering the difficulty of integrating the depth points of nautical charts of the East China Sea into a global high-precision Grid Digital Elevation Model(Grid-DEM),we proposed a“Fusion based on Image Recognition(...Considering the difficulty of integrating the depth points of nautical charts of the East China Sea into a global high-precision Grid Digital Elevation Model(Grid-DEM),we proposed a“Fusion based on Image Recognition(FIR)”method for multi-sourced depth data fusion,and used it to merge the electronic nautical chart dataset(referred to as Chart2014 in this paper)with the global digital elevation dataset(referred to as Globalbath2002 in this paper).Compared to the traditional fusion of two datasets by direct combination and interpolation,the new Grid-DEM formed by FIR can better represent the data characteristics of Chart2014,reduce the calculation difficulty,and be more intuitive,and,the choice of different interpolation methods in FIR and the influence of the“exclusion radius R”parameter were discussed.FIR avoids complex calculations of spatial distances among points from different sources,and instead uses spatial exclusion map to perform one-step screening based on the exclusion radius R,which greatly improved the fusion status of a reliable dataset.The fusion results of different experiments were analyzed statistically with root mean square error and mean relative error,showing that the interpolation methods based on Delaunay triangulation are more suitable for the fusion of nautical chart depth of China,and factors such as the point density distribution of multiple source data,accuracy,interpolation method,and various terrain conditions should be fully considered when selecting the exclusion radius R.展开更多
This paper advances a three-dimensional space interpolation method of grey / depth image sequence, which breaks free from the limit of original practical photographing route. Pictures can cruise at will in space. By u...This paper advances a three-dimensional space interpolation method of grey / depth image sequence, which breaks free from the limit of original practical photographing route. Pictures can cruise at will in space. By using space sparse sampling, great memorial capacity can be saved and reproduced scenes can be controlled. To solve time consuming and complex computations in three-dimensional interpolation algorithm, we have studied a fast and practical algorithm of scattered space lattice and that of 'Warp' algorithm with proper depth. By several simple aspects of three dimensional space interpolation, we succeed in developing some simple and practical algorithms. Some results of simulated experiments with computers have shown that the new method is absolutely feasible.展开更多
This paper presents a new fall detection method of etderly people in a room environment based on shape analysis of 3D depth images captured by a Kinect sensor. Depth images are pre- processed by a median filter both f...This paper presents a new fall detection method of etderly people in a room environment based on shape analysis of 3D depth images captured by a Kinect sensor. Depth images are pre- processed by a median filter both for background and target. The sithouette of moving individual in depth images is achieved by a subtraction method for background frames. The depth images are converted to disparity map, which is obtained by the horizontal and vertical projection histogram statistics. The initial floor plane information is obtained by V disparity map, and the floor ptane equation is estimated by the least square method. Shape information of human subject in depth images is analyzed by a set of moment functions. Coefficients of ellipses are calculated to determine the direction of individual The centroids of the human body are catculated and the angle between the human body and the floor plane is calculated. When both the distance from the centroids of the human body to the floor plane and the angle between the human body and the floor plane are tower than some threshotds, fall incident will be detected. Experiments with different failing direction are performed. Experimental results show that the proposed method can detect fall incidents effectively.展开更多
Medical image segmentation is a challenging task especially in multimodality medical image analysis.In this paper,an improved pulse coupled neural network based on multiple hybrid features grey wolf optimizer(MFGWO-PC...Medical image segmentation is a challenging task especially in multimodality medical image analysis.In this paper,an improved pulse coupled neural network based on multiple hybrid features grey wolf optimizer(MFGWO-PCNN)is proposed for multimodality medical image segmentation.Specifically,a two-stage medical image segmentation method based on bionic algorithm is presented,including image fusion and image segmentation.The image fusion stage fuses rich information from different modalities by utilizing a multimodality medical image fusion model based on maximum energy region.In the stage of image segmentation,an improved PCNN model based on MFGWO is proposed,which can adaptively set the parameters of PCNN according to the features of the image.Two modalities of FLAIR and TIC brain MRIs are applied to verify the effectiveness of the proposed MFGWO-PCNN algorithm.The experimental results demonstrate that the proposed method outperforms the other seven algorithms in subjective vision and objective evaluation indicators.展开更多
AIM:To evaluate the choroidopathy in patients with systemic lupus erythematosus(SLE)using enhanced depth imaging spectral domain optical coherence tomography(EDI SD-OCT)and optical coherence tomography angiography(OCT...AIM:To evaluate the choroidopathy in patients with systemic lupus erythematosus(SLE)using enhanced depth imaging spectral domain optical coherence tomography(EDI SD-OCT)and optical coherence tomography angiography(OCTA).METHODS:A total of 74 patients with SLE and 40 healthy volunteers were included in this cross-sectional study.SLE patients were further divided into three subgroups based on clinical and blood biochemistry findings.Ocular parameters obtained on ophthalmologic examination and optical imaging(EDI SD-OCT and OCTA)included the best corrected distance visual acuity(logMAR CDVA),subfoveal choroidal thickness(SCT),choroidal vascularity index(CVI)and vessel density(VD)of superficial capillary plexus(SCP)and deep capillary plexus(DCP).RESULTS:SLE patients had significantly lower values for CVI and VD of DCP(DVD)than control subjects.Amongst SLE patients,gender and chloroquine dose were found to be independent determinants of CVI while age predicted SCT.Steroid dose was a significant predictor for foveal VD of SCP(SVD),chloroquine dose for parafoveal SVD,gender for total DVD,and gender and steroid dose for perifoveal DVD.No correlation of logMAR CDVA and SCT was noted between SLE patients and control subjects.No correlation of SCT was noted with disease duration,Systemic Lupus Erythematosus Disease Activity Index(SLEDAI)score,hydroxychloroquine(HCQ)dose or steroid dose.No correlation of CVI was noted with patient age,disease duration,SLEDAI score,HCQ dose or steroid dose.No significant difference was noted between SLE subgroups in terms of any of the ocular parameters studied.CONCLUSION:The findings reveal the presence of ocular findings suggestive of early onset choroidopathy on EDI SD-OCT and OCTA in SLE patients,in the absence of ocular manifestations or active disease.展开更多
