Resistivity tomography based on multichannel electrodes

With the constantly changing engineering construction sector,the detection accuracy of conventional electrical resistivity tomography(ERT)is no longer suffi cient.A multichannel electrode design(MERT)-based ERT is introduced in this paper to address the growing need for resolution.The imaging accuracy of the ERT method is improved through the collection of apparent resistivity data in various directions by measuring the potential diff erence between diff erent channels.Numerical simulation results of the inclined high-resistivity anomaly model reveal that MERT is a precise representation of the shape,inclined direction,and buried depth of the anomaly,with thoroughfare M2N2 producing the most precise forward and inverse results.Based on the analysis results of the model resolution matrix,when the buried depth of power supply points and the gap between potential acquisition points are 30%-90%and 30%-60%of the electrode distance,respectively,the MERT approach yields superior detection outcomes.The detection eff ect of the MERT method on anomalous bodies with diff erent burial depths under the optimal parameters also indicates that the MERT method can obtain richer potential change information with higher resolution in deep areas compared to the ERT method.With the implementation of the MERT approach,the scope of applications for ERT is expanded,the accuracy of ERT detection is increased,and the progress of near-surface fi ne detection is positively infl uenced.

Advancing automated pupillometry:a practical deep learning model utilizing infrared pupil images

AIM:To establish pupil diameter measurement algorithms based on infrared images that can be used in real-world clinical settings.METHODS:A total of 188 patients from outpatient clinic at He Eye Specialist Shenyang Hospital from Spetember to December 2022 were included,and 13470 infrared pupil images were collected for the study.All infrared images for pupil segmentation were labeled using the Labelme software.The computation of pupil diameter is divided into four steps:image pre-processing,pupil identification and localization,pupil segmentation,and diameter calculation.Two major models are used in the computation process:the modified YoloV3 and Deeplabv 3+models,which must be trained beforehand.RESULTS:The test dataset included 1348 infrared pupil images.On the test dataset,the modified YoloV3 model had a detection rate of 99.98% and an average precision(AP)of 0.80 for pupils.The DeeplabV3+model achieved a background intersection over union(IOU)of 99.23%,a pupil IOU of 93.81%,and a mean IOU of 96.52%.The pupil diameters in the test dataset ranged from 20 to 56 pixels,with a mean of 36.06±6.85 pixels.The absolute error in pupil diameters between predicted and actual values ranged from 0 to 7 pixels,with a mean absolute error(MAE)of 1.06±0.96 pixels.CONCLUSION:This study successfully demonstrates a robust infrared image-based pupil diameter measurement algorithm,proven to be highly accurate and reliable for clinical application.

OPTIMIZATION OF WEIGHTED HIGH-RESOLUTION RANGE PROFILE FOR RADAR TARGET RECOGNITION

For the recognition of high-resolution range profile （HRRP） in radar, the weighted HRRP can reduce the instability of range cells caused by the attitude change of targets. A novel approach is proposed to optimize the weighted HRRP. In the approach, the separability of weighted HRRPs in different targets is measured by de- signing an objective function, and the weighted coefficients are computed by using the gradient descent method, thus enhancing the influence of stable range cells. Simulation results based on five aircraft models show that the approach can effectively optimize the weighted HRRP and improve the recognition accuracy.

A method based on mutual information and gradient information for medical image registration

Mutual information is widely used in medical image registration, because it does not require preprocessing the image. However, the local maximum problem in the registration is insurmountable. We combine mutual information and gradient information to solve this problem and apply it to the non-rigid deformation image registration. To improve the accuracy, we provide some implemental issues, for example, the Powell searching algorithm, gray interpolation and consideration of outlier points. The experimental results show the accuracy of the method and the feasibility in non-rigid medical image registration.

Extended Chirp Scaling Algorithm for Spotlight SAR

Among Synthetic Aperture Radar(SAR)image formation algorithms,the chirp scaling algorithm(CSA)is computationally highly efficient and has been practically used.In this paper,how to implement the subaperture extended CSA in a high resolution spotlight mode SAR is studied in detail.The procedures such as subaperture processing and azimuth processing are discussed.According to the problems presented when using the method(A.Moreira,et al,1996)to process spotlight SAR data,full formula expressions suitable for high squinted angles is presented.While point target simulation is analyzed,the method is validated by E SAR raw data.The results can be directly implemented in the design of SAR processors.

