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Статті в журналах з теми "Filtre spatial"

Автор: Grafiati

Опубліковано: 21 грудня 2024

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1

GOBERT, J., J. F. DEROUBAIX, and M. SEIDL. "Un filtre planté dans le bois de Boulogne : quelle intégration socio-spatiale d’un objet-frontière ?" Techniques Sciences Méthodes 12 (January 20, 2023): 55–72. http://dx.doi.org/10.36904/202212055.

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Le déversoir d’orage Bugeaud situé dans le bois de Boulogne a fait l’objet d’une rénovation importante. Alors qu’il déversait auparavant directement en Seine les eaux pluviales d’un tronçon du boulevard périphérique, la Ville de Paris l’a récemment aménagé en l’équipant d’un système de stockage et de traitement de ces eaux par un filtre planté de roseaux avant leur rejet dans le réseau hydrographique du bois de Boulogne et à terme en Seine. Le projet Life Adsorb accompagne la réalisation et évalue l’efficacité de l’ouvrage, notamment sur l’abattement des micropolluants. Il intègre un volet sociologique sur les conditions d’appropriation de l’ouvrage par les services techniques de la collectivité ainsi que par les riverains alentours et les visiteurs du Bois. Afin d’analyser l’insertion socio-spatiale du filtre planté, nous avons eu recours à un cadre d’analyse fondé sur l’acceptabilité sociale, en différenciant l’enjeu et le problème d’acceptabilité. Nous avons donc regardé, d’une part, l’ouvrage comme un objet socio-technique qui, de sa conception à sa mise en service, passe par différentes étapes et réinterprétations (sociologie de la traduction et de l’innovation). D’autre part, nous avons observé comment cet ouvrage se confrontait à de nouveaux collectifs une fois construit. In fine, l’article illustre l’ambiguïté existante entre le souhait de faire de cet objet un « projet vitrine » d’une ville innovante et durable et la recherche d’invisibilisation par le paysagement. De fait, cet article montre comment ce type d’ouvrage assez commun agit comme un objet-frontière à l’intersection des savoir-faire et pratiques des différents services qui interviennent dans sa conception et sa maintenance, et des usagers. La connaissance acquise sur la conception et la maintenance de techniques alternatives souffre de ne pas être assez capitalisée au niveau des collectivités d’une part et entre collectivités d’autre part. Leur pleine insertion socio-spatiale reste dépendante d’une reconfiguration des organisations qui les reçoivent.

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2

Xin He, Xin He, Jian Zhou Jian Zhou, Xiaoming Nie Xiaoming Nie, and Xingwu Long Xingwu Long. "Filtering characteristics of spatial filter for spatial filtering velocimeter." Chinese Optics Letters 13, no. 6 (2015): 060702–60705. http://dx.doi.org/10.3788/col201513.060702.

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3

Sivakumar, R., M. K. Gayathri, and D. Nedumaran. "Speckle Filtering of Ultrasound B-Scan Images- A Comparative Study of Single Scale Spatial Adaptive Filters, Multiscale Filter and Diffusion Filters." International Journal of Engineering and Technology 2, no. 6 (2010): 514–23. http://dx.doi.org/10.7763/ijet.2010.v2.174.

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4

Ying Zhang, Ying Zhang, Hongji Qi Hongji Qi, Kui Yi Kui Yi, Yanzhi Wang Yanzhi Wang, Hongbo He Hongbo He, and Jianda Shao Jianda Shao. "Design and fabrication of thin-film spatial filter." Chinese Optics Letters 12, s2 (2014): S20501–320504. http://dx.doi.org/10.3788/col201412.s20501.

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5

Dev, Ashwani, and George A. McMechan. "Spatial antialias filtering in the slowness-frequency domain." GEOPHYSICS 74, no. 2 (March 2009): V35—V42. http://dx.doi.org/10.1190/1.3052115.

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A rigorous, explicit spatial antialias filter is designed and applied to spatially coarsely sampled seismic data by removing all energy above the first Nyquist wavenumber, and aliased energy that is folded back across the Nyquist, in the horizontal slowness-frequency domain. The spatial filtering in the slowness-frequency domain is explicit, free from any event linearity assumption, and does not require any interpolation. The spatially aliased energy is dispersive, and present at small and large slownesses. Comparison of the output data after antialias spatial filtering, with output data after conventional antialias frequency filtering, shows that the filter removes the spatially aliased frequencies selectively at each slowness; antialias low-pass frequency filtering under- or overcorrects for spatial aliasing at all slownesses. A seismic gather can be spatially dealiased only at the expense of wavelet spectral changes; dealiasing and preservation of amplitude variations with offset are not simultaneously possible.

