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Light-spots centroid positions detection is one of the major error sources in the application of Hartmann-Shack (H-S) wavefront sensors. The double images (ghosting) in part of sub-aperture, caused by multiple reflections from optical elements or paraxial stray light from operating environment, result in centroid detection errors and that the precisions of wavefront aberration reconstruction decline. The conventional threshold method often leads to the loss of available information of light-spots, though, by which ghosting can been brought under effective control. In this paper, an improved method and procedure, aiming at ghosting detection and local removal automatically, is proposed by combining several algorithms which include nonlinear processing, autocorrelation, convolution, and local filtering, and so on. Then, the corresponding research has been carried out by a numerical simulation platform established by ourselves, and the results can show that this method and procedure is effective.

A novel analytical methodology for segregating healthy and neurological disorders from gait patterns is proposed by employing a set of oscillating components called intrinsic mode functions (IMF’s). These IMF’s are generated by the Empirical Mode Decomposition of the gait time series and the Hilbert transformed analytic signal representation forms the complex plane trace of the elliptical shaped analytic IMFs. The area measure and the relative change in the centroid position of the polygon formed by the Convex Hull of these analytic IMF’s are taken as the discriminative features. Classification accuracy of 79.31% with Ensemble learning based Adaboost classifier validates the adequacy of the proposed methodology for a computer aided diagnostic (CAD) system for gait pattern identification. Also, the efficacy of several potential biomarkers like Bandwidth of Amplitude Modulation and Frequency Modulation IMF’s and it’s Mean Frequency from the Fourier-Bessel expansion from each of these analytic IMF’s has been discussed for its potency in diagnosis of gait pattern identification and classification.

This paper proposes a novel method for extracting facades structure from real-world pictures by using local geometric moment. Compared with existing methods, the proposed method has advantages of easy-to-implement, low computational cost, and robustness to noises, such as uneven illumination, shadow, and shade from other objects. Besides, our method is faster and has a lower space complexity, making it feasible for mobile devices and the situation where real-time data processing is required. Specifically, a facades structure modal is first proposed to support the use of our special noise reduction method, which is based on a self-adapt local threshold with Gaussian weighted average for image binarization processing and the feature of the facades structure. Next, we divide the picture of the building into many individual areas, each of which represents a door or a window in the picture. Subsequently we calculate the geometric moment and centroid for each individual area, for identifying those collinear ones based on the feature vectors, each of which is thereafter replaced with a line. Finally, we comprehensively analyze all the geometric moment and centroid to find out the facades structure of the building. We compare our result with other methods and especially report the result from the pictures taken in bad environmental conditions. Our system is designed for two application, i.e, the reconstruction of facades based on higher resolution ground-based on imagery, and the positional system based on recognize the urban building.

In finger vein image preprocessing, finger angle correction and ROI extraction are important parts of the system. In this paper, we propose an angle correction algorithm based on the centroid of the vein image, and extract the ROI region according to the bidirectional gray projection method. Inspired by the fact that features in those vein areas have similar appearance as valleys, a novel method was proposed to extract center and width of palm vein based on multi-directional gradients, which is easy-computing, quick and stable. On this basis, an encoding method was designed to determine the gray value distribution of texture image. This algorithm could effectively overcome the edge of the texture extraction error. Finally, the system was equipped with higher robustness and recognition accuracy by utilizing fuzzy threshold determination and global gray value matching algorithm. Experimental results on pairs of matched palm images show that, the proposed method has a EER with 3.21% extracts features at the speed of 27ms per image. It can be concluded that the proposed algorithm has obvious advantages in grain extraction efficiency, matching accuracy and algorithm efficiency.

Content-based image retrieval (CBIR) aims at retrieving from a database objects that are similar to an object provided by a query, by taking into consideration a set of extracted features. While CBIR has been widely applied in the two-dimensional image domain, the retrieval of3D objects from medical image datasets using CBIR remains to be explored. In this context, the development of descriptors that can capture information specific to organs or structures is desirable. In this work, we focus on the retrieval of two anatomical structures commonly imaged by Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) techniques, the left ventricle of the heart and blood vessels. Towards this aim, we developed the Area-Distance Local Descriptor (ADLD), a novel 3D local shape descriptor that employs mesh geometry information, namely facet area and distance from centroid to surface, to identify shape changes. Because ADLD only considers surface meshes extracted from volumetric medical images, it substantially diminishes the amount of data to be analyzed. A 90% precision rate was obtained when retrieving both convex (left ventricle) and non-convex structures (blood vessels), allowing for detection of abnormalities associated with changes in shape. Thus, ADLD has the potential to aid in the diagnosis of a wide range of vascular and cardiac diseases.

In order to improve the measurement accuracy of the laser displacement sensor, the laser triangulation displacement measuring system is established. The spot subdivision location algorithms of the system are studied, such as the Gaussian curve fitting, traditional gray weighted centroid and gray square weighted centroid. First, according to the principle of laser triangulation, the displacement measuring system is built. Then, objects are moved by the high-precision motorized stage, obtaining multiple sets of image data. Next, these data respectively are processed by the above several algorithms to obtain the corresponding coordinates of the laser spot, the system is calibrated respectively. Finally, comparing the displacement of calibration results calculating with the actual displacement. The results show that: those data that are processed by the gray square weighted centroid are used to calibrate , calculating the minimum error, and is 71.1 μm. It concludes that gray square weighted centroid is an ideal location segmentation method, which is not only simple and easy to implement, but also has a higher positioning accuracy.

