Different Fields Of View Research Articles

Simple Shading Correction Method for Brightfield Whole Slide Imaging.

Whole slide imaging (WSI) refers to the process of creating a high-resolution digital image of a whole slide. Since digital images are typically produced by stitching image sequences acquired from different fields of view, the visual quality of the images can be degraded owing to shading distortion, which produces black plaid patterns on the images. A shading correction method for brightfield WSI is presented, which is simple but robust not only against typical image artifacts caused by specks of dust and bubbles, but also against fixed-pattern noise, or spatial variations in pixel values under uniform illumination. The proposed method comprises primarily of two steps. The first step constructs candidates of a shading distortion model from a stack of input image sequences. The second step selects the optimal model from the candidates. The proposed method was compared experimentally with two previous state-of-the-art methods, regularized energy minimization (CIDRE) and background and shading correction (BaSiC) and showed better correction scores, as smooth operations and constraints were not imposed when estimating the shading distortion. The correction scores, averaged over 40 image collections, were as follows: proposed method, 0.39 ± 0.099; CIDRE method, 0.67 ± 0.047; BaSiC method, 0.55 ± 0.038. Based on the quantitative evaluations, we can confirm that the proposed method can correct not only shading distortion, but also fixed-pattern noise, compared with the two previous state-of-the-art methods.

Simultaneous multipolarization and high-resolution oxygen A-band spectrometer optical design with the astigmatism-corrected method.

Simultaneous multipolarization and high-resolution oxygen A-band spectrometer (SPHABS) is proposed. The astigmatism correction theory is used to separate beams from different fields of view and make it possible to obtain multiple polarization information simultaneously. SPHABS' design and the basic principle of SPHABS and the astigmatism correction theory are elaborated in detail. The ray-tracing results of the model showed that the resolution reached 0.016 nm and information from four fields of view could be obtained simultaneously on the image surface.

HISTOPATOLOGI JANTUNG TIKUS PADA TOKSISITAS SUB KRONIS EKSTRAK ETANOL 70% KULIT BUAH JERUK Citrus nobilis Lour.

This research aimed to study the histopathological heart rat of sub chronic toxicity 70% ethanol extract Citrus nobilis Lourr. Peel. This study used 40 male white Galur Wistar rats in sub-chronic toxicity and each divided into 4 groups (control group giving CMC Na 0,5%, dose group 40 mg/kg BB, dose group 200 mg/kg BB, and dose group 1000 mg/kb BB). Each group consists of 10 males. Each cage contains 5 rats of the same age and gender, fed and drunk in ad libitum method. Furthermore, the rats are being weighing in. 70% extraction of Citrus nobilis Lour peel is orally given to the rats, every day for 90 days accordance with the group. After rats were given dose treatment for 90 days then observed histopathology of heart. Each rat organ preparation was observed in five different fields of view then scored and processed with rank ratings then analyzed using non parametric statistical test using Kruskal Wallis test. 70% extraction of Citrus nobilis Lour peel for 90 days with a dose of 1000mg / kg of rats based on statistical tests showed damaged heart muscle cell on histopathological observation of the heart. It is then expected to continue on the chronic toxicity test of 70% extraction of Citrus nobilis Lour peel for 12 months to predict the safety of clinical use in humans in the long term.
 Key word: heart, Citrus nobilis Lour.of peel, sub chronic toxicity

The Role of Bidara Leaf Extract (Ziziphus Mauritiana) on the Prevention of Renal Hypertension in Wistar Strain Rats

Hypertension is ranked the fourth highest disease in the word. Glomerulosclerosis is the initial lesion from kidney failure in patient with hypertension. Bidara leaves have active flavonoid such as saponins and tannins are thought to prevent an increase in blood pressure and repair glomerulosclerosis. This study to prove the effect of bidara leaf extract to decrease hypertension and to prevent glomerulosclerosis. This study was true experimental with Pre and Post Test Control Group Design. This study used male white rats wistar strain. Sample divided into 5 groups (positive group, treatment group with 3 leveled dose, and medicine-control group) and were being observed in 28 days. Hypertension was measured using tail cuff blood pressure. Glomerulus observed randomly with light microscope in 5 different field of view with 400x magnification and scoring by Sclerosis Index (SI). In the linear regression test had R 2 = 0,332 for systolic blood pressure and R 2 = 0,609 for diastolic blood pressure. For glomerulosclerosis we found that the highest SI (369,69) was positive control group, while the lowest SI (84,09) was the group with a dose 400mg/kg/day. Bidara leaves extract have a significant effect on reduce hypertension and glomerulosclerosis.

