Spatial Filtering Algorithm Research Articles

Quasi-likelihood techniques in a logistic regression equation for identifying Simulium damnosum s.l. larval habitats intra-cluster covariates in Togo

The standard methods for regression analyses of clustered riverine larval habitat data of Simulium damnosum s.l. a major black-fly vector of onchoceriasis, postulate models relating observational ecological-sampled parameter estimators to prolific habitats without accounting for residual intra-cluster error correlation effects. Generally, this correlation comes from two sources: (1) the design of the random effects and their assumed covariance from the multiple levels within the regression model and (2) the correlation structure of the residuals. Unfortunately, inconspicuous errors in residual intra-cluster correlation estimates can overstate precision in forecasted S. damnosum s.l. riverine larval habitat explanatory attributes regardless how they are treated (e.g. independent, autoregressive, Toeplitz, etc.). In this research, the geographical locations for multiple riverine-based S. damnosum s.l. larval ecosystem habitats sampled from two preestablished epidemiological sites in Togo were identified and recorded from July 2009 to June 2010. Initially, the data were aggregated into PROC GENMOD. An agglomerative hierarchical residual cluster-based analysis was then performed. The sampled clustered study site data was then analyzed for statistical correlations using monthly biting rates (MBR). Euclidean distance measurements and terrain-related geomorphological statistics were then generated in ArcGIS. A digital overlay was then performed also in ArcGIS using the georeferenced ground coordinates of high and low density clusters stratified by annual biting rates (ABR). The data was overlain onto multitemporal sub-meter pixel resolution satellite data (i.e. QuickBird 0.61 m wavbands). Orthogonal spatial filter eigenvectors were then generated in SAS/Geographic Information Systems (GIS). Univariate and nonlinear regression-based models (i.e. logistic, Poisson, and negative binomial) were also employed to determine probability distributions and to identify statistically significant parameter estimators from the sampled data. Thereafter, Durbin–Watson statistics were used to test the null hypothesis that the regression residuals were not autocorrelated against the alternative that the residuals followed an autoregressive process in AUTOREG. Bayesian uncertainty matrices were also constructed employing normal priors for each of the sampled estimators in PROC MCMC. The residuals revealed both spatially structured and unstructured error effects in the high and low ABR-stratified clusters. The analyses also revealed that the estimators, levels of turbidity, and presence of rocks were statistically significant for the high-ABR-stratified clusters, while the estimators distance between habitats and floating vegetation were important for the low-ABR-stratified cluster. Varying and constant coefficient regression models, ABR-stratified GIS-generated clusters, sub-meter resolution satellite imagery, a robust residual intra-cluster diagnostic test, MBR-based histograms, eigendecomposition spatial filter algorithms, and Bayesian matrices can enable accurate autoregressive estimation of latent uncertainity affects and other residual error probabilities (i.e. heteroskedasticity) for testing correlations between georeferenced S. damnosum s.l. riverine larval habitat estimators. The asymptotic distribution of the resulting residual adjusted intra-cluster predictor error autocovariate coefficients can thereafter be established while estimates of the asymptotic variance can lead to the construction of approximate confidence intervals for accurately targeting productive S. damnosum s.l. habitats based on spatiotemporal field-sampled count data.

Subpixel hyperspectral target detection using local spectral and spatial information

We present two methods to improve three hyperspectral stochastic algorithms for target detection; the algorithms are the constrained energy minimization, the generalized likelihood ratio test, and the adaptive coherence estimator. The original algorithms rely solely on spectral information and do not use spatial information; this usage is normally justified in subpixel target detection, since the target size is smaller than the size of a pixel. However, we found that since the background (and the false alarms) may be spatially correlated and the point spread function can distribute the energy of a point target between several neighboring pixels, the implementation of spatial filtering algorithms considerably improved target detection. Our first improvement used the local spatial mean and covariance matrices, which take into account the spatial local mean instead of the global mean. While this concept has been found in the literature, the effect of its implementation in both the estimated mean and the covariance matrix is examined quantitatively here. The second was based on the fact that the effect of a target of physical subpixel size will extend to a cluster of pixels. We tested our algorithms by using the data set and scoring methodology of the Rochester Institute of Technology Target Detection Blind Test project. The results showed that both spatial methods independently improved the basic spectral algorithms mentioned above, and when the two methods were used together, the results were even better.