With the rapid developing of science and technology, large aperture optical system plays an important role in the hightech fields including space optics, astronomical optics, inertial confinement fusion, the detecting and recognizing of space target. However, the problems of the wavefront sensing about large aperture optical system has been totally solved because of the equipment expenses and long manufacturing periods. In order to test the large aperture elements in optical system with cheaper costs and higher resolution, more and more attentions are paid into the wavefront sensing of large aperture optical systems. The scanning pentaprism system is introduced to divide the wavefront of the interferometer into a series of sub-wavefront, and the relative positions of the spot centroid accroding to every sub-wavefront are recorded on the CCD camera. The normal directions of every sub-wavefront are obtained to reconstruct the tested wavefront. Experimental results are accord with the interferometer measure results. The feasibility of the pentaprism scanning method has been validated. Finally the influences of measurement apparatus and environment on the measuring precision is discussed. Which is useful to expand the measuring range to keep high spatial resolution and reduce cost.

This paper analyzes the influence on the centroid detection accuracy by several parameters, including the signal-to-noise ratio, frame rate and spot diameter. It provides the selection basis for the camera used in the centroid detection system. The diameter of the spot has little influence on the centroid detection accuracy within a wide range. Meanwhile, with the same signal-to-noise ratio, when the frame rate of the camera becomes higher, the centroid detection accuracy can be improved through the method of superposing more frames. The measurement software uses the specific multi-frame superposition denoising algorithm based on high speed CMOS camera which solves the conflict of improvement of accuracy and time-consuming of the algorithm. The repeatability of the centroid can reach up to 0.0023 pixel with the measuring speed of 62.5fps, the same as the frame rate of the camera.

Light spot centroid detection is one of the key technologies in optical measurement. In order to overcome the poor stability caused by small scale spot, we proposed an extract method by using saturated light spot in this paper. By increasing the input voltage of LED and adjusting the exposure time of CCD, the image of LED which projected in the image plane become larger, it can help enhancing the stability of light spot centroid extracting. The experiment results showed that the extract stability of saturated light spot has improved obviously compared with small scale spot. This method can be adopted in close range measurement; it reflected the sub-pixel coordinate of spot in image plane coordinate more accurately after calibrating the distorted image.

Star centroiding accuracy decreases significantly when star sensor works under highly dynamic conditions or star images are corrupted by severe noise, reducing the output attitude precision. Herein, an adaptive iteration method is proposed to solve this problem. Firstly, initial star centroids are predicted by traditional method, and then based on initial reported star centroids and angular velocities of the star sensor, adaptive centroiding windows are generated to cover the star area and then an iterative method optimizing the location of centroiding window is used to obtain the final star spot extraction results. Simulation results shows that, compared with traditional star image restoration method and Iteratively Weighted Center of Gravity method, AWI algorithm maintains higher extraction accuracy when rotation velocities or noise level increases.

Laser image data-based target recognition technology is one of the key technologies of laser active imaging systems. This paper discussed the status quo of 3-D imaging development at home and abroad, analyzed the current technological bottlenecks, and built a prototype of range-gated systems to obtain a set of range-gated slice images, and then constructed the 3-D images of the target by binary method and centroid method, respectively, and by constructing different numbers of slice images explored the relationship between the number of images and the reconstruction accuracy in the 3-D image reconstruction process. The experiment analyzed the impact of two algorithms, binary method and centroid method, on the results of 3-D image reconstruction. In the binary method, a comparative analysis was made on the impact of different threshold values on the results of reconstruction, where 0.1, 0.2, 0.3 and adaptive threshold values were selected for 3-D reconstruction of the slice images. In the centroid method, 15, 10, 6, 3, and 2 images were respectively used to realize 3-D reconstruction. Experimental results showed that with the same number of slice images, the accuracy of centroid method was higher than the binary algorithm, and the binary algorithm had a large dependence on the selection of threshold; with the number of slice images dwindling, the accuracy of images reconstructed by centroid method continued to reduce, and at least three slice images were required in order to obtain one 3-D image.

The Dark Energy Spectroscopic Instrument (DESI) is a Stage IV ground-based dark energy experiment to map the large-scale structure of the universe and to probe the nature of dark energy. DESI is a massively multiplexed fiber-fed spectrograph, using a 5000-ffber-positioner focal plane assembly to image millions of galaxies. Since these fiber positioners must be positioned to 10-um accuracy, the focal plane must be mapped to micron level precision. We intend to use illuminated fiducials as point sources to accurately calibrate the focal plane surface. In this study we explored using short single-mode fibers as illuminated fiducials. However, despite the advantages of using single-mode fibers, as a near point source, optical fibers have length-dependence behavior: as shorter tend to guide core light into the cladding, which is not ideal for fiducial centroid-measurements. In this paper, we demonstrate that adding tight helical bends to the fibers eliminates unwanted flux in the cladding, improving centroid measurements by more than 50%. This technique has proven with fibers as short as 2-inches, obtaining centroid with at least 0.5-micron precision. This experiment eliminates fiber-length dependence, thus proving the viability of using short single-mode fibers as illuminated fiducials or similar applications

In this work we proposed a flat null-screen method to test parabolic trough solar collectors (PTSC). The null-screen testing method measures the slope of the test surface and by a numerical integration procedure the shape of the test surface can be obtained. In this work, we show that the test can be sensitive to small surface deformations, such as those caused by sinusoidal deformations with different amplitudes and spatial periods introduced on the PTSC surface. These calculations also show that the attainable theoretical slope accuracy in the rms sense is about 0.34 mrad. This value was obtained under the assumption that is possible to achieve a 1-pixel resolution on the measurement of the position departures of the centroids of the targets of the null-screen.