SDeepFM: Multi-Scale Stacking Feature Interactions for Click-Through Rate Prediction

For estimating the click-through rate of advertisements, there are some problems in that the features cannot be automatically constructed, or the features built are relatively simple, or the high-order combination features are difficult to learn under sparse data. To solve these problems, we propose a novel structure multi-scale stacking pooling (MSSP) to construct multi-scale features based on different receptive fields. The structure stacks multi-scale features bi-directionally from the angles of depth and width by constructing multiple observers with different angles and different fields of view, ensuring the diversity of extracted features. Furthermore, by learning the parameters through factorization, the structure can ensure high-order features being effectively learned in sparse data. We further combine the MSSP with the classical deep neural network (DNN) to form a unified model named sDeepFM. Experimental results on two real-world datasets show that the sDeepFM outperforms state-of-the-art models with respect to area under the curve (AUC) and log loss.

SPOT REGION OF INTEREST IMAGING: A NOVEL FUNCTIONALITY AIMED AT X-RAY DOSE REDUCTION IN NEUROINTERVENTIONAL PROCEDURES

Aim of the study: The aim of this study was to describe a new functionality aimed at X-ray dose reduction, referred to as spot region of interest (Spot ROI) and to compare it with existing dose-saving functionalities, spot fluoroscopy (Spot F), and conventional collimation (CC). Material and methods: Dose area product, air kerma, and peak skin dose were measured for Spot ROI, Spot F, and CC in three different fields of view (FOVs) 20 × 20 cm, 15 × 15 cm, and 11 × 11 cm using an anthropomorphic head phantom RS-230T. The exposure sequence was 5 min of pulsed fluoroscopy (7.5 pulses per s) followed by 7× digital subtraction angiography (DSA) runs with 30 frames per DSA acquisition (3 fps × 10 s). The collimation in Spot F and CC was adjusted such that the size of the anatomical area exposed was as large as the Spot ROI area in each FOV. Results: The results for all FOVs were the following: for the fluoroscopy, all measured parameters for Spot ROI and Spot F were lower than corresponding values for CC. For DSA and DSA plus fluoroscopy, all measured parameters for Spot ROI were lower than corresponding parameters for Spot F and CC. Conclusion: Spot ROI is a promising dose-saving technology that can be applied in fluoroscopy and acquisition. The biggest benefit of Spot ROI is its ability to keep the entire FOV information always visible.

Bitmap generation from computer-aided design for potential layer-quality evaluation in electron beam additive manufacturing

PurposeElectron beam additive manufacturing (EBAM) is a popular additive manufacturing (AM) technique used by many industrial sectors. In EBAM process monitoring, data analysis is focused on information extraction directly from the raw data collected in-process, i.e. thermal/optical/electronic images, and the comparison between the collected data and the computed tomography/microscopy images generated after the EBAM process. This paper aims to postulate that a stack of bitmaps could be generated from the computer-aided design (CAD) at a range of Z heights and user-defined region of interest during file preparation of the EBAM process, and serve as a reference image set.Design/methodology/approachComparison between that and the workpiece images collected during the EBAM process could then be used for quality assessment purposes. In spite of the extensive literature on CAD slicing and contour generation for AM process preparation, the method of bitmap generation from the CAD model at different field of views (FOVs) has not been disseminated in detail. This article presents a piece of custom CAD-bitmap generation software and an experiment demonstrating the application of the software alongside an electronic imaging system prototype.FindingsResults show that the software is capable of generating binary bitmaps with user-defined Z heights, image dimensions and image FOVs from the CAD model; and can generate reference bitmaps to work with workpiece electronic images for potential pixel-to-pixel image comparison.Originality/valueIt is envisaged that this CAD-bitmap image generation ability opens up new opportunities in quality assessment for the in-process monitoring of the EBAM process.

