Intensity Distribution Information Research Articles

Application of bioinspired geomagnetic sensor measurements and geomagnetic map modeling based on neural networks in simulated navigation

A geomagnetic field is a vector field in which the strength and direction are related to geographical location. Geomagnetic navigation technology, which uses collected geomagnetic field information to achieve positioning and navigation, has the advantages of reliability, stability, accuracy, and concealment. With the deepening research on geomagnetic navigation, bioinspired geomagnetic navigation technology has also been developed, which mainly studies and imitates the magnetic sensing mechanism and navigation behavior of animals, providing new research ideas for geomagnetic navigation technology. The magnetic particle hypothesis and free radical pair hypothesis are two mainstream mechanisms of biological sensing using the geomagnetic field, and studies have shown that these two mechanisms may be coupled within organisms. In this study, we propose a bioinspired weak magnetic vector (BWMV) sensor based on the joint sensing mechanism of magnetic particles and free radicals. It consists of a magnetic rod made of soft magnetic material and a tunnel magnetoresistance (TMR) sensor array. A magnetic rod was used to simulate magnetic particles to convert magnetic field angle information into magnetic field intensity distribution information, and the TMR sensor array was used to simulate the perception of the magnetic field distribution by free radicals. In addition, artificial neural networks (ANNs) were used for BWMV sensors to obtain the mapping relationship between the magnetic field distribution and parameters, which can be used for geomagnetic navigation. To verify the navigation effect of the BWMV sensor in the laboratory, a simulated geomagnetic navigation device was built, and the high-precision mapping relationship from geomagnetic parameters to latitude and longitude information of the selected navigation area was obtained through another ANN. Finally, the effectiveness of the BWMV sensor based on ANNs for geomagnetic navigation is verified using simulated navigation experiments.

Deep learning-based size prediction for optical trapped nanoparticles and extracellular vesicles from limited bandwidth camera detection.

Due to its ability to record position, intensity, and intensity distribution information, camera-based monitoring of nanoparticles in optical traps can enable multi-parametric morpho-optical characterization at the single-particle level. However, blurring due to the relatively long (10s of microsecond) integration times and aliasing from the resulting limited temporal bandwidth affect the detected particle position when considering nanoparticles in traps with strong stiffness, leading to inaccurate size predictions. Here, we propose a ResNet-based method for accurate size characterization of trapped nanoparticles, which is trained by considering only simulated time series data of nanoparticles' constrained Brownian motion. Experiments prove the method outperforms state-of-art sizing algorithms such as adjusted Lorentzian fitting or CNN-based networks on both standard nanoparticles and extracellular vesicles (EVs), as well as maintains good accuracy even when measurement times are relatively short (<1s per particle). On samples of clinical EVs, our network demonstrates a well-generalized ability to accurately determine the EV size distribution, as confirmed by comparison with gold-standard nanoparticle tracking analysis (NTA). Furthermore, by combining the sizing network with still frame images from high-speed video, the camera-based optical tweezers have the unique capacity to quantify both the size and refractive index of bio-nanoparticles at the single-particle level. These experiments prove the proposed sizing network as an ideal path for predicting the morphological heterogeneity of bio-nanoparticles in optical potential trapping-related measurements.

Classroom Energy-saving Control System Design

With the continuous development of science and technology, the degree of automation of various products in life is increasing. However, the impact on energy consumption and environment is getting bigger and bigger, and energy saving and emission reduction is gradually becoming a hot topic in the society. The huge loss of electrical energy in colleges and universities makes the development of energy-saving control technology particularly important. In this paper, we design an energy-saving control system for college classrooms based on STM32 main controller, which mainly collects the light intensity and personnel distribution information of classrooms through photoresistor sensors and infrared pyroelectric sensors, and combines software programming to realize effective and group control of classroom lights, so as to achieve the purpose of energy saving. On this basis, a simple upper computer monitoring interface is built to display the collected information, which facilitates real-time monitoring and management of the classroom. The system is analyzed and introduced in detail in three main aspects in the paper. Firstly, the selection of the main controller and its main hardware circuit modules are introduced; secondly, the software process design is centered around the system; finally, using simulation, analysis and online debugging, the light intensity and personnel distribution information collected can be used to control the lamps and lanterns at the corresponding locations of the personnel to turn on. In summary, this paper designs a classroom energy-saving control system to meet the current situation of large energy consumption in college classrooms, which can realize the effective lighting of the corresponding positions according to the changes of natural light environment and different positions of the personnel in the classroom, and verifies the feasibility of this scheme. It provides an idea to effectively reduce the waste of power resources and achieve the purpose of energy saving and emission reduction in colleges and universities.