Symmetrical aspects of the causality principle in statistical group-theoretical image-reconstruction methods

New image processing methods that are based on statistical estimates of the invariants of image automorphism groups are considered. The cause-and-effect relationships during the processing of images in group-theoretical filters are investigated. The Curie principle is used to analyze the formation of images in spatial filtering algorithms with a local group.

고기동 표적 추적 성능 개선을 위한 연구

In this paper, a new realtime algorithm called the RTPBTD-HPDAF (Recursive Temporal Profile Base Target Detection with Highest Probability Data Association Filter) is presented for tracking fast moving small targets with IIR (Imaging Infrared) sensor systems. Spatial filter algorithms are mainly used for target in IIR sensor system detection and tracking however they often generate high density clutter due to various shapes of cloud. The TPBTD (Temporal Profile Base Target Detection) algorithm based on the analysis of temporal behavior of individual pixels is known to have good performance for detection and tracking of fast moving target with suppressing clutter. However it is not suitable to detect stationary and abruptly maneuvering targets. Moreover its computational load may not be negligible. The PTPBTD-HPDAF algorithm proposed in this paper for real-time target detection and tracking is shown to be computationally cheap while it has benefit of tracking targets with abrupt maneuvers. The performance of the proposed RTPBTD-HPDAF algorithm is tested and compared with the spatial filter with HPDAF algorithm for run-time and track initiation at real IIR video.

PRESURGICAL LOCALIZATION OF PRIMARY MOTOR CORTEX IN PEDIATRIC PATIENTS WITH BRAIN LESIONS BY THE USE OF SPATIALLY FILTERED MAGNETOENCEPHALOGRAPHY

The objective of this study was to confirm the efficacy of spatially filtered magnetoencephalography for the preoperative localization of primary motor cortex in pediatric patients with focal lesions in the region of the sensorimotor cortex. We recorded movement-related magnetoencephalographic activity in 10 pediatric patients (age range, 7-18 years; mean age, 12.5 years) undergoing presurgical evaluation for focal brain lesion resection. Participants made transient movements of the right and left index finger in response to a visual cue. The premovement motor field component in the averaged brain response was localized with a newly developed beamformer spatial filter algorithm. Cortical mapping of motor cortex intraoperatively was conducted in 5 of the 10 patients. The motor field time-locked to electromyography onset was successfully localized to cortical areas corresponding to the hand region primary motor cortex in 95% of cases (9 of 10 from nonlesional hemisphere; 10 of 10 from lesional hemisphere). Intraoperative electrocortical stimulation activated the expected muscles at motor field coregistered cortical source locations in all cases tested (n = 5). Using these methods, we also found that displacement of the sensorimotor cortex by space-occupying tumors did not interfere with the localization of motor cortex. We conclude that noninvasive localization of the primary motor cortex can be reliably performed by using spatially filtered magnetoencephalography techniques, which provide a robust and accurate measurement of motor cortical function for the purpose of surgical guidance.