Distributed Multi-Sensor Fusion of PHD Filters With Different Sensor Fields of View

The paper addresses the problem of distributed multi-target tracking (MTT) in a network of sensors having different fields of view (FoVs). Probability hypothesis density (PHD) filters are running locally in every sensor for MTT. The weighted arithmetic average (WAA) fusion rule is employed to fuse the multiple local PHD densities due to its computational efficiency. First, we provide a theoretical analysis showing that the standard WAA fusion among sensors with different FoVs is unsuitable from the perspective of the principle of minimum discrimination information (PMDI). In fact, the information inconsistency among sensors due to the different FoVs unavoidably leads to an underestimation of the fused PHD. Then, motivated by the analysis, we devise two novel approaches to address the different FoV issue. The first approach accounts for the case where the sensor FoVs are known and time-invariant. The second one deals with the more complicated case where the actual sensor FoVs can be cropped due to unknown line of sight obstructions or sensors can obtain the information outside their current FoVs because of platform movement or information feedback. The essence of the proposed approaches is to perform the WAA in a more robust way by employing a set of state-dependent fusion weights which are computed online. The Gaussian mixture implementations of the proposed methods are also presented. Various simulation experiments, including a challenging tracking scenario involving six sensors with different FoVs and also random line of sight obstructions, are designed to demonstrate the efficacy of the proposed fusion approaches.

Multitask Deep Neural Networks for Tele-Wide Stereo Matching

In this paper, we introduce the problem of estimating the real world depth of elements in a scene captured by two cameras with different field of views, where the first field of view (FOV) is a Wide FOV (WFOV) captured by a wide angle lens, and the second FOV is contained in the first FOV and is captured by a tele zoom lens. We refer to the problem of estimating the inverse depth for the union of FOVs, while leveraging the stereo information in the overlapping FOV, as Tele-Wide Stereo Matching (TW-SM). We propose different deep learning solutions to the TW-SM problem. Since the disparity is proportional to the inverse depth, we train stereo matching disparity estimation (SMDE) networks to estimate the disparity for the union WFOV. We further propose an end-to-end deep multitask tele-wide stereo matching neural network (MT-TW-SMNet), which simultaneously learns the SMDE task for the overlapped Tele FOV and the single image inverse depth estimation (SIDE) task for the WFOV. Moreover, we design multiple methods for the fusion of the SMDE and SIDE networks. We evaluate the performance of TW-SM on the popular KITTI and SceneFlow stereo datasets, and demonstrate its practicality by synthesizing the Bokeh effect on the WFOV from a tele-wide stereo image pair.

Driver Behavior Recognition via Interwoven Deep Convolutional Neural Nets With Multi-Stream Inputs

Understanding driver activity is vital for in-vehicle systems that aim to reduce the incidence of car accidents rooted in cognitive distraction. Automating real-time behavior recognition while ensuring actions classification with high accuracy is however challenging, given the multitude of circumstances surrounding drivers, the unique traits of individuals, and the computational constraints imposed by in-vehicle embedded platforms. Prior work fails to jointly meet these runtime/accuracy requirements and mostly rely on a single sensing modality, which in turn can be a single point of failure. In this paper, we harness the exceptional feature extraction abilities of deep learning and propose a dedicated Interwoven Deep Convolutional Neural Network (InterCNN) architecture to tackle the problem of accurate classification of driver behaviors in real-time. The proposed solution exploits information from multi-stream inputs, i.e., in-vehicle cameras with different fields of view and optical flows computed based on recorded images, and merges through multiple fusion layers abstract features that it extracts. This builds a tight ensembling system, which significantly improves the robustness of the model. In addition, we introduce a temporal voting scheme based on historical inference instances, to enhance the classification accuracy. Experiments conducted with a dataset that we collect in a mock-up car environment demonstrate that the proposed InterCNN with MobileNet convolutional blocks can classify 9 different behaviors with 73.97% accuracy, and 5 'aggregated' behaviors with 81.66% accuracy. We further show that our architecture is highly computationally efficient, as it performs inferences within 15ms, which satisfies the real-time constraints of intelligent cars. Nevertheless, our InterCNN is robust to lossy input, as the classification remains accurate when two input streams are occluded.