Improving Small Lesion Segmentation in CT Scans using Intensity Distribution Supervision: Application to Small Bowel Carcinoid Tumor.

Finding small lesions is very challenging due to lack of noticeable features, severe class imbalance, as well as the size itself. One approach to improve small lesion segmentation is to reduce the region of interest and inspect it at a higher sensitivity rather than performing it for the entire region. It is usually implemented as sequential or joint segmentation of organ and lesion, which requires additional supervision on organ segmentation. Instead, we propose to utilize an intensity distribution of a target lesion at no additional labeling cost to effectively separate regions where the lesions are possibly located from the background. It is incorporated into network training as an auxiliary task. We applied the proposed method to segmentation of small bowel carcinoid tumors in CT scans. We observed improvements for all metrics (33.5% → 38.2%, 41.3% → 47.8%, 30.0% → 35.9% for the global, per case, and per tumor Dice scores, respectively.) compared to the baseline method, which proves the validity of our idea. Our method can be one option for explicitly incorporating intensity distribution information of a target in network training.

IDA-Net: Intensity-distribution aware networks for semantic segmentation of 3D MLS point clouds in indoor corridor environments

• An intensity-distribution aware (IDA) descriptor to mine the intensity information. • A prior mask to describe the intensity-distribution pattern. • A two-stage embedding strategy to improve the performance of fusing different information. Semantic segmentation of 3D mobile laser scanning point clouds is the foundational task for scene understanding in several fields. Most existing segmentation methods tend to simply stack the common point attributes, such as the coordinates and intensity, but ignore their heterogeneous. This paper presents IDA-Net, an intensity-distribution aware network that mines the uniqueness and discrepancy of these two modalities in a separate way for point cloud segmentation under indoor corridor environments. Specifically, IDA-Net consists of two key components. Firstly, an intensity-distribution aware (IDA) descriptor is proposed to mine the intensity distribution pattern. It outputs a multi-channel mask for each point to represent the intensity distribution information. Secondly, a two-stage embedding network is designed to fuse the coordinates and intensity information efficiently. It includes a guiding operation in training stage and a refining operation in testing stage. IDA-Net was evaluated on two indoor corridor areas. Experimental results show that the proposed method significantly improves the performance of segmentation. Specifically, with backbone of KPConv, IDA-Net achieves high mIoU of 90.58% and 88.94% on the above two testing areas respectively, which demonstrates the superiority of the designed IDA descriptor and two-stage embedding network.

ERROR ANALYSIS OF THE DISPERSION-COMPENSATED WIDE-BAND POLARIZATION SAGNAC INTERFEROMETER

Abstract. Imaging polarimetry can obtain two-dimensional intensity distribution information and its corresponding states of polarization from the target simultaneously. Sagnac polarization imaging interferometer is one of the typical channeled polarimetry technologies. By splitting the incident light through the triangular optical structure, the polarization information of the target is modulated into the interference fringes and can be obtained by the demodulation algorithm. The non-ideal optical elements in the system will cause reconstruction errors. This article analyses several factors that affect the measurement accuracy, including the splitting ratio of polarization beam splitter and the diffraction efficiency of the blazed grating. We derive the modified intensity formula and obtain interference patterns through numerical simulations. By calculating the modulation degree of interference fringes and reconstruction error under different non-ideal parameters, we have come to the conclusion that to meet the requirement of modulation degree &gt; 0.5 and reconstruction error &lt; 10%, the beam splitting ratio of the PBS should be α &lt; 1.22. The diffraction efficiency ratio of s-polarization to p-polarization of the blazed grating should be ηs/ηp &gt; 0.83, and the first-order diffraction efficiency should be η1 &gt; 79%.