CONSIDERING NEIGHBORHOOD INFORMATION IN IMAGE FUZZY CLUSTERING

Fuzzy C-means clustering algorithm is a classical non-supervised classification method.For image classification, fuzzy C-means clustering algorithm makes decisions on a pixel-by-pixel basis and does not take advantage of spatial information, regardless of the pixels' correlation. In this letter, a novel fuzzy C-means clustering algorithm is introduced, which is based on image's neighborhood system. During classification procedure, the novel algorithm regards all pixels'fuzzy membership as a random field. The neighboring pixels' fuzzy membership information is used for the algorithm's iteration procedure. As a result, the algorithm gives a more smooth classification result and cuts down the computation time.

MIMO RADAR IMAGING MODEL AND ALGORITHM

Based on the array architecture of multiple transmitting/receiving antennas, Multi-Input Multi-Output (MIMO) radar provides a new mechanism for radar imaging technology. In order to explore the processing approach to this imaging mechanism, the two dimensional (2D) imaging model of MIMO radar is established first, and the spatial sampling ability is analyzed from the concept of spatial convolution of the antenna elements. The target spatial spectral filling format of MIMO radar with monochromatic transmitting signal is described. High-resolution imaging capability of MIMO radar is analyzed according to spatial spectral coverage and the corresponding imaging algorithm is presented. Finally, field imaging experiment is used to demonstrate the superior imaging performance of MIMO radar.

Reconstruction of Novel Viewpoint Image Using GRNN

A neural statistical approach to the reconstruction of novel viewpoint image us ing general regression neural networks(GRNN) is presented. Different color value will be obtained by watching the same surface point of an object from different viewpoints due to specular reflection, and the difference is related to the pos ition of viewpoint. The relationship between the position of viewpoint and the c olor of image is non linear, neural network is introduced to make curve fitting , where the inputs of neural network are only a few calibrated images with obvio us specular reflection. By training the neural network, network model is obtaine d. By inputing an arbitrary virtual viewpoint to the model, the image of the vir tual viewpoint can be computed. By using the method presented here, novel viewpo int image with photo realistic property can be obtained, especially images with obvious specular reflection can accurately be generated. The method is an image based rendering method, geometric model of the scene and position of lighting are not needed.

Comparative analysis of different methods for image enhancement

Image enhancement technology plays a very important role to improve image quality in image processing. By enhancing some information and restraining other information selectively, it can improve image visual effect. The objective of this work is to implement the image enhancement to gray scale images using different techniques. After the fundamental methods of image enhancement processing are demonstrated, image enhancement algorithms based on space and frequency domains are systematically investigated and compared. The advantage and defect of the above-mentioned algorithms are analyzed. The algorithms of wavelet based image enhancement are also deduced and generalized. Wavelet transform modulus maxima(WTMM) is a method for detecting the fractal dimension of a signal, it is well used for image enhancement. The image techniques are compared by using the mean(μ),standard deviation(?), mean square error(MSE) and PSNR(peak signal to noise ratio). A group of experimental results demonstrate that the image enhancement algorithm based on wavelet transform is effective for image de-noising and enhancement. Wavelet transform modulus maxima method is one of the best methods for image enhancement.

A Fast Underwater Optical Image Segmentation Algorithm Based on a Histogram Weighted Fuzzy C-means Improved by PSO

The S/N of an underwater image is low and has a fuzzy edge.If using traditional methods to process it directly,the result is not satisfying.Though the traditional fuzzy C-means algorithm can sometimes divide the image into object and background,its time-consuming computation is often an obstacle.The mission of the vision system of an autonomous underwater vehicle (AUV) is to rapidly and exactly deal with the information about the object in a complex environment for the AUV to use the obtained result to execute the next task.So,by using the statistical characteristics of the gray image histogram,a fast and effective fuzzy C-means underwater image segmentation algorithm was presented.With the weighted histogram modifying the fuzzy membership,the above algorithm can not only cut down on a large amount of data processing and storage during the computation process compared with the traditional algorithm,so as to speed up the efficiency of the segmentation,but also improve the quality of underwater image segmentation.Finally,particle swarm optimization (PSO) described by the sine function was introduced to the algorithm mentioned above.It made up for the shortcomings that the FCM algorithm can not get the global optimal solution.Thus,on the one hand,it considers the global impact and achieves the local optimal solution,and on the other hand,further greatly increases the computing speed.Experimental results indicate that the novel algorithm can reach a better segmentation quality and the processing time of each image is reduced.They enhance efficiency and satisfy the requirements of a highly effective,real-time AUV.