Design of the Ship-Borne Multi-Wavelength Polarization Ocean Lidar System and Measurement of Seawater Optical Properties

A ship-borne multi-wavelength polarization ocean lidar system LOOP (Lidar for Ocean Optics Profiler) is introduced in detail, aiming to obtain high-precision vertical profiles of seawater optical characteristics. Based on Monte-Carlo simulation, the receiving telescope is designed with a variable field of view, producing system attenuation coefficient (Klidar) approximating the optical parameters of seawater under a different field of view and water body conditions. At first, a sea trial was conducted in Jiaozhou Bay, and the measured diffuse attenuation coefficient (Kd) of seawater was 0.3m−1, being in good agreement compared with the results measured by field instrument TriOS. Then a field campaign was organized in the South China Sea. The measurement of the seawater diffuse attenuation (Kd) was 0.035m−1. These results support the prospects that lidar, as an effective tool supplement to traditional passive ocean color remote sensing, can provide the vertical distributions of optical properties in the upper ocean.

On-Board and Train-to-Wayside Free Space Optical Link: Design and Characterization

Nowadays, online connectivity has become an important factor in the current competition between different means of transportation. Being one of the most comfortable and satisfactory means of transportation, a train acts as a mobile office for a wide range of customers. This demands for a high speed connection in order to provide personalized digital services. A reliable and highly secure link is needed for real time information required for signaling, control, safety and security. In this paper a free space optical (FSO) communication link is designed, implemented, and tested at different data rates reaching 5 Mb/s. A small scaled link between train waysides and carriages is implemented in order to set the geometrical boundaries for a practical scaled up link. A 1 m train track is built in laboratory to set the track length and angle of transceiver boundaries for continuous transmission. The received signal voltage is measured and bit error rate (BER) is calculated along the track. The experimental results are compared to the ray trace model simulation results. The experiment is conducted with different field of view (FOV) light emitting diodes (LEDs) in order to check their impact on short and long distance links. Obtained results showed full train track coverage of 90 cm is achieved using the 13°-LED, where the 3°-LED was limited to 85 cm. A modification to the geometrical dimensions is suggested in order to suit the real train track. According to the designed circuits, a bandwidth can reach up to 337 Mb/s. At a speed of 500 kb/s, 9 m of coverage and nearly 38 m are obtained for the 13°-LED and the 3°-LED, respectively. On the other hand, at the maximum bit rate of 337 Mb/s, the coverage is reduced to 1 m and 5 m for the 13° and the 3°- LEDs, respectively

Comparison of Bi-Hemispherical and Hemispherical-Conical Configurations for In Situ Measurements of Solar-Induced Chlorophyll Fluorescence

During recent decades, solar-induced chlorophyll fluorescence (SIF) has shown to be a good proxy for gross primary production (GPP), promoting the development of ground-based SIF observation systems and supporting a greater understanding of the relationship between SIF and GPP. However, it is unclear whether such SIF-oriented observation systems built from different materials and of different configurations are able to acquire consistent SIF signals from the same target. In this study, we used four different observation systems to measure the same targets together in order to investigate whether SIF from different systems is comparable. Integration time (IT), reflectance, and SIF retrieved from different systems with hemispherical-conical (hemi-con) and bi-hemispherical (bi-hemi) configurations were also evaluated. A newly built prism system (SIFprism, using prism to collect both solar and target radiation) has the shortest IT and highest signal to noise ratio (SNR). Reflectance collected from the different systems showed small differences, and the diurnal patterns of both red and far-red SIF derived from different systems showed a marginal difference when measuring the homogeneous vegetation canopy (grassland). However, when the target is heterogeneous, e.g., the Epipremnum aureum canopy, the values and diurnal pattern of far-red SIF derived from systems with a bi-hemi configuration were obviously different with those derived from the system with hemi-con configuration. These results demonstrate that different SIF systems are able to acquire consistent SIF for landscapes with a homogeneous canopy. However, SIF retrieved from bi-hemi and hemi-con configurations may be distinctive when the target is a heterogeneous (or discontinuous) canopy due to the different fields of view and viewing geometries. Our findings suggest that the bi-hemi configuration has an advantage to measure heterogeneous canopies due to the large field of view for upwelling sensors being representative for the footprint of the eddy covariance flux measurements.

Hyperspectral Super-Resolution Technique Using Histogram Matching and Endmember Optimization

In most hyperspectral super-resolution (HSR) methods, which are techniques used to improve the resolution of hyperspectral images (HSIs), the HSI and the target RGB image are assumed to have identical fields of view. However, because implementing these identical fields of view is difficult in practical applications, in this paper, we propose a HSR method that is applicable when an HSI and a target RGB image have different spatial information. The proposed HSR method first creates a low-resolution RGB image from a given HSI. Next, a histogram matching is performed on a high-resolution RGB image and a low-resolution RGB image obtained from an HSI. Finally, the proposed method optimizes endmember abundance of the high-resolution HSI towards the histogram-matched high-resolution RGB image. The entire procedure is evaluated using an open HSI dataset, the Harvard dataset, by adding spatial mismatch to the dataset. The spatial mismatch is implemented by shear transformation and cutting off the upper and left sides of the target RGB image. The proposed method achieved a lower error rate across the entire dataset, confirming its capability for super-resolution using images that have different fields of view.