The Kolmogorov’s theory has been used to explain physical phenomena like the vertical turbulence in atmosphere, others recent works have made new advances and have improved K41 theory. In addition, this theory has been applied to studying different issues associated to measure atmospheric effects, and have special interest to find answers in optics to questions as e.g. at ground level, Could it find edges of two or more close objects, from a distant observer? (Classic resolution problem). Although this subject is still open, we did a model using the statistics of the centroid and the diameter of the laser beam propagated under horizontal turbulence at ground level until the object plane. The goal is to measure efficiently the turbulence effects in the long horizontal path propagation of electromagnetic wave. Natural movement of laser beam within the cavity needs be subtracted from the total transversal displacement in order to obtain a best approach. This simple proposed method is used to find the actual statistics of the centroid and beam diameter on the object plane where the turbulence introduces an additional transversal shift. And it has been tested for different values of horizontal distances under non-controlled environment in a synchronized acquisition scheme. Finally, we show test results in open very strong turbulence with high controlled temperature. This paper presents the implemented tests mainly into laboratory and discuss issues to resolve.

The long trace profiler (LTP) at SPring-8 has been upgraded using a newly developed laser-based slope sensor for precise measurements with high spatial resolution. Simulations of centroid calculation in the slope sensor have been performed to evaluate the ultimate accuracy of slope measurement. A performance test of the LTP has also been performed, and a spatial resolution of 0.6 mm has been confirmed.

An algorithm of detection and tracking of multiple small moving space junks under the complex star sequential images is proposed in this paper. Firstly we take image smoothing and adaptive threshold segment to improve the weight of junks. Furthermore, back neighborhood frame correlation (BNFC) is proposed to detect and locate the junk which is sheltered by bigger interfaced stars. Through cross projection method, we could extract the centroid of the moving junks. At last, the Kalman Filter is used to track and estimate the trajectory of moving junks. Experiments show that through this algorithm the multiple small space junks could be detected and tracked effectively and accurately under complex star background with good performance in low error rate and good real-time processing.

A computer software is proposed in order to determine the centroids of the spots in a Hartmann pattern or Hartmanngrams. The software was developed using algorithms for segmentation of images, which are techniques used in digital image processing. Centroid determination for a Shack-Hartmann pattern is the key point to obtain reliable results. We focus in obtaining good centroid determination for Hartmanngrams under conditions of high noise. The proposed algorithms are the essential part of this work, as they are morphological algorithms, which mainly are modifications of the weighted averaging algorithm. They have several advantages, such as, the adjustment to the shape of every spot of the Hartmanngram and that it is an interactive and automatic software. Although the software is more complete and reliable than other techniques and algorithms, since it can analyze complicated pictures of Hartmanngrams and measure the centroids of the spots.

In our previous publications, we had successfully made a deflectometry measurement by using a portable laser projector. In this research, we propose the beam weighting centroid method rather than previous the phase shifting method for quantification of the angular direction of the testing beam in the tested optics entrance pupil. By projecting the angular sequential lines on tested optics entrance pupil, the wavefront aberration is reconstructed from two orthogonal directions measurements, in a similar way to the line scanning deflectometry. The limited gray scale problem of laser projector during the phase shifting measurement is therefore eliminated. The reconstructed wavefront is proven to yield a more accurate result than the phase shifting methods at the cost of more image frames and acquisition time.

We present a new application of frame transfer Charge-Coupled Device (CCD) on measuring astronomical seeing. If a telescope is equipped with a shutterless, frame transfer CCD camera, a bright star will generate a trail during the frame transfer phase. Because the transfer is very fast, the trail is a series of short exposures (about 1 ms) of the target star. Therefore the centroid is jittery due to atmospheric turbulence, and the amplitude can be utilized to derive astronomical seeing. We present the preliminary results from STA1600FT CCD on the second Antarctic Survey Telescope (AST3) tested in China. The trail seeing moderately agrees with the simultaneous DIMM seeing.

In the telescope observation, the position of fiber will highly influence the spectra efficient input in the fiber to the spectrograph. When the fibers were back illuminated on the spectra end, they would export light on the positioner end, so the CCD cameras could capture the photo of fiber tip position covered the focal plane, calculates the precise position information by light centroid method and feeds back to control system. A set of fiber back illuminated system was developed which combined to the low revolution spectro instruments in LAMOST. It could provide uniform light output to the fibers, meet the requirements for the CCD camera measurement. The paper was introduced the back illuminated system design and different test for the light resource. After optimization, the effect illuminated system could compare with the integrating sphere, meet the conditions of fiber position measurement.Using parallel controlled fiber positioner as the spectroscopic receiver is an efficiency observation system for spectra survey, has been used in LAMOST recently, and will be proposed in CFHT and rebuilt telescope Mayall. In the telescope observation, the position of fiber will highly influence the spectra efficient input in the fiber to the spectrograph. When the fibers were back illuminated on the spectra end, they would export light on the positioner end, so the CCD cameras could capture the photo of fiber tip position covered the focal plane, calculates the precise position information by light centroid method and feeds back to control system. After many years on these research, the back illuminated fiber measurement was the best method to acquire the precision position of fibers. In LAMOST, a set of fiber back illuminated system was developed which combined to the low revolution spectro instruments in LAMOST. It could provide uniform light output to the fibers, meet the requirements for the CCD camera measurement and was controlled by high-level observation system which could shut down during the telescope observation. The paper was introduced the back illuminated system design and different test for the light resource. After optimization, the effect illuminated system could compare the integrating sphere, meet the conditions of fiber position measurement.