Objective measurement for image defogging algorithms

Since there is lack of methodology to assess the performance of defogging algorithm and the existing assessment methods have some limitations,three new methods for assessing the defogging algorithm were proposed.One was using synthetic foggy image simulated by image degradation model to assess the defogging algorithm in full-reference way.In this method,the absolute difference was computed between the synthetic image with and without fog.The other two were computing the fog density of gray level image or constructing assessment system of color image from human visual perception to assess the defogging algorithm in no-reference way.For these methods,an assessment function was defined to evaluate algorithm performance from the function value.Using the defogging algorithm comparison,the experimental results demonstrate the effectiveness and reliability of the proposed methods.

Alternative Fuzzy Cluster Segmentation of Remote Sensing Images Based on Adaptive Genetic Algorithm

Remote sensing image segmentation is the basis of image understanding and analysis. However,the precision and the speed of segmentation can not meet the need of image analysis,due to strong uncertainty and rich texture details of remote sensing images. We proposed a new segmentation method based on Adaptive Genetic Algorithm(AGA) and Alternative Fuzzy C-Means(AFCM) . Segmentation thresholds were identified by AGA. Then the image was segmented by AFCM. The results indicate that the precision and the speed of segmentation have been greatly increased,and the accuracy of threshold selection is much higher compared with traditional Otsu and Fuzzy C-Means(FCM) segmentation methods. The segmentation results also show that multi-thresholds segmentation has been achieved by combining AGA with AFCM.

Inverse synthetic aperture radar imaging based on sparse signal processing

Based on the measurement model of inverse synthetic aperture radar (ISAR) within a small aspect sector,an imaging method was presented with the application of sparse signal processing.This method can form higher resolution inverse synthetic aperture radar images from compensating incomplete measured data,and improves the clarity of the images and makes the feature structure much more clear,which is helpful for target recognition.The simulation results indicate that this method can provide clear ISAR images with high contrast under complex motion case.

Brain MRI Segmentation Using KFCM and Chan-Vese Model

To extract region of interests （ROI） in brain magnetic resonance imaging （MRI） with more than two objects and improve the segmentation accuracy, a hybrid model of a kemel-based fuzzy c-means （KFCM） clustering algorithm and Chan-Vese （CV） model for brain MRI segmentation is proposed. The approach consists of two succes- sive stages. Firstly, the KFCM is used to make a coarse segmentation, which achieves the automatic selection of initial contour. Then an improved CV model is utilized to subdivide the image. Fuzzy membership degree from KFCM clus- tering is incorporated into the fidelity term of the 2-phase piecewise constant CV model to obtain accurate multi-object segmentation. Experimental results show that the proposed model has advantages both in accuracy and in robustness to noise in comparison with fuzzy c-means （FCM） clustering, KFCM, and the hybrid model of FCM and CV on brain MRI segmentation.

Electromagnetic Model and Image Reconstruction Algorithms Based on EIT System

An intuitive 2D model of circular electrical impedance tomography （EIT） sensor with small size electrodes is established based on the theory of analytic functions. The validation of the model is proved using the result from the solution of Laplace equation. Suggestions on to electrode optimization and explanation to the ill-condition property of the sensitivity matrix are provided based on the model, which takes electrode distance into account and can be generalized to the sensor with any simple connected region through a conformal transformation. Image reconstruction algorithms based on the model are implemented to show feasibility of the model using experimental data collected from the EIT system developed in Tianjin University. In the simulation with a human chestlike configuration, electrical conductivity distributions are reconstructed using equi-potential backprojection （EBP） and Tikhonov regularization （TR） based on a conformal transformation of the model. The algorithms based on the model are suitable for online image reconstruction and the reconstructed results are aood both in size and position.

