3D depth image analysis for indoor fall detection of elderly people

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.

Three-Dimensional Space Interpolation of Grey / Depth Image Sequence-A New Technique of Computer Graphics Synthesis

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.

Optical coherence tomography enhanced depth imaging of chorioretinal folds in patients with orbital tumors

AIM:To characterize spectral-domain optical coherence tomography(SD-OCT)features of chorioretinal folds in orbital mass imaged using enhanced depth imaging(EDI).METHODS:Prospective observational case-control study was conducted in 20 eyes of 20 patients,the uninvolved eye served as a control.All the patients underwent clinical fundus photography,computed tomography,EDI SDOCT imaging before and after surgery.Two patients with cavernous hemangiomas underwent intratumoral injection of bleomycin A5;the remaining patients underwent tumor excision.Patients were followed 1 to 14mo following surgery(average follow up,5.8mo).RESULTS:Visual acuity prior to surgery ranged from 20/20 to 20/200.Following surgery,5 patients’visual acuity remained unchanged while the remaining 15 patients had a mean letter improvement of 10(range 4 to 26 letters).Photoreceptor inner/outer segment defects were found in 10 of 15 patients prior to surgery.Following surgical excision,photoreceptor inner/outer segment defects fully resolved in 8 of these 10 patients.CONCLUSION:Persistence of photoreceptor inner/outer segment defects caused by compression of the globe by an orbital mass can be associated with reduced visual prognosis.Our findings suggest that photoreceptor inner/outer segment defects on EDI SD-OCT could be an indicator for immediate surgical excision of an orbital mass causing choroidal compression.

Low-Brightness Object Recognition Based on Deep Learning

This research focuses on addressing the challenges associated with image detection in low-light environments,particularly by applying artificial intelligence techniques to machine vision and object recognition systems.The primary goal is to tackle issues related to recognizing objects with low brightness levels.In this study,the Intel RealSense Lidar Camera L515 is used to simultaneously capture color information and 16-bit depth information images.The detection scenarios are categorized into normal brightness and low brightness situations.When the system determines a normal brightness environment,normal brightness images are recognized using deep learning methods.In low-brightness situations,three methods are proposed for recognition.The first method is the SegmentationwithDepth image(SD)methodwhich involves segmenting the depth image,creating amask from the segmented depth image,mapping the obtained mask onto the true color(RGB)image to obtain a backgroundreduced RGB image,and recognizing the segmented image.The second method is theHDVmethod(hue,depth,value)which combines RGB images converted to HSV images(hue,saturation,value)with depth images D to form HDV images for recognition.The third method is the HSD(hue,saturation,depth)method which similarly combines RGB images converted to HSV images with depth images D to form HSD images for recognition.In experimental results,in normal brightness environments,the average recognition rate obtained using image recognition methods is 91%.For low-brightness environments,using the SD method with original images for training and segmented images for recognition achieves an average recognition rate of over 82%.TheHDVmethod achieves an average recognition rate of over 70%,while the HSD method achieves an average recognition rate of over 84%.The HSD method allows for a quick and convenient low-light object recognition system.This research outcome can be applied to nighttime surveillance systems or nighttime road safety systems.

Detecting early changes in choroidal vascularity and thickness using optical coherence tomography in patients with corneal crosslinking for keratoconus

AIM:To investigate changes in choroidal thickness and vascularity in keratoconus patients treated with corneal crosslinking.METHODS:This study evaluated 28 eyes of 22 patients with keratoconus who underwent corneal crosslinking.The choroidal thicknesses were evaluated on enhanced depth imaging optical coherence tomography at the preoperative and postoperative 3d,1,and 3mo.Choroidal thickness in the four cardinal quadrants and the fovea were evaluated.The choroidal vascularity index was also calculated.RESULTS:There was no significant difference in central choroidal thickness between the preoperative and postoperative 3d,1mo(P>0.05).There was a significant increase in the 3mo(P=0.034)and a significant decrease in the horizontal choroidal vascularity index on the postoperative 3d(P=0.014),there was no statistically significant change in vertical axes and other visits in horizontal sections(P>0.05).CONCLUSION:This study sheds light on choroidal changes in postoperative corneal crosslinking for keratoconus.While it suggests the procedure’s relative safety for submacular choroid,more extensive research is necessary to confirm these findings and their clinical significance.

Improving sampling depth of laser speckle imaging by topical optical clearing:A theoretical and in vivo study

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.

Influence of limited-view scanning on depth imaging of photoacoustic tomography

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.