Multiple-field-point pupil wavefront optimization in computational lithography.

Some pupil wavefront optimization (PWO) approaches were studied to compensate the thick mask effects considering only a field point, and these PWO methods neglect the inherent wave aberration in a realistic lithography system. Particularly, the wave aberration of lithography projection optics is exposure field dependent, and the wave aberrations at different fields of view (FOVs) would seriously and unevenly impact the results and effects of PWO. The current PWO method for single FOV cannot match full FOV. In this paper, we propose a multiple-field-point PWO (MPWO) method to improve lithography imaging quality for full FOV. A multiple-field-point cost function is built including the uneven impact of multiple aberrations on lithography imaging at full FOV. The comprehensive simulations demonstrate that the proposed MPWO method can effectively improve consistency of lithography imaging and enlarge the overlapped process window for full FOV. The most important point is that the optimized wavefront attained by MPWO can be realized via pupil wavefront manipulatorFlexWave in lithography equipment, which is significant in holistic lithography for the next technology node.

Target-Specific Action Classification for Automated Assessment of Human Motor Behavior from Video.

Objective monitoring and assessment of human motor behavior can improve the diagnosis and management of several medical conditions. Over the past decade, significant advances have been made in the use of wearable technology for continuously monitoring human motor behavior in free-living conditions. However, wearable technology remains ill-suited for applications which require monitoring and interpretation of complex motor behaviors (e.g., involving interactions with the environment). Recent advances in computer vision and deep learning have opened up new possibilities for extracting information from video recordings. In this paper, we present a hierarchical vision-based behavior phenotyping method for classification of basic human actions in video recordings performed using a single RGB camera. Our method addresses challenges associated with tracking multiple human actors and classification of actions in videos recorded in changing environments with different fields of view. We implement a cascaded pose tracker that uses temporal relationships between detections for short-term tracking and appearance based tracklet fusion for long-term tracking. Furthermore, for action classification, we use pose evolution maps derived from the cascaded pose tracker as low-dimensional and interpretable representations of the movement sequences for training a convolutional neural network. The cascaded pose tracker achieves an average accuracy of 88% in tracking the target human actor in our video recordings, and overall system achieves average test accuracy of 84% for target-specific action classification in untrimmed video recordings.

IMPACT OF FIELD OF VIEW AND SCATTER INDUCED NOISE IN DETECTION OF MB2 CANALS

<h3>Background</h3> Cone beam computed tomography (CBCT) units provide different fields of view (FOVs) for different clinical applications. Small FOV scans are often used in endodontic treatment planning. However, endodontic pathology is also detected as an incidental finding in large FOV scans. Some machines have introduced a new feature known as <i>zoom reconstruction.</i> This essentially allows for a specific area of interest to be carved out of the original data set and made into an independent scan that will allow for evaluation of the area in better detail. <h3>Objective(s)</h3> The objective of this study was to evaluate the number of missed canals on small FOV scans and zoom reconstruction gleaned from large field of scans. <h3>Study Design</h3> A total of 30 extracted maxillary first molars were used in this study. The teeth were scanned with small FOV to locate MB2 canals. The results were used as the gold standard. The teeth were endodontically treated and restored. The MB2 canal was not instrumented when it was present or detected clinically. The prepared teeth were mounted on a dry human skull and secured with sticky wax. The skull was scanned with large and small FOV scans. The areas of interest were zoom reconstructed from large FOV scans. Interobserver variations were calculated using kappa statistics. Nonparametric tests were used to compare the reading of the observers with gold standard. <h3>Results</h3> Kappa statistics from 0.605 to 0.650 for small FOV, from 0.350 to 0.650 for large FOV, and from –0.305 to 0.590 for zoom reconstruction. In general, the smaller FOV scans had better agreement with gold standard. <h3>Discussion/Conclusions</h3> Small FOV scans were found to have better detection accuracy compared with small scans gleaned out of a larger FOV scan through zoom reconstruction in its ability to detect missing root canals and pathology.