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6

Cantet, Philippe, M. A. Boucher, S. Lachance-Coutier, R. Turcotte, and V. Fortin. "Using a Particle Filter to Estimate the Spatial Distribution of the Snowpack Water Equivalent." Journal of Hydrometeorology 20, no. 4 (March 29, 2019): 577–94. http://dx.doi.org/10.1175/jhm-d-18-0140.1.

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Abstract A snow model forced by temperature and precipitation is used to simulate the spatial distribution of snow water equivalent (SWE) over a 600 000 km2 portion of the province of Quebec, Canada. We propose to improve model simulations by assimilating SWE data from sporadic manual snow surveys with a particle filter. A temporally and spatially correlated perturbation of the meteorological forcing is used to generate the set of particles. The magnitude of the perturbations is fixed objectively. First, the particle filter and direct insertion were both applied on 88 sites for which measured SWE consisted of more or less five values per year over a period of 17 years. The temporal correlation of perturbations enables us to improve the accuracy and the ensemble dispersion of the particle filter, while the spatial correlation leads to a spatial coherence in the particle weights. The spatial estimates of SWE obtained with the particle filter are compared with those obtained through optimal interpolation of the snow survey data, which is the current operational practice in Quebec. Cross-validation results as well as validation against an independent dataset show that the proposed particle filter enables us to improve the spatial distribution of the snow water equivalent compared with optimal interpolation.

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7

Kim, J. S. "Mitigation of Inter-Symbol Interference in Underwater Acoustic Communication Using Spatial Filter." Journal Of The Acoustical Society Of Korea 33, no. 1 (2014): 48. http://dx.doi.org/10.7776/ask.2014.33.1.048.

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8

Hariharan, P., and M. Roy. "Improved pinhole spatial filter." Optics & Laser Technology 39, no. 1 (February 2007): 155–56. http://dx.doi.org/10.1016/j.optlastec.2005.04.002.

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9

Donohoe, Aaron, and David S. Battisti. "The Amplitude Asymmetry between Synoptic Cyclones and Anticyclones: Implications for Filtering Methods in Feature Tracking." Monthly Weather Review 137, no. 11 (November 1, 2009): 3874–87. http://dx.doi.org/10.1175/2009mwr2837.1.

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Abstract The “background” state is commonly removed from synoptic fields by use of either a spatial or temporal filter prior to the application of feature tracking. Commonly used spatial and temporal filters applied to sea level pressure data admit substantially different information to be included in the synoptic fields. The spatial filter retains a time-mean field that has comparable magnitude to a typical synoptic perturbation. In contrast, the temporal filter removes the entire time-mean field. The inclusion of the time-mean spatially filtered field biases the feature tracking statistics toward large cyclone (anticyclone) magnitudes in the regions of climatological lows (highs). The resulting cyclone/anticyclone magnitude asymmetries in each region are found to be inconsistent with the unfiltered data fields and merely result from the spurious inclusion of the time-mean fields in the spatially filtered data. The temporally filtered fields do not suffer from the same problem and produce modest cyclone/anticyclone magnitude asymmetries that are consistent with the unfiltered data. This analysis suggests that the weather forecaster’s assertion that cyclones have larger amplitudes than anticyclones is due to a composite of a small magnitude asymmetry in the synoptic waves and a large contribution from inhomogeneity in the background (stationary) field.

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10

Zhou, Hau‐wei. "Traveltime tomography with a spatial‐coherency filter." GEOPHYSICS 58, no. 5 (May 1993): 720–26. http://dx.doi.org/10.1190/1.1443456.

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Spatially incoherent small wavelengths in a velocity tomogram are usually noise that could destabilize an iterative traveltime tomographic process. By defining the number of connected cells of similar values in an image as the spatial‐coherency (SC) value of these cells, an SC filtering is introduced in this paper to suppress incoherent features in an image. An SC filter first quantifies the SC value of all image cells and then conducts a discriminative moving‐average over cells of low SC values. It is demonstrated that such an SC filtering of the intermediate‐stage tomograms may enhance the robustness of the iterative tomographic process to noise. The SC values can be used in general to deconvolve an image of any dimension into components of different lengths in spatial connectivity.