Snow depth （SD） is a key parameter for research into global climate changes and land surface processes. A method was developed to obtain daily SD images at a higher 4 km spatial resolution and higher precision with ...Snow depth （SD） is a key parameter for research into global climate changes and land surface processes. A method was developed to obtain daily SD images at a higher 4 km spatial resolution and higher precision with SD measurements from in situ observations and passive microwave remote sensing of Advanced Microwave Scanning Radiometer-EOS （AMSR-E） and snow cover measurements of the Interactive Multisensor Snow and Ice Mapping System （IMS）. AMSR-E SD at 25 km spatial resolution was retrieved from AMSR-E products of snow density and snow water equivalent and then corrected using the SD from in situ observations and IMS snow cover. Corrected AMSR-E SD images were then resampled to act as ＂virtual＂ in situ observations to combine with the real in situ observations to interpolate at 4 km spatial resolution SD using the Cressman method. Finally, daily SD data generation for several regions of China demonstrated that the method is well suited to the generation of higher spatial resolution SD data in regions with a lower Digital Elevation Model （DEM） but not so well suited to regions at high altitude and with an undulating terrain, such as the Tibetan Plateau. Analysis of the longer time period SD data generation for January between 2003 and 2010 in northern Xinjiang also demonstrated the feasibility of the method.展开更多
This paper proposes a new technique that is used to embed depth maps into corresponding 2-dimensional （2D） images. Since a 2D image and its depth map are integrated into one type of image format, they can be treated...This paper proposes a new technique that is used to embed depth maps into corresponding 2-dimensional （2D） images. Since a 2D image and its depth map are integrated into one type of image format, they can be treated as if they were one 2D image. Thereby, it can reduce the amount of data in 3D images by half and simplify the processes for sending them through networks because the synchronization between images for the left and right eyes becomes unnecessary. We embed depth maps in the quantized discrete cosine transform （DCT） data of 2D images. The key to this technique is whether the depth maps could be embedded into 2D images without perceivably deteriorating their quality. We try to reduce their deterioration by compressing the depth map data by using the differences from the next pixel to the left. We assume that there is only one non-zero pixel at most on one horizontal line in the DCT block because the depth map values change abruptly. We conduct an experiment to evaluate the quality of the 2D images embedded with depth maps and find that satisfactory quality could be achieved.展开更多
In general, to reconstruct the accurate shape of buildings, we need at least one stereomodel （two photographs） for each building. In most cases, however, only a single non-metric photograph is available, which is us...In general, to reconstruct the accurate shape of buildings, we need at least one stereomodel （two photographs） for each building. In most cases, however, only a single non-metric photograph is available, which is usually obtained either by an amateur, such as a tourist, or from a newspaper or a post card. To evaluate the validity of 3D reconstruction from a single non-metric image, this study analyzes the effects of object depth on the accuracy of dimensional shape in X and Y directions using a single non-metric image by means of simulation technique, as this was considered to be, in most cases, a main source of data acquisition in recording and documenting buildings.展开更多
Hyperspectral(HS)image classification plays a crucial role in numerous areas including remote sensing(RS),agriculture,and the monitoring of the environment.Optimal band selection in HS images is crucial for improving ...Hyperspectral(HS)image classification plays a crucial role in numerous areas including remote sensing(RS),agriculture,and the monitoring of the environment.Optimal band selection in HS images is crucial for improving the efficiency and accuracy of image classification.This process involves selecting the most informative spectral bands,which leads to a reduction in data volume.Focusing on these key bands also enhances the accuracy of classification algorithms,as redundant or irrelevant bands,which can introduce noise and lower model performance,are excluded.In this paper,we propose an approach for HS image classification using deep Q learning(DQL)and a novel multi-objective binary grey wolf optimizer(MOBGWO).We investigate the MOBGWO for optimal band selection to further enhance the accuracy of HS image classification.In the suggested MOBGWO,a new sigmoid function is introduced as a transfer function to modify the wolves’position.The primary objective of this classification is to reduce the number of bands while maximizing classification accuracy.To evaluate the effectiveness of our approach,we conducted experiments on publicly available HS image datasets,including Pavia University,Washington Mall,and Indian Pines datasets.We compared the performance of our proposed method with several state-of-the-art deep learning(DL)and machine learning(ML)algorithms,including long short-term memory(LSTM),deep neural network(DNN),recurrent neural network(RNN),support vector machine(SVM),and random forest(RF).Our experimental results demonstrate that the Hybrid MOBGWO-DQL significantly improves classification accuracy compared to traditional optimization and DL techniques.MOBGWO-DQL shows greater accuracy in classifying most categories in both datasets used.For the Indian Pine dataset,the MOBGWO-DQL architecture achieved a kappa coefficient(KC)of 97.68%and an overall accuracy(OA)of 94.32%.This was accompanied by the lowest root mean square error(RMSE)of 0.94,indicating very precise predictions with minimal error.In the case of the Pavia University dataset,the MOBGWO-DQL model demonstrated outstanding performance with the highest KC of 98.72%and an impressive OA of 96.01%.It also recorded the lowest RMSE at 0.63,reinforcing its accuracy in predictions.The results clearly demonstrate that the proposed MOBGWO-DQL architecture not only reaches a highly accurate model more quickly but also maintains superior performance throughout the training process.展开更多