The astronomical instrumentation needs high level of image quality and stability. The quality of images processed by an optical instrument can be referred to the size of the spot and/or the point spread function (p.s.f.), while the stability is related to the displacement of the spot centroid during the observations. The importance of new design procedures for astronomical instruments through the direct design of the materials taking into account their functionalities integrating di erent approaches (FEM + raytracing) is then enhanced by the new upcoming requirement.Di erent functional materials can bejoined together exploiting each peculiar property in order to realize an integrated structure better known asSmart Structure. They are capable of sensing and reacting to their environment in a predictable and desired manner, through the integration of various elements, such as sensors, actuators, power sources, signal processors, and communications network.The Paper describes possible application related to twomain functional materials: piezoelectric materials and Shape Memory Alloys.

The Photo-G program conducted by Naval Air Systems Command at the Atlantic Test Range in Patuxent River, Maryland, uses photogrammetric analysis of large amounts of real-world imagery to characterize the motion of objects in a 3-D scene. Current approaches involve several independent processes including target acquisition, target identification, 2-D tracking of image features, and 3-D kinematic state estimation. Each process has its own inherent complications and corresponding degrees of both human intervention and computational complexity. One approach being explored for automated target acquisition relies on exploiting the pixel intensity distributions of photogrammetric targets, which tend to be patterns with bimodal intensity distributions. The bimodal distribution partitioning algorithm utilizes this distribution to automatically deconstruct a video frame into regions of interest (ROI) that are merged and expanded to target boundaries, from which ROI centroids are extracted to mark target acquisition points. This process has proved to be scale, position and orientation invariant, as well as fairly insensitive to global uniform intensity disparities.

The grading of neuroendocrine tumors of the digestive system is dependent on accurate and reproducible assessment of the proliferation with the tumor, either by counting mitotic figures or counting Ki-67 positive nuclei. At the moment, most pathologists manually identify the hotspots, a practice which is tedious and irreproducible. To better help pathologists, we present an automatic method to detect all potential hotspots in neuroendocrine tumors of the digestive system. The method starts by segmenting Ki-67 positive nuclei by entropy based thresholding, followed by detection of centroids for all Ki-67 positive nuclei. Based on geodesic distance, approximated by the nuclei centroids, we compute two maps: an amoeba map and a weighted amoeba map . These maps are later combined to generate the heat map, the segmentation of which results in the hotspots. The method was trained on three and tested on nine whole slide images of neuroendocrine tumors. When evaluated by two expert pathologists, the method reached an accuracy of 92.6%. The current method does not discriminate between tumor, stromal and inflammatory nuclei. The results show that α-shape maps may represent how hotspots are perceived.

Face image symmetry is an important factor affecting the accuracy of automatic face recognition. Selecting high symmetrical face image could improve the performance of the recognition. In this paper, we proposed a novel facial symmetry evaluation scheme based on geometric features, including centroid, singular value, in-plane rotation angle of face and the structural similarity index (SSIM). First, we calculate the value of the four features according to the corresponding formula. Then, we use fuzzy logic algorithm to integrate the value of the four features into a single number which represents the facial symmetry. The proposed method is efficient and can adapt to different recognition methods. Experimental results demonstrate its effectiveness in improving the robustness of face detection and recognition.

The article focuses on the research of moving target detection and tracking algorithm in Aerial monitoring. Study includes moving target detection, moving target behavioral analysis and Target Auto tracking. In moving target detection, the paper considering the characteristics of background subtraction and frame difference method, using background reconstruction method to accurately locate moving targets; in the analysis of the behavior of the moving object, using matlab technique shown in the binary image detection area, analyzing whether the moving objects invasion and invasion direction; In Auto Tracking moving target, A video tracking algorithm that used the prediction of object centroids based on Kalman filtering was proposed.

With the tendency of aging society, countries all over the world are dealing with the demographic change. Fall had been proven to be of the highest fatality rate among the elderly. To realize the elderly fall detection, the proposed algorithm used the hybrid feature. Based on the rate of centroid change, the algorithm adopted VEI to offer the posture feature, this combined motion feature with posture feature. The algorithm also took advantage of SIFT descriptor of VEI(V-SIFT) to show more details of behaviors with occlusion. An improved motion detection method was proposed to improve the accuracy of front-view motion detection. The experimental results on CASIA database and self-built database showed that the proposed approach has high efficiency and strong robustness which effectively improved the accuracy of fall detection.

The article presents a new method to detect small moving targets in space surveillance. Image sequences are processed to detect and track targets under the assumption that the data samples are spatially registered. Maximum value projection and normalization are performed to reduce the data samples and eliminate the background clutter. Targets are then detected through connected component analysis. The velocities of the targets are estimated by centroid localization and least squares regression. The estimated velocities are utilized to track the targets. A sliding neighborhood operation is performed prior to target detection to significantly reduce the computation while preserving as much target information as possible. Actual data samples are acquired to test the proposed method. Experimental results show that the method can efficiently detect small moving targets and track their traces accurately. The centroid locating precision and tracking accuracy of the method are within a pixel.