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11

Bazine, Razika, Huayi Wu, and Kamel Boukhechba. "Spectral DWT Multilevel Decomposition with Spatial Filtering Enhancement Preprocessing-Based Approaches for Hyperspectral Imagery Classification." Remote Sensing 11, no. 24 (December 5, 2019): 2906. http://dx.doi.org/10.3390/rs11242906.

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In this paper, spectral–spatial preprocessing using discrete wavelet transform (DWT) multilevel decomposition and spatial filtering is proposed for improving the accuracy of hyperspectral imagery classification. Specifically, spectral DWT multilevel decomposition (SDWT) is performed on the hyperspectral image to separate the approximation coefficients from the detail coefficients. For each level of decomposition, only the detail coefficients are spatially filtered instead of being discarded, as is often adopted by the wavelet-based approaches. Thus, three different spatial filters are explored, including two-dimensional DWT (2D-DWT), adaptive Wiener filter (AWF), and two-dimensional discrete cosine transform (2D-DCT). After the enhancement of the spectral information by performing the spatial filter on the detail coefficients, DWT reconstruction is carried out on both the approximation and the filtered detail coefficients. The final preprocessed image is fed into a linear support vector machine (SVM) classifier. Evaluation results on three widely used real hyperspectral datasets show that the proposed framework using spectral DWT multilevel decomposition with 2D-DCT filter (SDWT-2DCT_SVM) exhibits a significant performance and outperforms many state-of-the-art methods in terms of classification accuracy, even under the constraint of small training sample size, and execution time.

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12

Amba, Prakhar, and David Alleysson. "Spectral image recovery from Spectral Filter Array cameras using LMMSE." Color and Imaging Conference 2019, no. 1 (October 21, 2019): 300–303. http://dx.doi.org/10.2352/issn.2169-2629.2019.27.54.

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A hyperspectral camera can record a cube of data with both spatial 2D and spectral 1D dimensions. Spectral Filter Arrays (SFAs) overlaid on a single sensor allows a snapshot version of a hyperspectral camera. But acquired image is subsampled both spatially and spectrally, and a recovery method should be applied. In this paper we present a linear model of spectral and spatial recovery based on Linear Minimum Mean Square Error (LMMSE) approach. The method learns a stable linear solution for which redundancy is controlled using spatial neighborhood. We evaluate results in simulation using gaussian shaped filter's sensitivities on SFA mosaics of upto 9 filters with sensitivities both in visible and Near-Infrared (NIR) wavelength. We show by experiment that by using big neighborhood sizes in our model we can accurately recover the spectra from the RAW images taken by such a camera. We also present results on recovered spectra of Macbeth color chart from a Bayer SFA having 3 filters.

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13

Burbeck, Christina A., and Yen Lee Yap. "Spatial-filter selection in large-scale spatial-interval discrimination." Vision Research 30, no. 2 (January 1990): 263–72. http://dx.doi.org/10.1016/0042-6989(90)90042-j.

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14

Fang Liu, Fang Liu, Jianqiang Zhu Jianqiang Zhu, Jia Xu Jia Xu, Quanyuan Shan Quanyuan Shan, Kun Xiao Kun Xiao, and Xuejie Zhang Xuejie Zhang. "Micro-vibrating spatial filters-induced beam positioning stability in large laser system." Chinese Optics Letters 10, no. 4 (2012): 041402–41404. http://dx.doi.org/10.3788/col201210.041402.

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15

Prakash, Shashi, and Chandra Shakher. "Spatial filtering using two thick phase transmission hololenses." Journal of Optics 28, no. 2 (April 1997): 74–77. http://dx.doi.org/10.1088/0150-536x/28/2/004.

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16

Hujie Zhang, Hujie Zhang, Shenlei Zhou Shenlei Zhou, Youen Jiang Youen Jiang, Jinghui Li Jinghui Li, Wei Feng Wei Feng, and Zunqi Lin Zunqi Lin. "Analysis on FM-to-AM conversion induced by spatial filter in a high-power laser system." Chinese Optics Letters 10, no. 6 (2012): 060501–60505. http://dx.doi.org/10.3788/col201210.060501.

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17

Kim, Seung-Wan, and Ou-Bong Gwun. "Sketch Style Effect using Spatial Filter." Journal of the Korea Contents Association 9, no. 12 (December 28, 2009): 86–93. http://dx.doi.org/10.5392/jkca.2009.9.12.086.