We propose a novel image segmentation algorithm to tackle the challenge of limited recognition and segmentation performance in identifying welding seam images during robotic intelligent operations.Initially,to enhance...We propose a novel image segmentation algorithm to tackle the challenge of limited recognition and segmentation performance in identifying welding seam images during robotic intelligent operations.Initially,to enhance the capability of deep neural networks in extracting geometric attributes from depth images,we developed a novel deep geometric convolution operator(DGConv).DGConv is utilized to construct a deep local geometric feature extraction module,facilitating a more comprehensive exploration of the intrinsic geometric information within depth images.Secondly,we integrate the newly proposed deep geometric feature module with the Fully Convolutional Network(FCN8)to establish a high-performance deep neural network algorithm tailored for depth image segmentation.Concurrently,we enhance the FCN8 detection head by separating the segmentation and classification processes.This enhancement significantly boosts the network’s overall detection capability.Thirdly,for a comprehensive assessment of our proposed algorithm and its applicability in real-world industrial settings,we curated a line-scan image dataset featuring weld seams.This dataset,named the Standardized Linear Depth Profile(SLDP)dataset,was collected from actual industrial sites where autonomous robots are in operation.Ultimately,we conducted experiments utilizing the SLDP dataset,achieving an average accuracy of 92.7%.Our proposed approach exhibited a remarkable performance improvement over the prior method on the identical dataset.Moreover,we have successfully deployed the proposed algorithm in genuine industrial environments,fulfilling the prerequisites of unmanned robot operations.展开更多
For traffic object detection in foggy environment based on convolutional neural network(CNN),data sets in fog-free environment are generally used to train the network directly.As a result,the network cannot learn the ...For traffic object detection in foggy environment based on convolutional neural network(CNN),data sets in fog-free environment are generally used to train the network directly.As a result,the network cannot learn the object characteristics in the foggy environment in the training set,and the detection effect is not good.To improve the traffic object detection in foggy environment,we propose a method of generating foggy images on fog-free images from the perspective of data set construction.First,taking the KITTI objection detection data set as an original fog-free image,we generate the depth image of the original image by using improved Monodepth unsupervised depth estimation method.Then,a geometric prior depth template is constructed to fuse the image entropy taken as weight with the depth image.After that,a foggy image is acquired from the depth image based on the atmospheric scattering model.Finally,we take two typical object-detection frameworks,that is,the two-stage object-detection Fster region-based convolutional neural network(Faster-RCNN)and the one-stage object-detection network YOLOv4,to train the original data set,the foggy data set and the mixed data set,respectively.According to the test results on RESIDE-RTTS data set in the outdoor natural foggy environment,the model under the training on the mixed data set shows the best effect.The mean average precision(mAP)values are increased by 5.6%and by 5.0%under the YOLOv4 model and the Faster-RCNN network,respectively.It is proved that the proposed method can effectively improve object identification ability foggy environment.展开更多
Case depth measurement of the induction hardened steel parts is necessary for quality control. Vickers microhardness test is the most industrially accepted method to identify the case depth. But this method is a time ...Case depth measurement of the induction hardened steel parts is necessary for quality control. Vickers microhardness test is the most industrially accepted method to identify the case depth. But this method is a time consuming one and it requires expensive equipment. The aim of this study is to develop a different method to determine the case depth using image processing. The surface hardened steel samples were cross cut, ground and etched with Nital. The etched macrosectioned specimens were scanned by a scanner. The scanned images were evaluated by the developed software. The principle of the software is to identify the gray level difference. The effective case depths of the surface hardened specimens obtained by Vickers microhardness test and the developed method were compared. It was found that the deviation of the developed method was ±0.12 mm at the case depth range of 0.6 - 2.0 mm and mm at the case depth range of 2.1 - 4.3 mm. The measuring time was only 20% of Vickers microhardness test. The deviation range is much lower than the tolerance case depth specification for induction hardening in general.展开更多
Due to their unique structural features, electrospun membranes have gained considerable attention for use in applications where quality of depth filtration is a dominant performance factor. To elucidate the depth filt...Due to their unique structural features, electrospun membranes have gained considerable attention for use in applications where quality of depth filtration is a dominant performance factor. To elucidate the depth filtration phenomena it is important to quantify the intrinsic structural properties independent from the dynamics of transport media. Several methods have been proposed for structural characterization of such membranes. However, these methods do not meet the requirement for the quantification of intrinsic structural properties in depth filtration. This may be due to the complex influence of transport media dynamics and structural elements in the depth filtration process. In addition, the different morphological architectures of electrospun membranes present obstacles to precise quantification. This paper seeks to quantify the structural characteristics of electrospun membranes by introducing a robust image analysis technique and exploiting it to evaluate the permeation-filtration mechanism. To this end, a nanostructured fibrous network was simulated as an ideal membrane using adaptive local criteria in the image analysis. The reliability of the proposed approach was validated with measurements and comparison of structural characteristics in different morphological conditions. The results were found to be well compatible with empirical observations of perfect membrane structures. This approach, based on optimization of electrospinning parameters, may pave the way for producing optimal membrane structures for boosting the performance of electrospun membranes in end-use applications.展开更多