Estimation of energy consumed by middle-aged recreational marathoners during a marathon using accelerometry-based devices

As long-distance races have substantially increased in popularity over the last few years, the improvement of training programs has become a matter of concern to runners, coaches and health professionals. Triaxial accelerometers have been proposed as a one of the most accurate tools to evaluate physical activity during free-living conditions. In this study, eighty-eight recreational marathon runners, aged 30–45 years, completed a marathon wearing a GENEActiv accelerometer on their non-dominant wrist. Energy consumed by each runner during the marathon was estimated based on both running speed and accelerometer output data, by applying the previously established GENEActiv cut-points for discriminating the six relative-intensity activity levels. Since accelerometry allowed to perform an individualized estimation of energy consumption, higher interpersonal differences in the number of calories consumed by a runner were observed after applying the accelerometry-based approach as compared to the speed-based method. Therefore, pacing analyses should include information of effort intensity distribution in order to adjust race pacing appropriately to achieve the marathon goal time. Several biomechanical and physiological parameters (maximum oxygen uptake, energy cost of running and running economy) were also inferred from accelerometer output data, which is of great value for coaches and doctors.

Infrared and Visible Image Fusion Based on the Total Variational Model and Adaptive Wolf Pack Algorithm

The purpose of image fusion is to merge a substantial amount of information, such as contour, texture and intensity distribution information from original images, with a fusion image. To retain a considerable amount of the fusion image while retaining the texture details of the source image and maintaining the edge of the source image, this paper proposes an improved infrared and visible image fusion algorithm that is based on total variation. First, source infrared and visible light images and a difference image were decomposed by a total variation model, and their respective cartoon and texture components were acquired. A fitness function was solved according to the entropy, standard deviation and edge similarity of the infrared and visible light images. The optimal combination weight of various kinds of texture and cartoon components was sought via a wolf pack intelligence optimization algorithm to acquire the final fusion results with high-quality contrast ratio and edge details. The experimental results indicate that the proposed method not only can preserve edge contour information about the original image but also can effectively retain its texture detail information. The method is superior to the traditional multiscale and sparse representation fusion method with regard to various indicators, such as subjective visual effect, mutual information, gradient information, structural similarity and visual sensitivity.

Distribution Information Based Intuitionistic Fuzzy Clustering for Infrared Ship Segmentation

This paper presents a distribution information based intuitionistic fuzzy clustering method for infrared ship segmentation. The algorithm could effectively suppress the influences of nontarget objects with high intensity and intensity inhomogeneity in the infrared ship images. There are mainly two improvements in this paper. First, it proposes a fuzzy clustering algorithm incorporating global distribution information of ship targets in the form of the Gaussian model. The spatial information, along with intensity, is used to exert different effects on different classes. Second, an intuitionistic fuzzy clustering way is incorporated into the process of ship segmentation, which combines the intensity distribution information of the local region. The intuitionistic fuzzy distance and local intensity distribution information would help in solving the problem of intensity inhomogeneity and blurring edges. Experiment results on the dataset containing 200 infrared ship images indicate the superiority of the proposed method compared with other state-of-the-art methods.

Infrared and visible image fusion using structure-transferring fusion method

It is commonly believed that the purpose of the image fusion is to merge as much information, such as contour, texture and intensity distribution information from original images, as possible into the fusion image. Most of the existing methods treat different source images equally with certain feature extracting operation during the fusion process. However, as for the infrared (IR) and visible image fusion problem, the features of images taken from two imaging devices with different sensitive wave bands are different, sometimes even adverse. We can’t extract and preserve the opposite information at the same time. To keep the targets salient in clutter background and visual friendly, in this paper, a novel IR and visible image fusion method called structure transferring fusion method (STF) is first proposed. Firstly, the structure-transferring model is built to transfer the grayscale structure from the visible input image into the IR image. Secondly, infrared detail enhancing strategy is carried out to supplement the missing details of the IR image. Experimental results reveal that the proposed STF method is both effective and efficient for IR and visible image fusion. The final fusion image with conspicuous targets and vivid texture is conducive to night vision surveillance for human observers.

Enhanced Cerenkov luminescence tomography analysis based on Y2O3:Eu3+ rare earth oxide nanoparticles.

Cerenkov luminescence imaging offers a new diagnostic alternative to radiation imaging, but lacks intensity and penetration. In this study, a Cerenkov luminescence signal and its image quality were enhanced using rare earth oxide nanoparticles as a basis for Cerenkov luminescence excited fluorescence imaging and Cerenkov luminescence excited fluorescence tomography. The results also provided 3D-imaging and quantitative information. The approach was evaluated using phantom and mice models and 3D reconstruction and quantitative studies were performed in vitro, showing improved optical signal intensity, similarity, accuracy, signal-to-noise ratio, and spatial distribution information. The method offers benefits for both optical imaging research and radiopharmaceutical development.