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18

KOHSAKA, Fusao, Toshio IINO, Kunio KAZAMI, Hiroshi NAKAYAMA, and Toshitsugu UEDA. "Multiturn absolute encoder using spatial filter." JSME international journal. Ser. 3, Vibration, control engineering, engineering for industry 33, no. 1 (1990): 94–99. http://dx.doi.org/10.1299/jsmec1988.33.94.

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19

CHANG, CHIN-CHEN, and JI-HAN JIANG. "A SPATIAL FILTER FOR SIMILARITY RETRIEVAL." International Journal of Pattern Recognition and Artificial Intelligence 10, no. 06 (September 1996): 711–30. http://dx.doi.org/10.1142/s0218001496000426.

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Recently, Yang and Lee13 proposed a powerful data structure, the 2D B-string, to express the spatial knowledge embedded in iconic images. The data structure can be used to describe images with partly- or completely- overlapping objects without the need for partitioning the objects. We propose a signature file method for similarity retrieval of symbolic images described in 2D B-strings. The signatures of images are extracted by the disjoint coding and the superimposed coding techniques. A linear hashing function is adopted to organize the signature file. The experimental results show that the method has better performance than the one proposed by Lee, Yang and Chen.11

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20

Guracar, Ismavil M. "Energy weighted parameter spatial∕temporal filter." Journal of the Acoustical Society of America 102, no. 4 (October 1997): 1929. http://dx.doi.org/10.1121/1.419686.

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21

Möbus, G., B. Schmidt, and H. J. Tiziani. "Self-induced photorefractive spatial frequency filter." Journal of the Optical Society of America B 7, no. 12 (December 1, 1990): 2374. http://dx.doi.org/10.1364/josab.7.002374.

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22

Jin, Yusheng, Hong Zhao, Feifei Gu, Penghui Bu, and Mulun Na. "A spatial minimum spanning tree filter." Measurement Science and Technology 32, no. 1 (January 1, 2020): 015204. http://dx.doi.org/10.1088/1361-6501/abaa65.

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23

ZHOU, XIAOFANG, XUEMIN LIN, CHENGFEI LIU, and JINLI CAO. "SPATIAL QUERY PROCESSING USING GENERALIZED FILTER." International Journal of Cooperative Information Systems 09, no. 01n02 (March 2000): 77–91. http://dx.doi.org/10.1142/s0218843000000065.

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Анотація:

Spatial data, ranging from various land information data to different types of environmental data, are typically collected and used by different custodians. The full benefits of using spatial data can be achieved by combining the data from different sources covering a common region. Due to organizational, political and technical reasons, it is unrealistic to physically integrate the vast amount of spatial data managed by different systems in different organizations. A practical approach is to provide interoperability to support multi-site data queries. In this paper, we study the performance aspect of complex spatial query processing. We propose a framework for processing queries with multiple spatial and aspatial predicates using data from multiple sites. Using a new concept called generalized filter, a query is processed in three steps. First, an aspatial filter that incorporates some conditions derived from spatial predicates is used to find a set of candidates, which is a superset of the final query results. Then, the candidates are manipulated and a refinement step is executed following an optimized candidate sequence. Finally, a post-processing step is used to handle spatial expressions in query results. The focus of this paper is to generate enhanced filters in order to minimize the need of transferring and processing complex spatial data.

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24

Pan, Shengdong, Xiangjing An, and Hangen He. "OptimalO(1) Bilateral Filter with Arbitrary Spatial and Range Kernels Using Sparse Approximation." Mathematical Problems in Engineering 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/289517.

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A number of acceleration schemes for speeding up the time-consuming bilateral filter have been proposed in the literature. Among these techniques, the histogram-based bilateral filter trades the flexibility for achievingO(1) computational complexity using box spatial kernel. A recent study shows that this technique can be leveraged forO(1) bilateral filter with arbitrary spatial and range kernels by linearly combining the results of multiple-box bilateral filters. However, this method requires many box bilateral filters to obtain sufficient accuracy when approximating the bilateral filter with a large spatial kernel. In this paper, we propose approximating arbitrary spatial kernel using a fixed number of boxes. It turns out that the multiple-box spatial kernel can be applied in manyO(1) acceleration schemes in addition to the histogram-based one. Experiments on the application to the histogram-based acceleration are presented in this paper. Results show that the proposed method has better accuracy in approximating the bilateral filter with Gaussian spatial kernel, compared with the previous histogram-based methods. Furthermore, the performance of the proposed histogram-based bilateral filter is robust with respect to the parameters of the filter kernel.