Cooperative sensor fusion in centralized sensor networks using Cauchy–Schwarz divergence

This paper presents a new solution for statistical fusion of multi-sensor information acquired from different fields of view, in a centralized sensor network. The focus is on applications that involve tracking unknown number of objects with time-varying states. Our solution is a track-to-track fusion method in which the information contents of posteriors are combined. Existing information-theoretic solutions for track-to-track fusion in sensor networks are commonly devised based on minimizing the average information divergence from the local posteriors to the fused one. A common approach is to use Generalized Covariance Intersection rule for sensor fusion. This approach works best when all the sensors detect the same object(s), and performs poorly when fields-of-view are different. We suggest Cauchy–Schwarz divergence to be used for measuring information divergence. We demonstrate that employing Cauchy–Schwarz divergence leads to fusion rules that are generally more tolerant to imperfect consensus. We show that the proposed fusion rule for multiple Poisson posteriors is the weighted arithmetic mean of the Poisson densities. Furthermore, we derive the fusion rule for labeled multi Bernoulli filter by approximating the labeled multi Bernoulli density to its first order moment. Numerical experiments show the superior performance of our solution compared to Kullback–Leibler averaging method.

Aerosol optical depth comparison between GAW-PFR and AERONET-Cimel radiometers from long-term (2005–2015) 1 min synchronous measurements

Abstract. A comprehensive comparison of more than 70 000 synchronous 1 min aerosol optical depth (AOD) data from three Global Atmosphere Watch precision-filter radiometers (GAW-PFR), traceable to the World AOD reference, and 15 Aerosol Robotic Network Cimel radiometers (AERONET-Cimel), calibrated individually with the Langley plot technique, was performed for four common or “near” wavelengths, 380, 440, 500 and 870 nm, in the period 2005–2015. The goal of this study is to assess whether, despite the marked technical differences between both networks (AERONET, GAW-PFR) and the number of instruments used, their long-term AOD data are comparable and consistent. The percentage of data meeting the World Meteorological Organization (WMO) traceability requirements (95 % of the AOD differences of an instrument compared to the WMO standards lie within specific limits) is &gt;92 % at 380 nm, &gt;95 % at 440 nm and 500 nm, and 98 % at 870 nm, with the results being quite similar for both AERONET version 2 (V2) and version 3 (V3). For the data outside these limits, the contribution of calibration and differences in the calculation of the optical depth contribution due to Rayleigh scattering and O3 and NO2 absorption have a negligible impact. For AOD &gt;0.1, a small but non-negligible percentage (∼1.9 %) of the AOD data outside the WMO limits at 380 nm can be partly assigned to the impact of dust aerosol forward scattering on the AOD calculation due to the different field of view of the instruments. Due to this effect the GAW-PFR provides AOD values, which are ∼3 % lower at 380 nm and ∼2 % lower at 500 nm compared with AERONET-Cimel. The comparison of the Ångström exponent (AE) shows that under non-pristine conditions (AOD &gt;0.03 and AE &lt;1) the AE differences remain &lt;0.1. This long-term comparison shows an excellent traceability of AERONET-Cimel AOD with the World AOD reference at 440, 500 and 870 nm channels and a fairly good agreement at 380 nm, although AOD should be improved in the UV range.

Distributed multi-sensor multi-view fusion based on generalized covariance intersection

Distributed multi-target tracking (DMTT) is addressed for sensors having different fields of view (FoVs). The proposed approach is based on the idea of fusing the posterior Probability Hypotheses Densities (PHDs) generated by the sensors on the basis of the local measurements. An efficient and robust distributed fusion algorithm combining the Generalized Covariance Intersection (GCI) rule with a suitable Clustering Algorithm (CA) is proposed, named CA-GCI fusion algorithm. The CA is used to decompose each posterior PHD into well-separated components (clusters). For the commonly detected targets, an efficient parallelized GCI fusion strategy is proposed and analyzed in terms of L1 error. For the remaining targets, a suitable compensation strategy is adopted so as to counteract the GCI sensitivity to independent detections while reducing the occurrence of false targets. Detailed implementation steps using a Gaussian Mixture (GM) representation of the PHDs are provided. Numerical experiments clearly confirm the effectiveness of the proposed CA-GCI fusion algorithm.