Small‐footprint, waveform‐resolving lidar estimation of submerged and sub‐canopy topography in coastal environments

The experimental advanced airborne research lidar (EAARL) is an airborne lidar instrument designed to map near‐shore submerged topography and adjacent land elevations simultaneously. This study evaluated data acquired by the EAARL system in February 2003 and March 2004 along the margins of Tampa Bay, Florida, USA, to map bare‐earth elevations under a variety of vegetation types and submerged topography in shallow, turbid water conditions. A spatial filtering algorithm, known as the iterative random consensus filter (IRCF), was used to extract ground elevations from a point cloud of processed last‐surface EAARL returns. Filtered data were compared with acoustic and field measurements acquired in shallow submerged (0–2.5 m water depth) and sub‐canopy environments. Root mean square elevation errors (RMSEs) ranged from 10–14 cm for submerged topography to 16–20 cm for sub‐canopy topography under a variety of vegetation communities. The effect of lidar sampling angles and global positioning system (GPS) satellite configuration on accuracy was investigated. Results show high RMSEs for data acquired during periods of poor satellite configuration and at large sampling angles along the edges of the lidar scan. The results presented in this study confirm the cross‐environment capability of a green‐wavelength, waveform‐resolving lidar system, making it an ideal tool for mapping coastal environments.

The circuit design of current-mode image sensor embedded smooth spatial filter with flash A/D converter

The paper presents a current-mode CMOS image sensor embedded smooth spatial filter algorithm with a flash analog-to-digital (A/D) converter. The sensor includes a 66×66 pixel array with an on-chip ...

Combining Spatial Filters for the Classification of Single-Trial EEG in a Finger Movement Task

Brain-computer interface (BCI) is to provide a communication channel that translates human intention reflected by a brain signal such as electroencephalogram (EEG) into a control signal for an output device. In recent years, the event-related desynchronization (ERD) and movement-related potentials (MRPs) are utilized as important features in motor related BCI system, and the common spatial patterns (CSP) algorithm has shown to be very useful for ERD-based classification. However, as MRPs are slow nonoscillatory EEG potential shifts, CSP is not an appropriate approach for MRPs-based classification. Here, another spatial filtering algorithm, discriminative spatial patterns (DSP), is newly introduced for better extraction of the difference in the amplitudes of MRPs, and it is integrated with CSP to extract the features from the EEG signals recorded during voluntary left versus right finger movement tasks. A support vector machines (SVM) based framework is designed as the classifier for the features. The results show that, for MRPs and ERD features, the combined spatial filters can realize the single-trial EEG classification better than anyone of DSP and CSP alone does. Thus, we propose an EEG-based BCI system with the two feature sets, one based on CSP (ERD) and the other based on DSP (MRPs), classified by SVM.

Geometrical Variations of Gain Patterns

This paper studies the effect of the altitude and the elliptical nature of the Earth on projected antenna pattern functions (APFs). These variations can play a significant role in spatial filter algorithms, such as the Backus-Gilbert method. In this paper, a solution for projecting an APF from an arbitrary sensor and platform onto an elliptical Earth is presented. A general time integration of this projection is presented, which is important for dynamically generating the effective antenna pattern functions (EAPFs) in cases where the variations are nonnegligible. It is shown that the elliptical nature of the Earth provides a variation of order 1% in the area of the EAPF. These results suggest that approximating the Earth as a sphere is valid. It is also shown that elliptical orbits should consider the implication of the altitude variation before precomputing EAPFs

Combining fMRI and MEG increases the reliability of presurgical language localization: A clinical study on the difference between and congruence of both modalities

To avoid neurological impairment during surgery near language-related eloquent brain areas, we performed presurgical functional brain mapping with functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) in 172 patients using language tasks. For MEG localizations, we used either a moving equivalent-current dipole fit or a current–density reconstruction using a minimum variance beamformer with a spatial filter algorithm. We localized the Wernicke and Broca language areas for every patient. We integrated the results into a frameless stereotaxy system. To visualize the results in the navigation microscope during surgery, we superimposed the fMRI and MEG findings on the brain surface. MEG and fMRI results differed in 4% of cases, and in 19%, one modality showed activation but not the other. In the vicinity of large gliomas, the BOLD (blood oxygenation level-dependent) effect was suppressed in 53% of our patients. Of the 124 patients who had surgery, only 7 patients (5.6%) experienced a transient language deterioration, which resolved in all cases. We used MEG and fMRI to show different aspects of brain activity and to establish validation between MEG and fMRI. We conclude that measurement by both MEG and fMRI increases the degree of reliability of language area localization and that the combination of fMRI and MEG is useful for presurgical localization of language-related eloquent cortex.