Optical coherence tomography with or without enhanced depth imaging for peripapillary retinal nerve fiber layer and choroidal thickness

AIM：To assess peripapillary retinal nerve fiber layer（RNFL）and choroidal thickness obtained with enhanced depth imaging（EDI）mode compared with those obtained without EDI mode using Heidelberg Spectralis optical coherence tomography（OCT）.METHODS：Fifty eyes of 25 normal healthy subjects and32 eyes of 20 patients with different eye diseases were included in the study.All subjects underwent 3.4 mm diameter peripapillary circular OCT scan centered on the optic disc using both the conventional and the EDI OCT protocols.The visualization of RNFL and choroidoscleral junction was assessed using an ordinal scoring scale.The paired t-test,intraclass correlation coefficient（ICC）,95%limits of agreement（LoA）,and Bland and Altman plots were used to test the agreement of measurements.RESULTS：The visibility score of RNFL obtained with and without EDI was of no significant difference（P=0.532）,the visualization of choroidoscleral junction was better using EDI protocol than conventional protocol（P〈0.001）.Peripapillary RNFL thickness obtained with EDI was slightly thicker than that obtained without EDI（103.25±9.42μm vs 101.87±8.78μm,P=0.010）.The ICC of the two protocols was excellent with the value of 0.867 to 0.924,the 95%LoA of global RNFL thickness was between-10.0 to 7.4μm.Peripapillary choroidal thickness obtained with EDI was slightly thinner than that obtained without EDI（147.23±51.04μm vs 150.90±51.84μm,P〈0.001）.The ICC was also excellent with the value of 0.960 to 0.987,the 95%LoA of global choroidal thickness was between-12.5 to 19.8μm.CONCLUSION：Peripapillary circular OCT scan with or without EDI mode shows comparable results in the measurement of peripapillary RNFL and choroidal thickness.

A photoacoustic imaging system with variable gain at different depths

We established a photoacoustic imaging(PAI)system that can provide variable gain at different depths.The PAI system consists of a pulsed laser with an optical parametric oscillator working at a 728 nmwavelength and an imaging-acquisition-and-processing unit with an ultrasound transducer.Avoltage-controlled attenuator was used to realize variable gain at different depths when acquiring PAI signals.The proof-of-concept imaging results for variable gain at different depths were achieved using specic phantoms.Both resolution and optical contrast obtained through the results of variable gain for a targeted depth range are better than those of constant gain for all depths.To further testify the function,we imaged the sagittal section of the body of in vivo nude mice.In addition,we imaged an absorption sample embedded in a chicken breast tissue,reaching a maximum imaging depth of
4.6 cm.The results obtained using the proposed method showed better resolution and contrast than when using 50 dB gain for all depths.The depth range resolution was
1 mm,and the maximum imaging depth of our system reached
4.6 cm.Furthermore,blood vessels can be revealed and targeted depth range can be selected in nude mice imaging.

Standardization of choroidal thickness measurements using enhanced depth imaging optical coherence tomography

AIM: To describe and evaluate a standardized protocol for measuring the choroidal thickness(Ch T) using enhanced depth imaging optical coherence tomography(EDI OCT).METHODS: Single 9 mm EDI OCT line scans across the fovea were used for this study. The protocol used in this study classified the EDI OCT images into four groups based on the appearance of the choroidal-scleral interface and suprachoroidal space. Two evaluation iterations of experiments were performed: first, the protocol was validated in a pilot study of 12 healthy eyes. Afterwards, the applicability of the protocol was tested in 82 eyes of patients with diabetes. Inter-observer and intra-observer agreements on image classifications were performed using Cohen’s kappa coefficient(κ). Intraclass correlation coefficient(ICC) and Bland-Altman’s methodology were used for the measurement of the Ch T.RESULTS: There was a moderate(κ=0.42) and perfect(κ =1) inter- and intra-observer agreements on image classifications from healthy eyes images and substantial(κ =0.66) and almost perfect(κ =0.86) agreements from diabetic eyes images. The proposed protocol showed excellent inter- and intra-observer agreements for the Ch T measurements on both, healthy eyes and diabetic eyes(ICC 】0.90 in all image categories). The Bland-Altman plot showed a relatively large Ch T measurement agreement in the scans that contained less visible choroidal outer boundary. CONCLUSION: A protocol to standardize Ch T measurements in EDI OCT images has been developed;the results obtained using this protocol show that the technique is accurate and reliable for routine clinical practice and research.