Centroid computation of Gaussian spot is often conducted to get the exact position of a target or to measure wave-front slopes in the fields of target tracking and wave-front sensing. Center of Gravity (CoG) is the most traditional method of centroid computation, known as its low algorithmic complexity. However both electronic noise from the detector and photonic noise from the environment reduces its accuracy. In order to improve the accuracy, thresholding is unavoidable before centroid computation, and optimum threshold need to be selected. In this paper, the model of Gaussian spot is established to analyze the performance of optimum threshold under different Signal-to-Noise Ratio (SNR) conditions. Besides, two optimum threshold selection methods are introduced: TmCoG (using m % of the maximum intensity of spot as threshold), and TkCoG ( usingμn +κσ n as the threshold), μn and σn are the mean value and deviation of back noise. Firstly, their impact on the detection error under various SNR conditions is simulated respectively to find the way to decide the value of k or m. Then, a comparison between them is made. According to the simulation result, TmCoG is superior over TkCoG for the accuracy of selected threshold, and detection error is also lower.

The triangulation measurement is a kind of active vision measurement. The laser triangulation displacement is widely used with advantages of non-contact, high precision, high sensitivity. The measuring error will increase with the nonlinear and noise disturbance when sensors work in large distance. The paper introduces the principle of laser triangulation measurement and analyzes the measuring error and establishes the compensation error. Spot centroid is extracted with digital image processing technology to increase noise-signal ratio. Results of simulation and experiment show the method can meet requirement of large distance and high precision.

We suggest a linear polarization sensor, which has been developed for the Shack-Hartmann wave-front sensor. Our system is classified into three parts. First, polarization filter using wire-grid-plate structure selects only one polarized wave between two linear orthogonal polarization states. Second, graded index layer refracts the wave passed and gives the wave-front distortion. Third, a lens array, one part of the Shack-Hartmann wave-front sensor, focuses the wave on focal plane. We can detect linear polarization state through measuring displacement of the spot centroid. This study on polarization sensor can offer help on vector field sensing, which is crucial to obtain a complete description of light in nanoscale device.

This paper aims to provide a procedure for improving automated 3D reconstruction methods via vision metrology. The 3D reconstruction problem is generally addressed using two different approaches. On the one hand, vision metrology (VM) systems try to accurately derive 3D coordinates of few sparse object points for industrial measurement and inspection applications; on the other, recent dense image matching (DIM) algorithms are designed to produce dense point clouds for surface representations and analyses. This paper strives to demonstrate a step towards narrowing the gap between traditional VM and DIM approaches. Efforts are therefore intended to (i) test the metric performance of the automated photogrammetric 3D reconstruction procedure, (ii) enhance the accuracy of the final results and (iii) obtain statistical indicators of the quality achieved in the orientation step. VM tools are exploited to integrate their main functionalities (centroid measurement, photogrammetric network adjustment, precision assessment, etc.) into the pipeline of 3D dense reconstruction. Finally, geometric analyses and accuracy evaluations are performed on the raw output of the matching (i.e. the point clouds) by adopting a metrological approach. The latter is based on the use of known geometric shapes and quality parameters derived from VDI/VDE guidelines. Tests are carried out by imaging the calibrated Portable Metric Test Object, designed and built at University College London (UCL), UK. It allows assessment of the performance of the image orientation and matching procedures within a typical industrial scenario, characterised by poor texture and known 3D/2D shapes.

For many target and anomaly detection algorithms, a key step is the estimation of a centroid (relatively easy) and a covariance matrix (somewhat harder) that characterize the background clutter. For a background that can be modeled as a multivariate Gaussian, the centroid and covariance lead to an explicit probability density function that can be used in likelihood ratio tests for optimal detection statistics. But ellipsoidal contours can characterize a much larger class of multivariate density function, and the ellipsoids that characterize the outer periphery of the distribution are most appropriate for detection in the low false alarm rate regime. Traditionally the sample mean and sample covariance are used to estimate ellipsoid location and shape, but these quantities are confounded both by large lever-arm outliers and non-Gaussian distributions within the ellipsoid of interest. This paper compares a variety of centroid and covariance estimation schemes with the aim of characterizing the periphery of the background distribution. In particular, we will consider a robust variant of the Khachiyan algorithm for minimum-volume enclosing ellipsoid. The performance of these different approaches is evaluated on multispectral and hyperspectral remote sensing imagery using coverage plots of ellipsoid volume versus false alarm rate.

We have performed a systematic study regarding the diblock composition to keep the size of the cylinders relatively constant despite the shape of the guiding pattern. We have also explored how some guiding patterns shapes provide acceptable cylindrical assembly using an EUV exposure system. This study assumes that LER is a random phenomenon which conformably follows the shape of the guiding pattern. While the edges of the guiding pattern have fluctuations related to the LER of the EUV resist, as long as the centroid of the guiding pattern remains constant, the rectification characteristics of DSA permits adequate hole formation. In this paper we include the level of LER a guiding pattern can exhibit given a pre-determined diblock copolymer / homopolymer mixture. As the amount of homopolymer increases, the size and placement of the assembled diblock becomes less sensitive to the guiding pattern’s edge roughness. This study also explores how the addition of homopolymer is only effective up to a point, as a homopolymer-rich blend is not able to assemble properly. One of the concerns about homopolymer-rich mixtures is the effect it has in the formation of defects. Such effect has not been fully characterized but this study serves as the basis for testing optimal combinations of materials and lithography settings for an EUV system, with the end goal to enable contact/via printing at lower EUV source power requirements.