Correlation-coefficient Based Ring-filtering Method for Centering a Non-diffracting Fringe Image

A correlation-coefficient based ring-filtering method for centering a concentric circle fringe is proposed. The image of a non-diffracting beam, whose cross section is a concentric circle fringe, is recorded by a matrix array image sensor and downloaded onto a computer. The image is then processed by ring-filtering, with a center position in the zeroth order interference area of the non-diffracting image. Next, we change the center position of the ring-filtering and calculate the coherence coefficient between the filtered before and after images. The center of the non-diffracting image can be calculated by finding the position of the peak value of the dimensionless coherence coefficient. Because all the intensity distribution information of the non-diffracting beam is used for the calculation in the process of ring-filtering, the effect of random noise or a non-circular fringe on the centering is greatly reduced, and the center position resolution can achieve subpixel accuracy. The measurement of the spatial angle is discussed.

Intensity population based unsupervised hemorrhage segmentation from brain CT images

This article has proposed an intelligent knowledge driven method to segment hemorrhage from brain CT images using the information of pixel intensity population and distribution. A mathematical model is designed to identify the unexpected variation in pixel intensity population in a brain CT image having hemorrhage. Complete batch of multi-slice CT scan images is taken as input. Fusion of knowledge of brain anatomy with intensity distribution information of CT brain image results in a unique solution for hemorrhage segmentation. To test the robustness, segmentation of different types of hemorrhage of different patients is done using the proposed method. The results are accepted and validated by radiology experts. A fully automatic and fast Computer Aided Diagnosis (CAD) is designed, using the proposed method, to segment hemorrhage automatically, in the absence of an expert, for further inspections like checking severity, volume, size, shape and type of hemorrhage. Competence of the CAD is tested against mostly used established clustering methods to demonstrate its potential.

Pattern analysis of protein images from fluorescence microscopy using Gray Level Co-occurrence Matrix

Extraction of useful and discriminative information from fluorescence microscopy protein images is a challenging task in the field of machine learning and pattern recognition.Gray Level Co-occurrence Matrix (GLCM) was among the first methods developed for textural analysis, which holds information of intensity distribution as well as the respective distance of intensity levels in the original image. In this paper, several GLCMs are constructed with different quantization levels for different values of offset d. Haralick descriptors are extracted from each GLCM, which are then utilized to train support vector machines. The final output is obtained through the majority voting scheme. Hybrid models from different individual feature spaces have also been constructed. Additionally, Correlation-based Feature Selection (CFS) is performed to extract the most useful features from the hybrid models.The empirical analysis reveals that varying the value of parameter d causes the GLCM to extract different information from a particular fluorescence microscopy image. Hence, producing diversified co-occurrence matrices for same images. Similarly, using more quantization levels for constructing a GLCM generates informative and discriminative features for the classification phase. Furthermore, CFS has significantly reduced the feature space dimensionality achieving almost the same accuracy as full feature space.The performance of the proposed system is validated using three benchmark datasets including HeLa (99.6%), CHO (100%), and LOCATE Endogenous (100%) datasets. It is anticipated that GLCM is still an efficient technique for pattern analysis in the field of bioinformatics and computational biology as well as might be helpful in drug discovery related applications.

Nanoscale depth reconstruction from defocus: within an optical diffraction model.

Depth from defocus (DFD) based on optical methods is an effective method for depth reconstruction from 2D optical images. However, due to optical diffraction, optical path deviation occurs, which results in blurring imaging. Blurring, in turn, results in inaccurate depth reconstructions using DFD. In this paper, a nanoscale depth reconstruction method using defocus with optical diffraction is proposed. A blurring model is proposed by considering optical diffraction, leading to a much higher accuracy in depth reconstruction. Firstly, Fresnel diffraction in an optical system is analyzed, and a relationship between intensity distribution and depth information is developed. Secondly, a blurring imaging model with relative blurring and heat diffusion is developed through curving fitting of a numerical model. In this way, a new DFD method with optical diffraction is proposed. Finally, experimental results show that this new algorithm is more effective for depth reconstruction on the nanoscale.