TH‐D‐VaIB‐01: Correction of Couch Artifacts in KV Cone‐Beam CT From An On‐Board Imaging System

Purpose: To evaluate artifacts caused by treatment couch attenuation on 3D image reconstruction for a new kV on‐board‐imager (OBI) and cone beam CT (CBCT) system and to develop an algorithm that filters couch effects from two‐dimensional radiographic projections prior to inputting to the 3D reconstruction algorithm. Material and methods: A standard quality assurance phantom was scanned in air and on couch top using both full and half fan cone‐beam scanning modes with and without bowtie filter combination. A spatial domain filter algorithm was developed to remove couch attenuation from each radiographic projection. This filter is based on a pixel‐by‐pixel subtraction technique of radiographic projections of cone‐beam scans of the couch from the corresponding radiographic projections of scans with phantom on top of the couch. The net couch‐filtered radiographic projections were used to reconstruct CT images. Results: CT numbers for scans of the phantom on couch top are less uniform than for scans of the phantom in air. The couch artifacts vary the linearity of the CT numbers by 5–15%, depending on the density of the material. Noise of the scans with phantom on couch top (3.5%) is higher than that with phantom in air (1.5%). The increased noise hinders the ability of the CBCT system to resolve low‐contrast regions when the couch is present. Pre‐reconstruction processing of the couch suppresses noise (< 1.5%) improves uniformity by a factor of 2 and removes ring and streak artifacts in the couch‐filtered reconstructed CBCT images. Conclusion: The treatment couch produces streaking artifacts, enhances noise, and causes drifting of CT numbers in the reconstructed OBI CBCT images. The developed couch pre‐processing algorithm suppresses noise, improves CT number uniformity by a factor of 2 and removes ring and streak artifacts in the couch‐filtered reconstructed CBCT images.Conflict of Interest: Supported by NCI Grant P01‐CA59017.

Source Localization Using Subspace Estimation and Spatial Filtering

Subspace-tracking algorithms have traditionally been unable to deal with a large number of sources and at the same timepreserve their computationally efficiency, since, typically, efficiency goes down as the cube of the signal subspace dimension. One solution to this problem, which is presented in this paper, is to use a newly developed algorithm for the design of spatial filters in matrix form, in order to spatially filter the incoming data snapshots. The result is that the signal subspaces are confined to small angular sectors and, thus, the effective number of signals present is reduced. A method is developed for designing spatial filters in an efficient manner by formulating the design procedure as a rank-deficient linear least-squares problem. The source-bearing estimation is done using the signal-covariance matrix, which is updated using a recently developed fast algorithm, which is necessary in situations where one or more sources are nonstationary. The combination of the subspace-based bearing-estimation and spatial filter algorithms is shown to give good performance in cases of medium signal-to-noise ratio and is capable of resolving sources that are below the resolution limit of both conventional and adaptive beamforming. In addition, the use of spatial filtering makes it possible to estimate bearings for more than N narrow-band sources, using an N-element array. An example illustrating this capability is given.

Spatial filtering techniques applied to source localization

Matched-field processing outputs may be degraded by the presence of multiple sources. To isolate single sources, a spatial filtering algorithm has been developed. Element level data are transformed to beam space, filtered in bearing, then transformed back to element level data which are then used in simulated annealing inversions. The efficacy of the approach was demonstrated by comparing filtered and unfiltered ambiguity surfaces of depth, range, and bearing using a cross-correlation cost function in a simulated multisource environment with equally spaced array elements [Stotts et al., J. Acoust. Soc. Am. 113, 2217 (2003)]. Recently, this method was successfully applied to a real data set where localizations had previously been obtained using beam processing, and ground truth bottom properties have been established. Ambiguity surfaces are exhibited to demonstrate filter performance for real data. Examining the spectrum of singular values associated with the filter shows differences between equally and non-uniformly spaced elements, where a modification is required for frequencies greater than ideal. Energy conservation requirements validate filter output. Filter design details and results for both simulated and real data will be presented. Effectiveness as a function of signal-to-noise ratio, measured relative to an interferer, is also examined.