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25

Wójcicki, Jeremi, Krzysztof Mendrok, and Tadeusz Uhl. "Spatial Filter for Operational Deflection Shape Component Filtration." Key Engineering Materials 569-570 (July 2013): 868–75. http://dx.doi.org/10.4028/www.scientific.net/kem.569-570.868.

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Modal filtration in the field of damage detection has many advantages, including: its autonomous operation (without the interaction of qualified staff), low computational cost and low sensitivity to changes in external conditions. However, the main drawback of this group of damage detection methods is its limited applicability to operational data. The modal filtration of the responses spectra (in place of FRFs), proposed in the literature, often does not give the expected results, working properly only for excitation in the form of white noise, or an ideal impulse. In other cases, for example in rotational machines, when in the response spectrum the rotational velocity harmonics dominate it can give wrong results. For such cases authors propose to use a new type of spatial filter, similar to modal filter with the difference that it has ability to filter the operational deflection shape components from the system response. Its application together with classical modal filter allows for damage detection using operational data with other type of excitation. The main assumption of the new spatial filter is the orthogonality of the filter coefficient vector to the operational deflection shape vector, it is then similar to the classical modal filter.

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26

Behar, V., and V. Bogdanova. "Pre-Processing of Hyperspectral Images Using Nonlinear Filters." Information Technologies and Control 11, no. 1 (March 1, 2013): 8–13. http://dx.doi.org/10.2478/itc-2013-0002.

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Abstract In this paper the use of a set of nonlinear edge-preserving filters is proposed as a pre-processing stage with the purpose to improve the quality of hyperspectral images before object detection. The capability of each nonlinear filter to improve images, corrupted by spatially and spectrally correlated Gaussian noise, is evaluated in terms of the average Improvement factor in the Peak Signal to Noise Ratio (IPSNR), estimated at the filter output. The simulation results demonstrate that this pre-processing procedure is efficient only in case the spatial and spectral correlation coefficients of noise do not exceed the value of 0.6

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27

Liu, Zhang-Meng, Zheng Liu, Dao-Wang Feng, and Zhi-Tao Huang. "Direction-of-Arrival Estimation for Coherent Sources via Sparse Bayesian Learning." International Journal of Antennas and Propagation 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/959386.

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A spatial filtering-based relevance vector machine (RVM) is proposed in this paper to separate coherent sources and estimate their directions-of-arrival (DOA), with the filter parameters and DOA estimates initialized and refined via sparse Bayesian learning. The RVM is used to exploit the spatial sparsity of the incident signals and gain improved adaptability to much demanding scenarios, such as low signal-to-noise ratio (SNR), limited snapshots, and spatially adjacent sources, and the spatial filters are introduced to enhance global convergence of the original RVM in the case of coherent sources. The proposed method adapts to arbitrary array geometry, and simulation results show that it surpasses the existing methods in DOA estimation performance.

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28

Fan, Keqi, Qianqian Yu, Yiyang Wang, Deng Chen, and Yuhui Zheng. "Visual Object Tracking Based on Adaptive Background-Awareness and Spatial Constraint." Mathematical Problems in Engineering 2022 (June 29, 2022): 1–11. http://dx.doi.org/10.1155/2022/6062283.

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The correlation filter method is effective in visual tracking tasks, whereas it suffers from the boundary effect and filter degradation in complex situations, which can result in suboptimal performance. Aiming at the solving above problem, this study proposes an object tracking method with a discriminant correlation filter, which combines an adaptive background perception and a spatial dynamic constraint. In this method, an adaptive background-awareness strategy is used to filter the background information trained by the interference filter to improve the discriminability between the object and the background. In addition, the spatial regularization term is introduced, and the dynamic change of the real filter and the predefined spatial constraint template is used to optimize filter learning to enhance the spatial information capture ability of the filter model. Experiments on the OTB100, VOT2018, and TrackingNet standard datasets demonstrate that our method achieves favorable accuracy and success rates. Compared with the current popular correlation filter methods, the proposed method can still maintain stable tracking performance with a scene scale variation, complex background, motion blur, and fast motion.

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29

Romanuke, Vadim V. "Smooth Non-increasing Square Spatial Extents of Filters in Convolutional Layers of CNNs for Image Classification Problems." Applied Computer Systems 23, no. 1 (May 1, 2018): 52–62. http://dx.doi.org/10.2478/acss-2018-0007.