A B-spline active contour model based on finite element method

A B-spline active contour model based on finite element method is presented, into which the advantages of a B-spline active contour attributing to its fewer parameters and its smoothness is built accompanied with reduced computational complexity and better numerical stability resulted from the finite element method. In this model, a cubic B-spline segment is taken as an element, and the finite element method is adopted to solve the energy minimization problem of the B-spline active contour, thus to implement image segmentation. Experiment results verify that this method is efficient for B-spline active contour, which attains stable, accurate and faster convergence.

Characterizing Thermal Augmentation of Convection-Enhanced Drug Delivery with the Fiberoptic Microneedle Device

Convection-enhanced delivery （CED） is a promising technique leveraging pressure-driven flow to increase penetration of infused drugs into interstitial spaces. We have developed a fiberoptic microneedle device for inducing local sub-lethal hyperthermia to further improve CED drug distribution volumes, and this study seeks to quantitatively characterize this approach in agarose tissue phantoms. Infusions of dye were conducted in 0.6% （w/w） agarose tissue phantoms with isothermal conditions at 15 ℃, 20℃, 25 ℃, and 30 ℃. Infusion metrics were quantified using a custom shadowgraphy setup and image- processing algorithm. These data were used to build an empirical predictive temporal model of distribution volume as a function of phantom temperature. A second set of proof- of-concept experiments was conducted to evaluate a novel fiberoptic device capable of generating local photothermal heating during fluid infusion. The isothermal infusions showed a positive correlation between temperature and distribution volume, with the volume at 30℃ showing a 7-fold increase at 100 min over the 15 ℃ isothermal case. Infusions during photothermal heating （1064 nm at 500 mW） showed a similar effect with a 3.5-fold increase at 4 h over the control （0 mW）. These results and analyses serve to provide insight into and characterization of heat-mediated enhancement of volumetric dispersal.

Fusing PLSA model and Markov random fields for automatic image annotation

A novel image auto-annotation method is presented based on probabilistic latent semantic analysis(PLSA) model and multiple Markov random fields(MRF).A PLSA model with asymmetric modalities is first constructed to estimate the joint probability between images and semantic concepts,then a subgraph is extracted served as the corresponding structure of Markov random fields and inference over it is performed by the iterative conditional modes so as to capture the final annotation for the image.The novelty of our method mainly lies in two aspects:exploiting PLSA to estimate the joint probability between images and semantic concepts as well as multiple MRF to further explore the semantic context among keywords for accurate image annotation.To demonstrate the effectiveness of this approach,an experiment on the Corel5 k dataset is conducted and its results are compared favorably with the current state-of-the-art approaches.

Tracking Target Identification Model Based on Multiple Algorithms

In view of current situation of bad data synchronization, image blurring and tracking station stability in tracking target identification, a kind of tracking target identification model based on multiple algorithms was put forward, firstly establishing the image degradation model, using the wavelet algorithm for image preprocessing, doing image edge segmentation by using Robert algorithm after pretreatment, then using the maximum variance threshold method for image threshold segmentation, then extracting target features from the segmented image, and finally using the ABS algorithm to finish target tracking. Experiments proved the proposed model practical and effective.

Vehicle detection algorithm based on codebook and local binary patterns algorithms

Detecting the moving vehicles in jittering traffic scenes is a very difficult problem because of the complex environment.Only by the color features of the pixel or only by the texture features of image cannot establish a suitable background model for the moving vehicles. In order to solve this problem, the Gaussian pyramid layered algorithm is proposed, combining with the advantages of the Codebook algorithm and the Local binary patterns(LBP) algorithm. Firstly, the image pyramid is established to eliminate the noises generated by the camera shake. Then, codebook model and LBP model are constructed on the low-resolution level and the high-resolution level of Gaussian pyramid, respectively. At last, the final test results are obtained through a set of operations according to the spatial relations of pixels. The experimental results show that this algorithm can not only eliminate the noises effectively, but also save the calculating time with high detection sensitivity and high detection accuracy.