The current automation of image-based alignment of NIF high energy laser beams is providing the capability of executing multiple target shots per day. An important aspect of performing multiple shots in a day is to reduce additional time spent aligning specific beams due to perturbations in those beam images. One such alignment is beam centration through the second and third harmonic generating crystals in the final optics assembly (FOA), which employs two retro-reflecting corner cubes to represent the beam center. The FOA houses the frequency conversion crystals for third harmonic generation as the beams enters the target chamber. Beam-to-beam variations and systematic beam changes over time in the FOA corner-cube images can lead to a reduction in accuracy as well as increased convergence durations for the template based centroid detector. This work presents a systematic approach of maintaining FOA corner cube centroid templates so that stable position estimation is applied thereby leading to fast convergence of alignment control loops. In the matched filtering approach, a template is designed based on most recent images taken in the last 60 days. The results show that new filter reduces the divergence of the position estimation of FOA images.

In this paper, we propose an automated method to classify normal/abnormal wall motion in Left Ventricle (LV) function in cardiac cine-Magnetic Resonance Imaging (MRI), taking as reference, strain information obtained from 2D Speckle Tracking Echocardiography (STE). Without the need of pre-processing and by exploiting all the images acquired during a cardiac cycle, spatio-temporal profiles are extracted from a subset of radial lines from the ventricle centroid to points outside the epicardial border. Classical Support Vector Machines (SVM) are used to classify features extracted from gray levels of the spatio-temporal profile as well as their representations in the Wavelet domain under the assumption that the data may be sparse in that domain. Based on information obtained from radial strain curves in 2D-STE studies, we label all the spatio-temporal profiles that belong to a particular segment as normal if the peak systolic radial strain curve of this segment presents normal kinesis, or abnormal if the peak systolic radial strain curve presents hypokinesis or akinesis. For this study, short-axis cine- MR images are collected from 9 patients with cardiac dyssynchrony for which we have the radial strain tracings at the mid-papilary muscle obtained by 2D STE; and from one control group formed by 9 healthy subjects. The best classification performance is obtained with the gray level information of the spatio-temporal profiles using a RBF kernel with 91.88% of accuracy, 92.75% of sensitivity and 91.52% of specificity.

Research on three-dimensional (3D) surface reconstruction from range slices obtained from range-gated laser imaging system is of significance. 3D surfaces reconstructed based on existing binarization method or centroid method are rough or discontinuous in some circumstances. In this paper we address these problems and develop a 3D surface reconstruction algorithm based on the idea that combining the centroid method with weighted linear interpolation and mean filter. The algorithm consists of three steps. In the first step, interesting regions are extracted from each range slice based on mean filter, and then are merged to derive a single range image. In the second step, the derived range image is denoised and smoothed based on adaptive histogram method, weighted linear interpolation and mean filter method respectively. Finally, nonzero valued pixels in the after processed range image are converted to point cloud according to the range-gated imaging parameters, and then 3D surface meshes are established from the point cloud based on the topological relationship between adjacent pixels in the range image. Experiment is conducted on range slices generated from range-gated laser imaging simulation platform, and the registration result of the reconstructed surface of our method with the original surface of the object shows that the proposed method can reconstruct object surface accurately, so it can be used for the designing of reconstruction and displaying of range-gated laser imaging system, and also can be used for 3D object recognition.

Our primary interest is in real-time one-dimensional object’s pose estimation. In this paper, a method to estimate general motion one-dimensional object’s pose, that is, the position and attitude parameters, using a single camera is proposed. Centroid-movement is necessarily continuous and orderly in temporal space, which means it follows at least approximately certain motion law in a short period of time. Therefore, the centroid trajectory in camera frame can be described as a combination of temporal polynomials. Two endpoints on one-dimensional object, A and B, at each time are projected on the corresponding image plane. With the relationship between A, B and centroid C, we can obtain a linear equation system related to the temporal polynomials’ coefficients, in which the camera has been calibrated and the image coordinates of A and B are known. Then in the cases that object moves continuous in natural temporal space within the view of a stationary camera, the position of endpoints on the one-dimensional object can be located and also the attitude can be estimated using two end points. Moreover the position of any other point aligned on one-dimensional object can also be solved. Scene information is not needed in the proposed method. If the distance between the endpoints is not known, a scale factor between the object’s real positions and the estimated results will exist. In order to improve the algorithm’s performance from accuracy and robustness, we derive a pain of linear and optimal algorithms. Simulations’ and experiments’ results show that the method is valid and robust with respect to various Gaussian noise levels. The paper’s work contributes to making self-calibration algorithms using one-dimensional objects applicable to practice. Furthermore, the method can also be used to estimate the pose and shape parameters of parallelogram, prism or cylinder objects.

A target tracking model and a technique for target tracking filtering based on sequential unscented Kalman filter are presented to improve target tracking performance of high resolution radar/infrared imaging sensor composite guidance system. Firstly, a measurement model for imaging sensor based of the centroid of the target is derived from images. Secondly, a measurement model for radar based of the centroid of the target is derived from traits of high resolution radar. Finally, the data fusion filtering framework for target tracking based on sequential unscented Kalman filter is presented. From the results of simulated experiments, average rate and target tracking accuracy of convergence for the technique developed are superior to those of other techniques. In conclusion, the target tracking model and filtering algorithm developed are proper for high resolution radar/infrared imagery sensor composite guidance system.