Peripapillary choroidal thickness in Chinese children using enhanced depth imaging optical coherence tomography

AIM：To evaluate the peripapillary choroidal thickness（PPCT） in Chinese children,and to analyze the influencing factors.METHODS： PPCT was measured with enhanced depth imaging optical coherence tomography（EDI-OCT） in 70children（53 myopes and 17 non-myopes） aged 7 to 18 y,with spherical equivalent refractive errors between 0.50and-5.87 diopters（D）.Peripapillary choroidal imaging was performed using circular scans of a diameter of 3.4 mm around the optic disc.PPCT was measured by EDI-OCT in six sectors： nasal（N）,superonasal（SN）,superotemporal（ST）,temporal（T）,inferotemporal（IT）and inferonasal（IN）,as well as global RNFL thickness（G）.RESULTS： The mean global PPCT was 165.49±33.76 μm.The temporal,inferonasal,inferotemporal PPCT were significantly thinner than the nasal,superonasal,superotemporal segments PPCT were significantly thinner in the myopic group at temporal,superotemporal and inferotemporal segments.The axial length was significantly associated with the average global（β=-0.419,P =0.014）,superonasal（β=-2.009,P=0.049） and inferonasal（β=-2.000,P=0.049） PPCT.The other factors（gender,age,SE） were not significantly associated with PPCT.CONCLUSION： PPCT was thinner in the myopic group at temporal,superotemporal and inferotemporal segments.The axial length was found to be negatively correlated to PPCT.We need more further studies about the relationship between PPCT and myopia.

Experimental Detection of Depth of Field for a Thermal Light Lensless Ghost Imaging System

We propose optical experiments to study the depth of field for a thermal light lensless ghost imaging system. It is proved that the diaphragm is an important factor to influence the depth of field, and the ghost images of two detected objects with longitudinal distance less than the depth of field can be achieved simultaneously. The longitudinal coherence scale of the thermal light lensless ghost imaging determines the depth of field. Theoretical analysis can well explain the experimental results.

A Novel Capability of Object Identification and Recognition Based on Integrated mWMM

In the last decade,there has been remarkable progress in the areas of object detection and recognition due to high-quality color images along with their depth maps provided by RGB-D cameras.They enable artificially intelligent machines to easily detect and recognize objects and make real-time decisions according to the given scenarios.Depth cues can improve the quality of object detection and recognition.The main purpose of this research study to find an optimized way of object detection and identification we propose techniques of object detection using two RGB-D datasets.The proposed methodology extracts image normally from depth maps and then performs clustering using the Modified Watson Mixture Model(mWMM).mWMM is challenging to handle when the quality of the image is not good.Hence,the proposed RGB-D-based system uses depth cues for segmentation with the help of mWMM.Then it extracts multiple features from the segmented images.The selected features are fed to the Artificial Neural Network(ANN)and Convolutional Neural Network(CNN)for detecting objects.We achieved 92.13%of mean accuracy over NYUv1 dataset and 90.00%of mean accuracy for the Redweb_v1 dataset.Finally,their results are compared and the proposed model with CNN outperforms other state-of-the-art methods.The proposed architecture can be used in autonomous cars,traffic monitoring,and sports scenes.

Vision-based measuring method for individual cow feed intake using depth images and a Siamese network

Feed intake is an important indicator to reflect the production performance and disease risk of dairy cows,which can also evaluate the utilization rate of pasture feed.To achieve an automatic and non-contact measurement of feed intake,this paper proposes a method for measuring the feed intake of cows based on computer vision technology with a Siamese network and depth images.An automated data acquisition system was first designed to collect depth images of feed piles and constructed a dataset with 24150 samples.A deep learning model based on the Siamese network was then constructed to implement non-contact measurement of feed intake for dairy cows by training with collected data.The experimental results show that the mean absolute error(MAE)and the root mean square error(RMSE)of this method are 0.100 kg and 0.128 kg in the range of 0-8.2 kg respectively,which outperformed existing works.This work provides a new idea and technology for the intelligent measuring of dairy cow feed intake.

Joint head pose and facial landmark regression from depth images

This paper presents a joint head pose and facial landmark regression method with input from depth images for realtime application. Our main contributions are: firstly, a joint optimization method to estimate head pose and facial landmarks, i.e., the pose regression result provides supervised initialization for cascaded facial landmark regression, while the regression result for the facial landmarks can also help to further refine the head pose at each stage. Secondly,we classify the head pose space into 9 sub-spaces, and then use a cascaded random forest with a global shape constraint for training facial landmarks in each specific space. This classification-guided method can effectively handle the problem of large pose changes and occlusion.Lastly, we have built a 3D face database containing 73 subjects, each with 14 expressions in various head poses. Experiments on challenging databases show our method achieves state-of-the-art performance on both head pose estimation and facial landmark regression.