Aiming at removing fog from traffic images, a distance field is built according to the characteristics of traffic images, and a novel parameter estimation method based on the traffic image sequence is proposed. The fo...Aiming at removing fog from traffic images, a distance field is built according to the characteristics of traffic images, and a novel parameter estimation method based on the traffic image sequence is proposed. The fog model is derived from atmospheric scattering models. The direction of the distance field is parallel to the center line of the road, which increases along a line from the observer to the horizon, and the normalization is carried out to improve the distribution of the distance field model. After parameter initialization, the variations of the average gray values of reference regions are taken as the determining conditions to adjust the parameters. Finally, restorations are made by the fog model. Experimental results show that the proposed method can effectively remove fog from traffic images.展开更多
Proper parameters for image taking and minimum field number for image processing were investigated to evaluate volume fraction of unhydrated cement（UHC） in both neat cement paste and slag blended cement paste. Our r...Proper parameters for image taking and minimum field number for image processing were investigated to evaluate volume fraction of unhydrated cement（UHC） in both neat cement paste and slag blended cement paste. Our research suggested that magnification 250x was sufficient for the two pastes, and accelerating voltage should be set as 15 kV and 20 kV for BSE image taking of neat cement paste and slag blended cement paste respectively; the minimum field number increased while the total imaging area stayed the same as the magnification increased within certain statistical bias.展开更多
Objectives： Polymerization shrinkage of dental composites remains a major concern in restorative dentistry because it can lead to micro-cracking of the tooth and debonding at the tooth-restoration interface. The aim ...Objectives： Polymerization shrinkage of dental composites remains a major concern in restorative dentistry because it can lead to micro-cracking of the tooth and debonding at the tooth-restoration interface. The aim of this study was to measure the full-field polymerization shrinkage of dental composites using the optical digital image correlation （DIC） method and to evaluate how the measurement is influenced by the factors in experiment setup and image analysis. Methods： Four commercial dental composites, Premise Dentine, Z 100, Z250 and Tetric EvoCeram, were tested. Composite was first placed into a slot mould to form a bar specimen with rectangular-section of 4 mmx2 mm, followed by the surface painting to create irregular speckles. Curing was then applied at one end of the specimen while the other part were covered against curing light for simulating the clinical curing condition of composite in dental cavity. The painted surface was recorded by a charge-coupled device （CCD） camera before and after curing. Subsequently, the volumetric shrinkage of the specimen was calculated with specialist DIC software based on image cross correlation. In addition, a few factors that may influence the measuring accuracy, including the subset window size, speckle size, illumination light and specimen length, were also evaluated. Results： The volumetric shrinkage of the specimen generally decreases with increasing distance from the irradiated surface with a conspicuous exception being the composite Premise Dentine as its maximum shrinkage occurred at a subsurface distance of about 1 mm instead of the irradiated surface. Z100 had the greatest maximum shrinkage strain, followed by Z250, Tetric EvoCeram and then Premise Dentine. Larger subset window size made the shrinkage strain contour smoother. But the cost was that some details in the heterogeneity of the material were lost. Very small subset window size resulted in a lot of noise in the data, making it difficult to discern the general pattern in the strain distribution. Speckle size did not seem to have obvious effect on the volumetric shrinkage strain along specimen length; however, larger speckles resulted in higher level of noise or heterogeneity in the shrinkage distribution. Compared with bright illumination, dimmer lighting produced larger standard deviations in the measured shrinkage~ indicating a higher level of noise. The longer the specimen, the greater was the rate of reduction with distance from the irradiated surface, especially for the longitudinal strain. Significance： The image correlation method is capable of producing full-field polymerization shrinkage of dental composites. The accuracy of the measurements relies on selection of optimal parameters in experimental setup and DIC analysis.展开更多
The development of deep learning has revolutionized image recognition technology.How to design faster and more accurate image classification algorithms has become our research interests.In this paper,we propose a new ...The development of deep learning has revolutionized image recognition technology.How to design faster and more accurate image classification algorithms has become our research interests.In this paper,we propose a new algorithm called stochastic depth networks with deep energy model(SADIE),and the model improves stochastic depth neural network with deep energy model to provide attributes of images and analysis their characteristics.First,the Bernoulli distribution probability is used to select the current layer of the neural network to prevent gradient dispersion during training.Then in the backpropagation process,the energy function is designed to optimize the target loss function of the neural network.We also explored the possibility of using Adam and SGD combination optimization in deep neural networks.Finally,we use training data to train our network based on deep energy model and testing data to verify the performance of the model.The results we finally obtained in this research include the Classified labels of images.The impacts of our obtained results show that our model has high accuracy and performance.展开更多
The effect of optical cleaning method combined with laser speckle imaging(LSI)was discussed to improve the detection depth of LSI due to high scattering characteristics of skin,which limit its clinical application.A d...The effect of optical cleaning method combined with laser speckle imaging(LSI)was discussed to improve the detection depth of LSI due to high scattering characteristics of skin,which limit its clinical application.A double-layer skin tissue model embedded with a single blood vessel was established,and the Monte Carlo method was used to simulate photon propagation under the action of light-permeating agent.808 nm semiconductor and 632.8 nm He–Ne lasers were selected to study the e®ect of optical clearing agents(OCAs)on photon deposition in tissues.Results show that the photon energy deposition density in the epidermis increases with the amount of tissue°uid replaced by OCA.Compared with glucose solution,polyethylene glycol 400(PEG 400)and glycerol can considerably increase the average penetration depth of photons in the skin tissue,thereby raising the sampling depth of the LSI.After the action of glycerol,PEG 400,and glucose,the average photon penetration depth is increased by 51.78%,51.06%,and 21.51%for 808nm,68.93%,67.94%,and 26.67%for 632.8 nm lasers,respectively.In vivo experiment by dorsal skin chamber proves that glycerol can cause a substantial decrease in blood°ow rate,whereas PEG 400 can signicantly improve the capability of light penetration without a®ecting blood velocity,which exhibits considerable potential in the monitoring of blood°ow in skin tissues.展开更多