Creating Spatially Contiguous Yield Classes for Site‐Specific Management

Annual yield maps are spatially fragmented because of random variation caused by crop management as well as measurement errors. Two approaches for creating maps of spatially contiguous yield classes were evaluated at two irrigated sites. In the first approach, prior‐classification interpolation (PCI), grid size was increased from 4, 8, 16, and 32 to 64 m by kriging interpolation before cluster analysis used for mapping yield classes. Choosing a coarse resolution (>16 m) for yield interpolation before spatial classification resulted in maps that did not accurately depict yield patterns, significant decline of the yield variance accounted for, and loss of resolution in areas of sharp yield transitions caused by irrigation or near the field borders. In the second approach, postclassification filtering (PCF), cluster analysis of mean relative yield was conducted on the smallest grid size (4 m), and the classification results were postprocessed using a spatial filtering algorithm with window sizes that were equivalent to the 8‐, 16‐, 32‐, and 64‐m grid sizes used in PCI. This procedure removed erroneous map fragmentation and created maps of contiguous yield classes while preserving the class means and general yield patterns at high spatial resolution. Window sizes for spatial filtering of yield maps should be in the 30‐ to 60‐m range. Landscape pattern metrics may offer new potential for assessing mapping techniques as well as comparing agricultural production fields with regard to ranking their relative opportunities for site‐specific crop management.

Mathematical Aspects in Meteorological Processing of Infrared Spectral Measurements from the GOES Sounder. Part II: Analysis of Spatial and Temporal Continuity of Spectral Measurements from theGOES-8Sounder

Abstract The spatial and temporal continuity of the infrared measurements from the Geostationary Operational Environmental Satellite (GOES)-8 sounder data are investigated, and an experimental processing approach is presented. Spatial filtering and cloud detection are performed in a joint algorithm: the preparation of the data for sounding analysis starts with spatial smoothing, followed by cloud detection, followed by averaging the clear-sky (cloud free) subsamples. Analysis of the sounder images reveals the presence of coherent noise on large spatial scales in some of the spectral bands. Analysis of a temporal sequence of spatially smoothed sounder images reveals regions of unphysical hourly change likely induced by instrument noise. A nonlinear temporal–spatial filtering algorithm is presented and tested that improves the noise filtering for the sounder spectral measurements and the thermodynamical spatial and temporal consistency of the sounding retrievals in the troposphere.

Localization of human somatosensory cortex using spatially filtered magnetoencephalography

A spatial filter algorithm based on minimum-variance beamforming (synthetic aperture magnetometry (SAM)) was applied to single trial neuromagnetic recordings in order to localize primary somatosensory cortex. Magnetoencephalography (MEG) responses to electrical stimulation of the right and left median nerve were recorded using a whole-head MEG system and localized using both SAM spatial filtering and dipole analysis. Spatial filtering was applied to single trial neuromagnetic recordings to produce 3-dimensional difference images of source power between active (0–50 ms) and control states (−50–0 ms) in the range of 15–300 Hz. Average difference between N20m dipole location and location of maximal increase in power in the SAM images was 3.7 mm (1.5 mm SD) and localized to primary somatosensory cortex. Time-frequency analysis of spatially filtered output for the peak SAM locations showed a brief (10 ms) increase in the 60–100 Hz band coincident with the N20m response and a longer duration (approx. 80 ms) increase in power in the 10–40 Hz band following N20m onset. These results indicate that beamformer based spatial filter methods such as SAM can be used to localize temporally discrete cortical activity produced by median nerve stimulation.