When starlight navigation method is applied in the hypersonic vehicle, the complex turbulence generated around the window of star sensor causes starlight deflection, thus lead to the centroid offset of navigation star in the star-map imaging. Starting from characteristics of the flow field, the deflection effects of starlight transmission are researched to solve. At first, based on Reynolds average, the model of flow around the window was established to obtain the density distribution that can be divided into mean-time and fluctuation flow field to analyze the whole field. On this basis, the starlight is traced by using the Runge-Kutta method, while taking the principle of refraction, the evaluation index for starlight deflection is derived to characterize the deflection effect of the field. Finally, verify the applicability of the evaluation index through comparative analysis and also study the impact on deflection effect with the follow situations: different installation locations of star sensor, different angles of incident ray, different Mach numbers and wavelengths of starlight. The study provides the predictive information for centroid offset of navigation star in star-map pre processing to improve the efficiency of star-map matching, and also provides the best choice for the work of the star sensor.

In order to recognize parts’ pose on Coordinate Measuring Machine (CMM) correctly and fast, based on the translation of CMM, A single camera stereo vision measurement method for feature points’ 3D coordinate on the measured parts is proposed. According to the double cameras stereo vision principle, a image of the part to be measured is captured by A CCD camera, which is driven by CMM along its X or Y axis, on two different position correspondly. Thus, the part’s single camera stereo vision measurement is realized with the proposed image matching method, which is based on the centroid offset of image edge, on two images of the same feature point on the part, and each feature point’s 3D coordinate in the camera coordinate system can be obtained. The measuring system is set up, and the experiment is conducted. The feature point’s coordinate measuring time is 1.818s, and the difference value, which is between feature points’ 3D coordinate calculated with the experiment result and that measured by CMM in the machine coordinate system, is less than 0.3mm. This measuring result can meet parts’ pose real-time recognization requirement on the intelligent CMM, and also show that the method proposed in this paper is feasible.

Adaptive optics (AO) system can be used to detect and compensate the aberrational wavefront of beam in real time. The compensative ability will be affected by the detecting precision of wavefront. Noise is one of the most important factors that affect the detecting precision of Hartmann-Shack (HS) sensor. Noise can induce the errors of centroid detected by HS sensor, consequently influences the wavefront reconstruction. Based on the characteristic of Charge Coupled Devices (CCD), the model that simulates the detecting precision of wavefront affected by noise is built. Based on the factual application scene of artificial beacon, for typical HS sensor, the effects of noise on the detecting precision of wavefront are simulated, and the relations of wavefront beam spot in CCD, and the number of pixel used to calculate spot centroid are analyzed. Several results that can be used in engineering application are obtained.

IMOTEP is a GPU-based (Graphical Processing Units) software relying on a fast parallel implementation of Fresnel diffraction through successive phase screens. Its applications include active imaging, laser telemetry and passive imaging through turbulence with anisoplanatic spatial and temporal fluctuations. Thanks to parallel implementation on GPU, speedups ranging from 40X to 70X are achieved. The present paper gives a brief overview of IMOTEP models, algorithms, implementation and user interface. It then focuses on major improvements recently brought to the anisoplanatic imaging simulation method. Previously, we took advantage of the computational power offered by the GPU to develop a simulation method based on large series of deterministic realisations of the PSF distorted by turbulence. The phase screen propagation algorithm, by reproducing higher moments of the incident wavefront distortion, provides realistic PSFs. However, we first used a coarse gaussian model to fit the numerical PSFs and characterise there spatial statistics through only 3 parameters (two-dimensional displacements of centroid and width). Meanwhile, this approach was unable to reproduce the effects related to the details of the PSF structure, especially the “speckles” leading to prominent high-frequency content in short-exposure images. To overcome this limitation, we recently implemented a new empirical model of the PSF, based on Principal Components Analysis (PCA), ought to catch most of the PSF complexity. The GPU implementation allows estimating and handling efficiently the numerous (up to several hundreds) principal components typically required under the strong turbulence regime. A first demanding computational step involves PCA, phase screen propagation and covariance estimates. In a second step, realistic instantaneous images, fully accounting for anisoplanatic effects, are quickly generated. Preliminary results are presented.

This paper proposes a new image registration method based on grade-by-grade matching in interferometric inverse synthetic aperture radar (InISAR) imaging system using two antennas. The causation and quantitative analysis of the offset between two ISAR images for different antennas along each baseline is analyzed. Strong scatterer centers (SSCs) are extracted from the ISAR images of each antenna by OTSU method firstly. A standard matching is calculated by the image centroid. Then a mapping of region of interest (ROI) and correlation is carried out to get the precise registration. Simulation results demonstrate that the offset between two ISAR images can be compensated effectively when the proposed method is used, achieving a high quality 3D InISAR image consequently.