We study the influence of limited-view scanning on the depth imaging of photoacoustic tomography. The situation, in which absorbers are located at different depths with respect to the limited-view scanning trajectory,...We study the influence of limited-view scanning on the depth imaging of photoacoustic tomography. The situation, in which absorbers are located at different depths with respect to the limited-view scanning trajectory, is called depth imaging and is investigated in this paper. The results show that limited-view scanning causes the reconstructed intensity of deep absorbers to be weaker than that of shallow ones and that deep absorbers will be invisible if the scanning range is too small. The concept of effective scanning angle is proposed to analyse that phenomenon. We find that an effective scanning angle can well predict the relationship between scanning angle and the intensity ratio of absorbers. In addition, limited-view scanning is employed to improve image quality.展开更多
基金Supported by the National Key R&D Program of China (No.2023YFC3008100)the National Natural Science Foundation of China (No.U23A2033)
文摘Considering the difficulty of integrating the depth points of nautical charts of the East China Sea into a global high-precision Grid Digital Elevation Model(Grid-DEM),we proposed a“Fusion based on Image Recognition(FIR)”method for multi-sourced depth data fusion,and used it to merge the electronic nautical chart dataset(referred to as Chart2014 in this paper)with the global digital elevation dataset(referred to as Globalbath2002 in this paper).Compared to the traditional fusion of two datasets by direct combination and interpolation,the new Grid-DEM formed by FIR can better represent the data characteristics of Chart2014,reduce the calculation difficulty,and be more intuitive,and,the choice of different interpolation methods in FIR and the influence of the“exclusion radius R”parameter were discussed.FIR avoids complex calculations of spatial distances among points from different sources,and instead uses spatial exclusion map to perform one-step screening based on the exclusion radius R,which greatly improved the fusion status of a reliable dataset.The fusion results of different experiments were analyzed statistically with root mean square error and mean relative error,showing that the interpolation methods based on Delaunay triangulation are more suitable for the fusion of nautical chart depth of China,and factors such as the point density distribution of multiple source data,accuracy,interpolation method,and various terrain conditions should be fully considered when selecting the exclusion radius R.
文摘This paper advances a three-dimensional space interpolation method of grey / depth image sequence, which breaks free from the limit of original practical photographing route. Pictures can cruise at will in space. By using space sparse sampling, great memorial capacity can be saved and reproduced scenes can be controlled. To solve time consuming and complex computations in three-dimensional interpolation algorithm, we have studied a fast and practical algorithm of scattered space lattice and that of 'Warp' algorithm with proper depth. By several simple aspects of three dimensional space interpolation, we succeed in developing some simple and practical algorithms. Some results of simulated experiments with computers have shown that the new method is absolutely feasible.
基金AcknowledgementsThis work is financially supported by the National Natural Science Foundation of China （61005015）, the third National Post-Doctoral Special Foundation of China （201003280）, and 2011 Shanshai city young teachers＇ subsidy scheme. The authors would like to thank the reviewers for their useful comments.
文摘This paper presents a new fall detection method of etderly people in a room environment based on shape analysis of 3D depth images captured by a Kinect sensor. Depth images are pre- processed by a median filter both for background and target. The sithouette of moving individual in depth images is achieved by a subtraction method for background frames. The depth images are converted to disparity map, which is obtained by the horizontal and vertical projection histogram statistics. The initial floor plane information is obtained by V disparity map, and the floor ptane equation is estimated by the least square method. Shape information of human subject in depth images is analyzed by a set of moment functions. Coefficients of ellipses are calculated to determine the direction of individual The centroids of the human body are catculated and the angle between the human body and the floor plane is calculated. When both the distance from the centroids of the human body to the floor plane and the angle between the human body and the floor plane are tower than some threshotds, fall incident will be detected. Experiments with different failing direction are performed. Experimental results show that the proposed method can detect fall incidents effectively.
基金This research is supported by the National Key Research and Development Program of China(2018YFB0804202,2018YFB0804203)Regional Joint Fund of NSFC(U19A2057),the National Natural Science Foundation of China(61672259,61876070)and the Jilin Province Science and Technology Development Plan Project(20190303134SF,20180201064SF).
文摘Medical image segmentation is a challenging task especially in multimodality medical image analysis.In this paper,an improved pulse coupled neural network based on multiple hybrid features grey wolf optimizer(MFGWO-PCNN)is proposed for multimodality medical image segmentation.Specifically,a two-stage medical image segmentation method based on bionic algorithm is presented,including image fusion and image segmentation.The image fusion stage fuses rich information from different modalities by utilizing a multimodality medical image fusion model based on maximum energy region.In the stage of image segmentation,an improved PCNN model based on MFGWO is proposed,which can adaptively set the parameters of PCNN according to the features of the image.Two modalities of FLAIR and TIC brain MRIs are applied to verify the effectiveness of the proposed MFGWO-PCNN algorithm.The experimental results demonstrate that the proposed method outperforms the other seven algorithms in subjective vision and objective evaluation indicators.