High-resolution magnetoencephalography--studies with a small-volume phantom

To investigate the spatiotemporal organisation of neuronal processes in an animal model using magnetoencephalography (MEG), a high temporal resolution (ms) and an appropriate spatial resolution of about 1 mm is necessary. With the aim of determining the localization error and the resolution power of high-resolution MEG systems, we developed a phantom capable of simulating the characteristics of animal models. The phantom enables us to variably position at least two magnetic field sources to within 0.1 mm. For source localization on the basis of the magnetic field data, a spatial filtering algorithm was used. The investigation of a 16-channel micro SQUID-MEG system with a current dipole orientated tangentially to the phantom surface produced the following localization data (min ... max, x, y--horizontal plane, z--depth); systematic localization error e(x) = 1.16 ... 1.67 mm, e(y) = -1.01 ... -1.28 mm, e(z) = -5.22 ... -7.64 mm, standard deviation of the individual measurements perpendicular to the dipole axis s(perp) = 0.05 ... 0.22 mm, along this axis s(long) = 0.20 ... 1.73 mm, in the depths sz = 0.17 ... 3.17 mm. The "goodness of fit" was > 95%. Separation of two dipoles was still possible for parallel dipoles at a distance apart of d(parallel) = 0.03 mm and for those oriented perpendicularly to each other at a distance apart of d(perp) = 0.10 mm. On the basis of these results we conclude that the MEG system can achieve a resolution sufficient to permit the investigation of neuronal microstructures. The spatial errors detected were related to sensor position in the cryostatic vessel as well as to external low-frequency noise.

Adaptive Spatial Filtering Algorithm for Mobile Communications Networks

Adaptive Spatial Filtering Algorithm for Mobile Communications Networks

Assessing habitat suitability at multiple scales: A landscape-level approach

The distribution and abundance of many plants and animals are influenced by the spatial arrangement of suitable habitats across landscapes. We derived habitat maps from a digital land cover map of the ∼ 178,000 km 2 Chesapeake Bay Watershed by using a spatial filtering algorithm. The regional amounts and patterns of habitats were different for species which occur in ‘woody’, ‘herbaceous’, and ‘woody-edge’ habitats. Habitat for finer-scale species (∼ 5 ha home ranges) was twice as abundant and more evenly distributed than habitat for coarser-scale species (∼ 410 ha home ranges) in a 11,000 km 2 sub-region. Potential impacts of land cover changes on habitats in different parts of the region were assessed by the frequency distributions of habitat suitability for smaller (∼ 3000 km 2) embedded watersheds. The methods described in this paper can be applied to several scales of digital land cover data, and used to derive multiple-scale habitat suitabilities for a number of species or guilds.

Performance evaluation of a feature‐preserving filtering algorithm for removing additive random noise in digital images

We evaluate the performance of a feature-preserving filtering algorithm over a range of images corrupted by typical additive random noise against three common spatial filter algorithms: median, sigma and averaging. The concept of the new algorithm is based on a corrupted- pixel identification methodology over a variable subimage size. Rather than processing every pixel indiscriminately in a digital image, this corrupted-pixel identification algorithm interrogates the image in variable-sized subimage regions to determine which are the corrupted pixels and which are not. As a result, only the corrupted pixels are being filtered, whereas the uncorrupted pixels are untouched. Extensive evalu- ation of the algorithm over a large number of noisy images shows that the corrupted-pixel identification algorithm exhibits three major charac- teristics. First, its ability in removing additive random noise is better vi- sually (subjective) and has the smallest mean-square errors (objective) in all cases compared with the median filter, averaging filter and sigma filter. Second, the effect of smoothing introduced by the new filter is minimal. In other words, most edge and line sharpness is preserved. Third, the corrupted-pixel identification algorithm is consistently faster than the median and sigma filters in all our test cases. © 1996 Society of Photo-Optical Instrumentation Engineers. Subject terms: noise removal; feature preserving filtering; corrupted-pixel identi- fication; impulse noise; Gaussian white noise; mean-square error.