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Анотація:

Abstract The present paper considers an open problem of setting hyperparameters for convolutional neural networks aimed at image classification. Since selecting filter spatial extents for convolutional layers is a topical problem, it is approximately solved by accumulating statistics of the neural network performance. The network architecture is taken on the basis of the MNIST database experience. The eight-layered architecture having four convolutional layers is nearly best suitable for classifying small and medium size images. Image databases are formed of grayscale images whose size range is 28 × 28 to 64 × 64 by step 2. Except for the filter spatial extents, the rest of those eight layer hyperparameters are unalterable, and they are chosen scrupulously based on rules of thumb. A sequence of possible filter spatial extents is generated for each size. Then sets of four filter spatial extents producing the best performance are extracted. The rule of this extraction that allows selecting the best filter spatial extents is formalized with two conditions. Mainly, difference between maximal and minimal extents must be as minimal as possible. No unit filter spatial extent is recommended. The secondary condition is that the filter spatial extents should constitute a non-increasing set. Validation on MNIST and CIFAR- 10 databases justifies such a solution, which can be extended for building convolutional neural network classifiers of colour and larger images.

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30

Wang, Yulei, Qingyu Zhu, Yao Shi, Meiping Song, and Chunyan Yu. "A Spatial-Enhanced LSE-SFIM Algorithm for Hyperspectral and Multispectral Images Fusion." Remote Sensing 13, no. 24 (December 7, 2021): 4967. http://dx.doi.org/10.3390/rs13244967.

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Анотація:

The fusion of a hyperspectral image (HSI) and multispectral image (MSI) can significantly improve the ability of ground target recognition and identification. The quality of spatial information and the fidelity of spectral information are normally contradictory. However, these two properties are non-negligible indicators for multi-source remote-sensing images fusion. The smoothing filter-based intensity modulation (SFIM) method is a simple yet effective model for image fusion, which can improve the spatial texture details of the image well, and maintain the spectral characteristics of the image significantly. However, traditional SFIM has a poor effect for edge information sharpening, leading to a bad overall fusion result. In order to obtain better spatial information, a spatial filter-based improved LSE-SFIM algorithm is proposed in this paper. Firstly, the least square estimation (LSE) algorithm is combined with SFIM, which can effectively improve the spatial information quality of the fused image. At the same time, in order to better maintain the spatial information, four spatial filters (mean, median, nearest and bilinear) are used for the simulated MSI image to extract fine spatial information. Six quality indexes are used to compare the performance of different algorithms, and the experimental results demonstrate that the LSE-SFIM based on bilinear (LES-SFIM-B) performs significantly better than the traditional SFIM algorithm and other spatially enhanced LSE-SFIM algorithms proposed in this paper. Furthermore, LSE-SFIM-B could also obtain similar performance compared with three state-of-the-art HSI-MSI fusion algorithms (CNMF, HySure, and FUSE), while the computing time is much shorter.

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31

Hallum, Luke E., Michael S. Landy, and David J. Heeger. "Human primary visual cortex (V1) is selective for second-order spatial frequency." Journal of Neurophysiology 105, no. 5 (May 2011): 2121–31. http://dx.doi.org/10.1152/jn.01007.2010.

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A variety of cues can differentiate objects from their surrounds. These include “first-order” cues such as luminance modulations and “second-order” cues involving modulations of orientation and contrast. Human sensitivity to first-order modulations is well described by a computational model involving spatially localized filters that are selective for orientation and spatial frequency (SF). It is widely held that first-order modulations are represented by the firing rates of simple and complex cells (“first-order” neurons) in primary visual cortex (V1) that, likewise, have spatially localized receptive fields that are selective for orientation- and SF. Human sensitivity to second-order modulations is well described by a filter-rectify-filter (FRF) model, with first- and second-order filters selective for orientation and SF. However, little is known about how neuronal activity in visual cortex represents second-order modulations. We tested the FRF model by using an functional (f)MRI-adaptation protocol to characterize the selectivity of activity in visual cortex to second-order, orientation-defined gratings of two different SFs. fMRI responses throughout early visual cortex exhibited selective adaptation to these stimuli. The low-SF grating was a more effective adapter than the high-SF grating, incompatible with the FRF model. To explain the results, we extended the FRF model by incorporating normalization, yielding a filter-rectify-normalize-filter model, in which normalization enhances selectivity for second-order SF but only for low spatial frequencies. We conclude that neurons in human visual cortex are selective for second-order SF, that normalization (surround suppression) contributes to this selectivity, and that the selectivity in higher visual areas is simply fed forward from V1.