위상차 현미경 영상 내 푸리에 묘사자를 이용한 암세포 형태별 분류

Tumor cell morphology is closely related to its migratory behaviors. An active tumor cell has a highly irregular shape, whereas a spherical cell is inactive. Thus, quantitative analysis of cell features is crucial to determine tumor malignancy or to test the efficacy of anticancer treatment. We use 3D time-lapse phase-contrast microscopy to analyze single cell morphology because it enables to observe long-term activity of living cells without photobleaching and phototoxicity, which is common in other fluorescence-labeled microscopy. Despite this advantage, there are image-level drawbacks to phase-contrast microscopy, such as local light effect and contrast interference ring. Therefore, we first corrected for non-uniform illumination artifacts and then we use intensity distribution information to detect cell boundary. In phase contrast microscopy image, cell is normally appeared as dark region surrounded by bright halo ring. Due to halo artifact is minimal around the cell body and has non-symmetric diffusion pattern, we calculate cross sectional plane which intersects center of each cell and orthogonal to first principal axis. Then, we extract dark cell region by analyzing intensity profile curve considering local bright peak as halo area. Finally, we calculated the Fourier descriptor that morphological characteristics of cell to classify tumor cells into active and inactive groups. We validated classification accuracy by comparing our findings with manually obtained results.

Zernike moment and local distribution fitting fuzzy energy-based active contours for image segmentation

This paper presents a new region-based active contour model for extracting the object boundaries in an image, based on techniques of curve evolution. The proposed model introduces an energy functional that involves intensity distributions in local image regions and fuzzy membership functions. The local image intensity distribution information used to guide the motion of the contour, in the paper, is derived by Hueckel operator in the neighborhood of each image point. The parameters of Hueckel operator are estimated by a set of orthogonal Zernike moments before curve evolution. Meanwhile, the fuzzy membership functions are used to measure the association degree of each image pixel to the region outside and inside the contour. To minimize the energy functional, instead of solving the Euler–Lagrange equation of the underlying problem, the paper employs a direct method to compute the energy alterations. As a result, the model can deal with images with intensity inhomogeneity. In addition, the model effectively alleviates the sensitivity to contour initialization. Moreover, the model reduces computational cost, avoids problems associated with choosing time steps as well as allows fast convergence to the segmentation solutions. Experimental results on synthetic, real images and comparisons with other models show the desired performances of the proposed model.

Integrating local distribution information with level set for boundary extraction

This paper presents a general object boundary extraction model for piecewise smooth images, which incorporates local intensity distribution information into an edge-based implicit active contour. Unlike traditional edge-based active contours that use gradient to detect edges, our model derives the neighborhood distribution and edge information with two different region-based operators: a Gaussian mixture model (GMM)-based intensity distribution estimator and the Hueckel operator. We propose the local distribution fitting model for more accurate segmentation, which incorporates the operator outcomes into the recent local binary fitting (LBF) model. The GMM and the Hueckel model parameters are estimated before contour evolution, which enables the use of the proposed model without the need for initial contour selection, i.e., the level set function is initialized with a random constant instead of a distance map. Thus our model essentially alleviates the initialization sensitivity problem of most active contours. Experiments on synthetic and real images show the improved performance of our approach over the LBF model.

An improved algorithm for vessel centerline tracking in coronary angiograms

For automated visualization and quantification of artery diseases, the accurate determination of the arterial centerline is a prerequisite. Existing tracking-based approaches usually suffer from the inaccuracy, inflexion and discontinuity in the extracted centerlines, and they may even fail in complicated situations. In this paper, an improved algorithm for coronary arterial centerline extraction is proposed, which incorporates a new tracking direction updating scheme, a self-adaptive magnitude of linear extrapolation and a dynamic-size search window for matched filtering. A simulation study is conducted for the determination of the optimal weighting factor which is used to combine the geometrical topology information and intensity distribution information to obtain the proposed tracking direction. Synthetic and clinical examples, representing some difficult situations that may occur in coronary angiograms, are presented. Results show that the proposed algorithm outperforms the conventional methods. By adopting the proposed algorithm, centerlines are successfully extracted under these complicated situations, and with satisfactory accuracy.

Hyperspectral texture classification using generalized Markov fields

We present a generalized random field model in a random environment to classify hyperspectral textures. The model generalizes traditional random field models by allowing the spatial interaction parameters of the field to be random variables. Principal component analysis is used to reduce the dimensionality of the data set to a small number of spectral bands that capture almost all of the energy in the original hyperspectral textures. Using the model we obtain a compact feature vector that efficiently computes within- and between-band information. Using a set of hyperspectral samples, we evaluate the performance of this model for classifying textures and compare the results with other approaches that consider different kinds of spatial, spectral, and intensity distribution information.