文摘AIM:To evaluate the choroidopathy in patients with systemic lupus erythematosus(SLE)using enhanced depth imaging spectral domain optical coherence tomography(EDI SD-OCT)and optical coherence tomography angiography(OCTA).METHODS:A total of 74 patients with SLE and 40 healthy volunteers were included in this cross-sectional study.SLE patients were further divided into three subgroups based on clinical and blood biochemistry findings.Ocular parameters obtained on ophthalmologic examination and optical imaging(EDI SD-OCT and OCTA)included the best corrected distance visual acuity(logMAR CDVA),subfoveal choroidal thickness(SCT),choroidal vascularity index(CVI)and vessel density(VD)of superficial capillary plexus(SCP)and deep capillary plexus(DCP).RESULTS:SLE patients had significantly lower values for CVI and VD of DCP(DVD)than control subjects.Amongst SLE patients,gender and chloroquine dose were found to be independent determinants of CVI while age predicted SCT.Steroid dose was a significant predictor for foveal VD of SCP(SVD),chloroquine dose for parafoveal SVD,gender for total DVD,and gender and steroid dose for perifoveal DVD.No correlation of logMAR CDVA and SCT was noted between SLE patients and control subjects.No correlation of SCT was noted with disease duration,Systemic Lupus Erythematosus Disease Activity Index(SLEDAI)score,hydroxychloroquine(HCQ)dose or steroid dose.No correlation of CVI was noted with patient age,disease duration,SLEDAI score,HCQ dose or steroid dose.No significant difference was noted between SLE subgroups in terms of any of the ocular parameters studied.CONCLUSION:The findings reveal the presence of ocular findings suggestive of early onset choroidopathy on EDI SD-OCT and OCTA in SLE patients,in the absence of ocular manifestations or active disease.
基金Meteorological Research in the Public Interest,No.GYHY201106014Beijing Nova Program,No.2010B037China Special Fund for the National High Technology Research and Development Program of China(863 Program),No.412230
文摘Snow depth （SD） is a key parameter for research into global climate changes and land surface processes. A method was developed to obtain daily SD images at a higher 4 km spatial resolution and higher precision with SD measurements from in situ observations and passive microwave remote sensing of Advanced Microwave Scanning Radiometer-EOS （AMSR-E） and snow cover measurements of the Interactive Multisensor Snow and Ice Mapping System （IMS）. AMSR-E SD at 25 km spatial resolution was retrieved from AMSR-E products of snow density and snow water equivalent and then corrected using the SD from in situ observations and IMS snow cover. Corrected AMSR-E SD images were then resampled to act as ＂virtual＂ in situ observations to combine with the real in situ observations to interpolate at 4 km spatial resolution SD using the Cressman method. Finally, daily SD data generation for several regions of China demonstrated that the method is well suited to the generation of higher spatial resolution SD data in regions with a lower Digital Elevation Model （DEM） but not so well suited to regions at high altitude and with an undulating terrain, such as the Tibetan Plateau. Analysis of the longer time period SD data generation for January between 2003 and 2010 in northern Xinjiang also demonstrated the feasibility of the method.
文摘This paper proposes a new technique that is used to embed depth maps into corresponding 2-dimensional （2D） images. Since a 2D image and its depth map are integrated into one type of image format, they can be treated as if they were one 2D image. Thereby, it can reduce the amount of data in 3D images by half and simplify the processes for sending them through networks because the synchronization between images for the left and right eyes becomes unnecessary. We embed depth maps in the quantized discrete cosine transform （DCT） data of 2D images. The key to this technique is whether the depth maps could be embedded into 2D images without perceivably deteriorating their quality. We try to reduce their deterioration by compressing the depth map data by using the differences from the next pixel to the left. We assume that there is only one non-zero pixel at most on one horizontal line in the DCT block because the depth map values change abruptly. We conduct an experiment to evaluate the quality of the 2D images embedded with depth maps and find that satisfactory quality could be achieved.
文摘In general, to reconstruct the accurate shape of buildings, we need at least one stereomodel （two photographs） for each building. In most cases, however, only a single non-metric photograph is available, which is usually obtained either by an amateur, such as a tourist, or from a newspaper or a post card. To evaluate the validity of 3D reconstruction from a single non-metric image, this study analyzes the effects of object depth on the accuracy of dimensional shape in X and Y directions using a single non-metric image by means of simulation technique, as this was considered to be, in most cases, a main source of data acquisition in recording and documenting buildings.
文摘Hyperspectral(HS)image classification plays a crucial role in numerous areas including remote sensing(RS),agriculture,and the monitoring of the environment.Optimal band selection in HS images is crucial for improving the efficiency and accuracy of image classification.This process involves selecting the most informative spectral bands,which leads to a reduction in data volume.Focusing on these key bands also enhances the accuracy of classification algorithms,as redundant or irrelevant bands,which can introduce noise and lower model performance,are excluded.In this paper,we propose an approach for HS image classification using deep Q learning(DQL)and a novel multi-objective binary grey wolf optimizer(MOBGWO).We investigate the MOBGWO for optimal band selection to further enhance the accuracy of HS image classification.In the suggested MOBGWO,a new sigmoid function is introduced as a transfer function to modify the wolves’position.The primary objective of this classification is to reduce the number of bands while maximizing classification accuracy.To evaluate the effectiveness of our approach,we conducted experiments on publicly available HS image datasets,including Pavia University,Washington Mall,and Indian Pines datasets.We compared the performance of our proposed method with several state-of-the-art deep learning(DL)and machine learning(ML)algorithms,including long short-term memory(LSTM),deep neural network(DNN),recurrent neural network(RNN),support vector machine(SVM),and random forest(RF).Our experimental results demonstrate that the Hybrid MOBGWO-DQL significantly improves classification accuracy compared to traditional optimization and DL techniques.MOBGWO-DQL shows greater accuracy in classifying most categories in both datasets used.For the Indian Pine dataset,the MOBGWO-DQL architecture achieved a kappa coefficient(KC)of 97.68%and an overall accuracy(OA)of 94.32%.This was accompanied by the lowest root mean square error(RMSE)of 0.94,indicating very precise predictions with minimal error.In the case of the Pavia University dataset,the MOBGWO-DQL model demonstrated outstanding performance with the highest KC of 98.72%and an impressive OA of 96.01%.It also recorded the lowest RMSE at 0.63,reinforcing its accuracy in predictions.The results clearly demonstrate that the proposed MOBGWO-DQL architecture not only reaches a highly accurate model more quickly but also maintains superior performance throughout the training process.