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32

Liao, Jianshang, and Liguo Wang. "Hyperspectral Image Classification Based on Fusion of Curvature Filter and Domain Transform Recursive Filter." Remote Sensing 11, no. 7 (April 7, 2019): 833. http://dx.doi.org/10.3390/rs11070833.

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Анотація:

In recent decades, in order to enhance the performance of hyperspectral image classification, the spatial information of hyperspectral image obtained by various methods has become a research hotspot. For this work, it proposes a new classification method based on the fusion of two spatial information, which will be classified by a large margin distribution machine (LDM). First, the spatial texture information is extracted from the top of the principal component analysis for hyperspectral images by a curvature filter (CF). Second, the spatial correlation information of a hyperspectral image is completed by using domain transform recursive filter (DTRF). Last, the spatial texture information and correlation information are fused to be classified with LDM. The experimental results of hyperspectral images classification demonstrate that the proposed curvature filter and domain transform recursive filter with LDM(CFDTRF-LDM) method is superior to other classification methods.

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33

Liu, Bin, Tingzhang Liu, Jianfei Zhao, and Dan Hang. "Frequency Aliasing-Based Spatial-Wavenumber Filter for Online Damage Monitoring." Shock and Vibration 2020 (August 3, 2020): 1–10. http://dx.doi.org/10.1155/2020/8856241.

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The spatial-wavenumber filter method can extract the specific mode of the Lamb wave, thereby distinguishing the incident wave and the damage reflection wave. This method has been widely studied for damage imaging. However, the diameter of piezoelectric transducer (PZT) sensor limits the spatial sampling wavenumber of the linear PZT sensor array, which limits the application of this method because of the Nyquist–Shannon sampling theorem. Therefore, the wavenumber filtering range of spatial-wavenumber filter should be less than half of the spatial sampling wavenumber. In this paper, a frequency aliasing based spatial-wavenumber filter for online damage monitoring is proposed. In this method, the wavenumber filtering range is extended to the spatial sampling wavenumber, and two wavenumber results will be calculated as for the frequency aliasing. Subsequently, the wavenumber of the received Lamb wave signal can be obtained according to the average arrival time difference between the two adjacent sensors in the linear PZT sensor array. Finally, the damage is localized using the spatial-wavenumber filter and cruciform PZT sensor array. This method was validated on an epoxy laminate plate. The maximum damage localization errors are less than 2 cm. It is indicated that this method can extend the spatial-wavenumber filtering range to the spatial sampling wavenumber and the application of spatial-wavenumber filter-based online damage monitoring.

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34

Feng, Yun, Yaonan Wang, Jun‐Wei Wang, Han‐Xiong Li, and Steven X. Ding. "Spatio‐temporal fault localization for nonlinear spatially distributed processes: A spatial mapping filter‐based framework." International Journal of Robust and Nonlinear Control 31, no. 14 (July 6, 2021): 6953–71. http://dx.doi.org/10.1002/rnc.5650.

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35

Radoux, Julien, and Pierre Defourny. "Mathematical morphology pre-processing for enhanced segmentation of heterogeneous spatial regions." Revue Française de Photogrammétrie et de Télédétection, no. 208 (September 8, 2014): 33–38. http://dx.doi.org/10.52638/rfpt.2014.133.

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The very high spatial resolution of Pleiades images allows for the detection of small spatial objects such as buildings or isolated trees. However, the delineation of spatial regions, defined as associations between different spatial objects (such as open canopy forests or urban areas), becomes more challenging with the high level of details. On one hand, automated image segmentation algorithms often yield over-segmented polygons due to due to the high spectral heterogeneity of those regions. On the other hand, manual delineation was shown to end up with a significant bias from the interpreter and even a lack of consistency when the same person works more than one hour on the same task. In this study, we aimed at implementing a new filter to increase the contextual consistency of automated segmentation while preserving the geometric precision of the delineation of spectrally homogeneous spatial regions. A new mathematical morphology approach is proposed, which consists in applying a set of rules to an image based on the presence of absence of vegetation pixels within a structuring element. Two composite filters were then built based on the new filters. The opening filter removes isolated vegetation patches inside heterogeneous spatial regions, while the closing filter fills the gaps between those vegetation patches. The filters have been tested on a Pleiades images located in Belgium around the city of Leuven. A composite image was then created with the NIR and Red filtered bands stacked with the original image bands. The composite and the original bands were then segmented using e-Cognition software with the same parameters. The results show that the segmentation of the filtered images is spatially more consistent than the segmentation based on the unfiltered image. The over-segmentation is reduced in the heterogeneous areas, while the precision of the delineation is improved. The objects derived from the filtered images are thus more appropriate for the monitoring of spatial regions.