Combined application of extended depth of field imaging, image stitching and polarized microscopy techniques in identification of Spatholobus suberectus

Objective:As traditional techniques for microscopic identification of Chinese medicines currently lack objective and high-quality reference images,here we developed a systemic procedure to be used in microscopic identification of Chinese medicines,which would lead to more objective,effective and accurate identification process.Methods:Spatholobi Caulis(Jixueteng in Chinese)was used as the specimen in the development of such procedure.Jixueteng samples were microscopically examined in bright-and dark-field microscopy.Microscopic images were obtained by regular,EDF,and image stitching techniques.Results:The microscopic images of the characteristics in pulverized Jixueteng were captured,thanks to EDF imaging and image stitching techniques which allowed the detailed and full sighting of each characteristic to be obtained simultaneously.Different layers in anatomical transverse section,including cork,phelloderm,cortex,phloem,cambium,xylem and pith,were distinctively observed.Moreover,by comparing images of bright-and dark-field microscopy,birefringent and non-birefringent components could readily be distinguished.Conclusion:With application of the developed procedure,high-definition,panoramic and microscopic images were acquired,which could be used as the reference images for microscopic identification of Chinese medicines.

An amplitude-preserved adaptive focused beam seismic migration method

Gaussian beam migration （GBM） is an effec- tive and robust depth seismic imaging method, which overcomes the disadvantage of Kirchhoff migration in imaging multiple arrivals and has no steep-dip limitation of one-way wave equation migration. However, its imaging quality depends on the initial beam parameters, which can make the beam width increase and wave-front spread with the propagation of the central ray, resulting in poor migration accuracy at depth, especially for exploration areas with complex geological structures. To address this problem, we present an adaptive focused beam method for shot-domain prestack depth migration. Using the infor- mation of the input smooth velocity field, we first derive an adaptive focused parameter, which makes a seismic beam focused along the whole central ray to enhance the wave- field construction accuracy in both the shallow and deep regions. Then we introduce this parameter into the GBM, which not only improves imaging quality of deep reflectors but also makes the shallow small-scale geological struc- tures well-defined. As well, using the amplitude-preserved extrapolation operator and deconvolution imaging condi- tion, the concept of amplitude-preserved imaging has been included in our method. Typical numerical examples and the field data processing results demonstrate the validity and adaptability of our method.

Estimation of particle depth from two defocused images using the Fourier transform

Depth from defocus is one technology for depth estimation.We estimate particle depth information from two defocused images captured simultaneously by two coaxial cameras with different imaging distances.The images are processed with the Fourier transform to obtain the characteristic parameter(i.e.,the standard deviation of the relative blur kernel of these two defocused images).First,we theoretically analyze the functional relationship between the object depth and the standard deviation or variation of the relative blur kernel.Then,we verify the relationship experimentally.We analyze the influence of particle size,window size and image noise on the calibration curves using both numerical simulations and experiments.We obtain the depth range and accuracy of this measurement system experimentally.For the verification experiments,we use a sample of glass microbeads and the irregularly-shaped dust particles on a microscope slide.Both of these experiments present a suitable depth measurement result.Finally,we apply the measuring system to the depth estimation of drops from a small anti-fogging spray.The results show that our system and image processing algorithm are robust for different types of particles,facilitating the in-line three-dimensional positioning of particles.

Hand Gesture Recognition Using Appearance Features Based on 3D Point Cloud

This paper presents a method for hand gesture recognition based on 3D point cloud. Digital image processing technology is used in this research. Based on the 3D point from depth camera, the system firstly extracts some raw data of the hand. After the data segmentation and preprocessing, three kinds of appearance features are extracted, including the number of stretched fingers, the angles between fingers and the gesture region’s area distribution feature. Based on these features, the system implements the identification of the gestures by using decision tree method. The results of experiment demonstrate that the proposed method is pretty efficient to recognize common gestures with a high accuracy.

Plane extraction for navigation of humanoid robot

In order to make the humanoid robot walk freely in complicated circumstance, the reliable capabilities for obtaining plane information from its surroundings are demanded. A system for extracting planes from data taken by stereo vision was presented, After the depth image was obtained, the pixels of each line were scanned and split into straight line segments. The neighbouring relation of line segments was kept in link structure. The groups of three line segments were selected as seed regions. A queue was maintained for storing seed regions, and then the plane region was expanded around the seed region. The process of region growing continued until the queue of seed regions was empty. After trimming, the edges of the planes became smooth. In the end, extracted planes were obtained. In the experiment, two models were used： pipe and stairs. Two planes in pipe mode/and six planes in stairs model were extracted exactly. The speed and precision of algorithm can satisfy the demands of humanoid robot＇s navigation.