基金This work was supported by the National Natural Science Foundation of China(Grant No.U20A20197).
文摘We propose a novel image segmentation algorithm to tackle the challenge of limited recognition and segmentation performance in identifying welding seam images during robotic intelligent operations.Initially,to enhance the capability of deep neural networks in extracting geometric attributes from depth images,we developed a novel deep geometric convolution operator(DGConv).DGConv is utilized to construct a deep local geometric feature extraction module,facilitating a more comprehensive exploration of the intrinsic geometric information within depth images.Secondly,we integrate the newly proposed deep geometric feature module with the Fully Convolutional Network(FCN8)to establish a high-performance deep neural network algorithm tailored for depth image segmentation.Concurrently,we enhance the FCN8 detection head by separating the segmentation and classification processes.This enhancement significantly boosts the network’s overall detection capability.Thirdly,for a comprehensive assessment of our proposed algorithm and its applicability in real-world industrial settings,we curated a line-scan image dataset featuring weld seams.This dataset,named the Standardized Linear Depth Profile(SLDP)dataset,was collected from actual industrial sites where autonomous robots are in operation.Ultimately,we conducted experiments utilizing the SLDP dataset,achieving an average accuracy of 92.7%.Our proposed approach exhibited a remarkable performance improvement over the prior method on the identical dataset.Moreover,we have successfully deployed the proposed algorithm in genuine industrial environments,fulfilling the prerequisites of unmanned robot operations.
文摘For traffic object detection in foggy environment based on convolutional neural network(CNN),data sets in fog-free environment are generally used to train the network directly.As a result,the network cannot learn the object characteristics in the foggy environment in the training set,and the detection effect is not good.To improve the traffic object detection in foggy environment,we propose a method of generating foggy images on fog-free images from the perspective of data set construction.First,taking the KITTI objection detection data set as an original fog-free image,we generate the depth image of the original image by using improved Monodepth unsupervised depth estimation method.Then,a geometric prior depth template is constructed to fuse the image entropy taken as weight with the depth image.After that,a foggy image is acquired from the depth image based on the atmospheric scattering model.Finally,we take two typical object-detection frameworks,that is,the two-stage object-detection Fster region-based convolutional neural network(Faster-RCNN)and the one-stage object-detection network YOLOv4,to train the original data set,the foggy data set and the mixed data set,respectively.According to the test results on RESIDE-RTTS data set in the outdoor natural foggy environment,the model under the training on the mixed data set shows the best effect.The mean average precision(mAP)values are increased by 5.6%and by 5.0%under the YOLOv4 model and the Faster-RCNN network,respectively.It is proved that the proposed method can effectively improve object identification ability foggy environment.
文摘Case depth measurement of the induction hardened steel parts is necessary for quality control. Vickers microhardness test is the most industrially accepted method to identify the case depth. But this method is a time consuming one and it requires expensive equipment. The aim of this study is to develop a different method to determine the case depth using image processing. The surface hardened steel samples were cross cut, ground and etched with Nital. The etched macrosectioned specimens were scanned by a scanner. The scanned images were evaluated by the developed software. The principle of the software is to identify the gray level difference. The effective case depths of the surface hardened specimens obtained by Vickers microhardness test and the developed method were compared. It was found that the deviation of the developed method was ±0.12 mm at the case depth range of 0.6 - 2.0 mm and mm at the case depth range of 2.1 - 4.3 mm. The measuring time was only 20% of Vickers microhardness test. The deviation range is much lower than the tolerance case depth specification for induction hardening in general.
文摘Due to their unique structural features, electrospun membranes have gained considerable attention for use in applications where quality of depth filtration is a dominant performance factor. To elucidate the depth filtration phenomena it is important to quantify the intrinsic structural properties independent from the dynamics of transport media. Several methods have been proposed for structural characterization of such membranes. However, these methods do not meet the requirement for the quantification of intrinsic structural properties in depth filtration. This may be due to the complex influence of transport media dynamics and structural elements in the depth filtration process. In addition, the different morphological architectures of electrospun membranes present obstacles to precise quantification. This paper seeks to quantify the structural characteristics of electrospun membranes by introducing a robust image analysis technique and exploiting it to evaluate the permeation-filtration mechanism. To this end, a nanostructured fibrous network was simulated as an ideal membrane using adaptive local criteria in the image analysis. The reliability of the proposed approach was validated with measurements and comparison of structural characteristics in different morphological conditions. The results were found to be well compatible with empirical observations of perfect membrane structures. This approach, based on optimization of electrospinning parameters, may pave the way for producing optimal membrane structures for boosting the performance of electrospun membranes in end-use applications.
基金The National Natural Science Foundation of China ( No.60972001)the National Key Technologies R& D Program of China during the 11th Five-Year Period ( No. 2009BAG13A06)
文摘Aiming at removing fog from traffic images, a distance field is built according to the characteristics of traffic images, and a novel parameter estimation method based on the traffic image sequence is proposed. The fog model is derived from atmospheric scattering models. The direction of the distance field is parallel to the center line of the road, which increases along a line from the observer to the horizon, and the normalization is carried out to improve the distribution of the distance field model. After parameter initialization, the variations of the average gray values of reference regions are taken as the determining conditions to adjust the parameters. Finally, restorations are made by the fog model. Experimental results show that the proposed method can effectively remove fog from traffic images.