Shack-Hartman wave sensor is applied widely in wave-front process with real-time and stable advantages. To increase the testing accuracy of Shack-Hartman sensor, the centroid testing accuracy must be increased first. But the centroid testing accuracy is decided by the detector performance, which is at the system focal plane. The testing accuracy will decrease with detector pixel size increasing. Based on micro-scanning system, the detector in Shack-Hartman wave sensor will receive four images of the wave-front under test. They are rebuilt to a single image after digital image procession. The centroid is calculated and wav-front is rebuilt by wave-front processor. The pixel distance is subdivided to 1/N along X and Y direction respectively with micro-scanning system. N*N is the detected image frames. The sub-pixel shifting images are rebuilt to a whole image after digital image procession. And the resolving power is realized to be increased finally. With application of micro-scanning in Shack-Hartman wave sensor, the system error due to detector accuracy in Shack-Hartman will be decreased. Consequently, the requirement on detector will be decreased. The resolving power of detector is improved greatly. As a result, the image quality testing accuracy of Shack-Hartman wave-front is improved. The image quality testing accuracy of traditional Shack-Hartman wave-front sensor will be increased within the original field of view. And the application range of Shack-Hartman wave sensor is also enlarged effectively.

Neuronal cells play very important role on metabolism regulation and mechanism control, so cell number is a fundamental determinant of brain function. Combined suitable cell-labeling approaches with recently proposed three-dimensional optical imaging techniques, whole mouse brain coronal sections can be acquired with 1-μm voxel resolution. We have developed a completely automatic pipeline to perform cell centroids detection, and provided three-dimensional quantitative information of cells in the primary motor cortex of C57BL/6 mouse. It involves four principal steps: i) preprocessing; ii) image binarization; iii) cell centroids extraction and contour segmentation; iv) laminar density estimation. Investigations on the presented method reveal promising detection accuracy in terms of recall and precision, with average recall rate 92.1% and average precision rate 86.2%. We also analyze laminar density distribution of cells from pial surface to corpus callosum from the output vectorizations of detected cell centroids in mouse primary motor cortex, and find significant cellular density distribution variations in different layers. This automatic cell centroids detection approach will be beneficial for fast cell-counting and accurate density estimation, as time-consuming and error-prone manual identification is avoided.

We present a simple method to measure the spatial coherence of a partially coherent field by analyzing far-field measurements with and without a well-characterized obscuration. From these measurements, the coherence can be estimated for all pairs of points whose centroid is the obstacle's centroid. By scanning the obstacle over the test plane, one can recover the four-dimensional coherence function. In principle, such measurements can be performed without any refractive or diffractive elements, allowing them to be done in higher frequency regimes.

This study analyzes the measurement errors of three dimensional coordinates of binocular stereo vision for tomatoes based on three stereo matching methods, centroid-based matching, area-based matching, and combination matching to improve the localization accuracy of the binocular stereo vision system of tomato harvesting robots. Centroid-based matching was realized through the matching of the feature points of centroids of tomato regions. Area-based matching was realized based on the gray similarity between two neighborhoods of two pixels to be matched in stereo images. Combination matching was realized using the rough disparity acquired through centroid-based matching as the center of the dynamic disparity range which was used in area-based matching. After stereo matching, three dimensional coordinates of tomatoes were acquired using the triangle range finding principle. Test results based on 225 stereo images captured at the distances from 300 to 1000 mm of 3 tomatoes showed that the measurement errors of x coordinates were small, and can meet the need of harvesting robots. However, the measurement biases of y coordinates and depth values were large, and the measurement variation of depth values was also large. Therefore, the measurement biases of y coordinates and depth values, and the measurement variation of depth values should be corrected in the future researches.

The calculation of star point centroid is a key step of improving star tracker measuring error. A star map photoed by APS detector includes several noises which have a great impact on veracity of calculation of star point centroid. Through analysis of characteristic of star map noise, an algorithm of calculation of star point centroid based on background forecast is presented in this paper. The experiment proves the validity of the algorithm. Comparing with classic algorithm, this algorithm not only improves veracity of calculation of star point centroid, but also does not need calibration data memory. This algorithm is applied successfully in a certain star tracker.

The Nano-JASMINE mission has been designed to perform absolute astrometric measurements with unprecedented accuracy; the end-of-mission parallax standard error is required to be of the order of 3 milli arc seconds for stars brighter than 7.5 mag in the zw-band(0.6μm-1.0μm) .These requirements set a stringent constraint on the accuracy of the estimation of the location of the stellar image on the CCD for each observation. However each stellar images have individual shape depend on the spectral energy distribution of the star, the CCD properties, and the optics and its associated wave front errors. So it is necessity that the centroiding algorithm performs a high accuracy in any observables. Referring to the study of Gaia, we use LSF fitting method for centroiding algorithm, and investigate systematic error of the algorithm for Nano-JASMINE. Furthermore, we found to improve the algorithm by restricting sample LSF when we use a Principle Component Analysis. We show that centroiding algorithm error decrease after adapted the method.

We present advances on Spline based ABerration REconstruction (SABRE) from (Shack-)Hartmann (SH) wavefront measurements for large-scale adaptive optics systems. SABRE locally models the wavefront with simplex B-spline basis functions on triangular partitions which are defined on the SH subaperture array. This approach allows high accuracy through the possible use of nonlinear basis functions and great adaptability to any wavefront sensor and pupil geometry. The main contribution of this paper is a distributed wavefront reconstruction method, D-SABRE, which is a 2 stage procedure based on decomposing the sensor domain into sub-domains each supporting a local SABRE model. D-SABRE greatly decreases the computational complexity of the method and removes the need for centralized reconstruction while obtaining a reconstruction accuracy for simulated E-ELT turbulences within 1% of the global method's accuracy. Further, a generalization of the methodology is proposed making direct use of SH intensity measurements which leads to an improved accuracy of the reconstruction compared to centroid algorithms using spatial gradients.