基金Funded by the Major State Basic Research Development Program of China(973 Program)(No.2009CB623104)
文摘Proper parameters for image taking and minimum field number for image processing were investigated to evaluate volume fraction of unhydrated cement（UHC） in both neat cement paste and slag blended cement paste. Our research suggested that magnification 250x was sufficient for the two pastes, and accelerating voltage should be set as 15 kV and 20 kV for BSE image taking of neat cement paste and slag blended cement paste respectively; the minimum field number increased while the total imaging area stayed the same as the magnification increased within certain statistical bias.
文摘Objectives： Polymerization shrinkage of dental composites remains a major concern in restorative dentistry because it can lead to micro-cracking of the tooth and debonding at the tooth-restoration interface. The aim of this study was to measure the full-field polymerization shrinkage of dental composites using the optical digital image correlation （DIC） method and to evaluate how the measurement is influenced by the factors in experiment setup and image analysis. Methods： Four commercial dental composites, Premise Dentine, Z 100, Z250 and Tetric EvoCeram, were tested. Composite was first placed into a slot mould to form a bar specimen with rectangular-section of 4 mmx2 mm, followed by the surface painting to create irregular speckles. Curing was then applied at one end of the specimen while the other part were covered against curing light for simulating the clinical curing condition of composite in dental cavity. The painted surface was recorded by a charge-coupled device （CCD） camera before and after curing. Subsequently, the volumetric shrinkage of the specimen was calculated with specialist DIC software based on image cross correlation. In addition, a few factors that may influence the measuring accuracy, including the subset window size, speckle size, illumination light and specimen length, were also evaluated. Results： The volumetric shrinkage of the specimen generally decreases with increasing distance from the irradiated surface with a conspicuous exception being the composite Premise Dentine as its maximum shrinkage occurred at a subsurface distance of about 1 mm instead of the irradiated surface. Z100 had the greatest maximum shrinkage strain, followed by Z250, Tetric EvoCeram and then Premise Dentine. Larger subset window size made the shrinkage strain contour smoother. But the cost was that some details in the heterogeneity of the material were lost. Very small subset window size resulted in a lot of noise in the data, making it difficult to discern the general pattern in the strain distribution. Speckle size did not seem to have obvious effect on the volumetric shrinkage strain along specimen length; however, larger speckles resulted in higher level of noise or heterogeneity in the shrinkage distribution. Compared with bright illumination, dimmer lighting produced larger standard deviations in the measured shrinkage~ indicating a higher level of noise. The longer the specimen, the greater was the rate of reduction with distance from the irradiated surface, especially for the longitudinal strain. Significance： The image correlation method is capable of producing full-field polymerization shrinkage of dental composites. The accuracy of the measurements relies on selection of optimal parameters in experimental setup and DIC analysis.
文摘The development of deep learning has revolutionized image recognition technology.How to design faster and more accurate image classification algorithms has become our research interests.In this paper,we propose a new algorithm called stochastic depth networks with deep energy model(SADIE),and the model improves stochastic depth neural network with deep energy model to provide attributes of images and analysis their characteristics.First,the Bernoulli distribution probability is used to select the current layer of the neural network to prevent gradient dispersion during training.Then in the backpropagation process,the energy function is designed to optimize the target loss function of the neural network.We also explored the possibility of using Adam and SGD combination optimization in deep neural networks.Finally,we use training data to train our network based on deep energy model and testing data to verify the performance of the model.The results we finally obtained in this research include the Classified labels of images.The impacts of our obtained results show that our model has high accuracy and performance.
基金supported by the National Natural Science Foundation of China(Grant No.51727811).
文摘The effect of optical cleaning method combined with laser speckle imaging(LSI)was discussed to improve the detection depth of LSI due to high scattering characteristics of skin,which limit its clinical application.A double-layer skin tissue model embedded with a single blood vessel was established,and the Monte Carlo method was used to simulate photon propagation under the action of light-permeating agent.808 nm semiconductor and 632.8 nm He–Ne lasers were selected to study the e®ect of optical clearing agents(OCAs)on photon deposition in tissues.Results show that the photon energy deposition density in the epidermis increases with the amount of tissue°uid replaced by OCA.Compared with glucose solution,polyethylene glycol 400(PEG 400)and glycerol can considerably increase the average penetration depth of photons in the skin tissue,thereby raising the sampling depth of the LSI.After the action of glycerol,PEG 400,and glucose,the average photon penetration depth is increased by 51.78%,51.06%,and 21.51%for 808nm,68.93%,67.94%,and 26.67%for 632.8 nm lasers,respectively.In vivo experiment by dorsal skin chamber proves that glycerol can cause a substantial decrease in blood°ow rate,whereas PEG 400 can signicantly improve the capability of light penetration without a®ecting blood velocity,which exhibits considerable potential in the monitoring of blood°ow in skin tissues.
基金Project supported by the National Basic Research Program of China(Grant No.2012CB921504)the National Natural Science Foundation of China(Grant Nos.10874088,10904069,and 11028408)the Natural Science Foundation of Jiangsu Province,China(Grant No.SBK201021985)
文摘We study the influence of limited-view scanning on the depth imaging of photoacoustic tomography. The situation, in which absorbers are located at different depths with respect to the limited-view scanning trajectory, is called depth imaging and is investigated in this paper. The results show that limited-view scanning causes the reconstructed intensity of deep absorbers to be weaker than that of shallow ones and that deep absorbers will be invisible if the scanning range is too small. The concept of effective scanning angle is proposed to analyse that phenomenon. We find that an effective scanning angle can well predict the relationship between scanning angle and the intensity ratio of absorbers. In addition, limited-view scanning is employed to improve image quality.