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36

Pershin, I. M. "The investigation of the characteristics of spatial filters." Sovremennaya nauka i innovatsii, no. 2 (46) (2024): 48–58. http://dx.doi.org/10.37493/2307-910x.2024.2.5.

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Анотація:

The paper presents the results of studies of spatial filters used to extract useful information from distributed signals subject to significant noise influences. The adjustment of the spatial filter to the selection of a given spatial mode is shown, and the results of modeling the operation of the considered spatial filter are also presented.

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37

Brown, Stefanie, Vidhyasaharan Sethu, and David Taubman. "Spatial Wiener filter to reduce spatial aliasing with spherical microphone arrays." Journal of the Acoustical Society of America 145, no. 4 (April 2019): 2254–64. http://dx.doi.org/10.1121/1.5096184.

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38

Hyde, Milo. "Controlling the Spatial Coherence of an Optical Source Using a Spatial Filter." Applied Sciences 8, no. 9 (August 26, 2018): 1465. http://dx.doi.org/10.3390/app8091465.

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This paper presents the theory for controlling the spectral degree of coherence via spatial filtering. Starting with a quasi-homogeneous partially coherent source, the cross-spectral density function of the field at the output of the spatial filter is found by applying Fourier and statistical optics theory. The key relation obtained from this analysis is a closed-form expression for the filter function in terms of the desired output spectral degree of coherence. This theory is verified with Monte Carlo wave-optics simulations of spatial coherence control and beam shaping for potential use in free-space optical communications and directed energy applications. The simulated results are found to be in good agreement with the developed theory. The technique presented in this paper will be useful in applications where coherence control is advantageous, e.g., directed energy, free-space optical communications, remote sensing, medicine, and manufacturing.

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39

Hirukawa, Shigeru, Naomasa Shiraishi, Yuichiro Takeuchi, and Koichi Matsumoto. "Evaluation of Incoherent-Type Pupil Filter: Spatial Filter for Incoherent Stream." Japanese Journal of Applied Physics 33, Part 1, No. 12B (December 30, 1994): 6863–65. http://dx.doi.org/10.1143/jjap.33.6863.

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40

Chłsińska-Macukow, K., and T. Nitka. "Phase-only filter as matched spatial filter with enhanced discrimination capability." Optics Communications 64, no. 3 (November 1987): 224–28. http://dx.doi.org/10.1016/0030-4018(87)90380-4.

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41

Chai, Jinchuan, and Jialiang Ding. "Spatial-temporal regularized UAV correlation filter based on adaptive filter template." Journal of Physics: Conference Series 2577, no. 1 (August 1, 2023): 012018. http://dx.doi.org/10.1088/1742-6596/2577/1/012018.

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Abstract Due to its quick computation, the correlation filter (CF) is frequently employed in object-tracking activities. The occlusion and deformation of the tracking object during the CF tracking process will pollute the filter, which will cause the filter’s performance to degrade. This is especially obvious in unmanned aerial vehicle (UAV) tracking and increased interfered sequences will further aggravate the problem. In response to this problem, we use ARCF as the baseline and add a spatial-temporal regularization term based on the adaptive template. We also introduce a historical filter template to generate a more global adaptive template. The experimental results illustrate that our tracker has the most sophisticated capabilities compared to the other 8 SOTA trackers on the DTB70 benchmark.

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42

Yang, Yen-Hsiu, and Cheng-Lun Chen. "Spatial Domain Adaptive Control of Nonlinear Rotary Systems Subject to Spatially Periodic Disturbances." Journal of Applied Mathematics 2012 (2012): 1–20. http://dx.doi.org/10.1155/2012/610971.

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We propose a generic spatial domain control scheme for a class of nonlinear rotary systems of variable speeds and subject to spatially periodic disturbances. The nonlinear model of the rotary system in time domain is transformed into one in spatial domain employing a coordinate transformation with respect to angular displacement. Under the circumstances that measurement of the system states is not available, a nonlinear state observer is established for providing the estimated states. A two-degree-of-freedom spatial domain control configuration is then proposed to stabilize the system and improve the tracking performance. The first control module applies adaptive backstepping with projected parametric update and concentrates on robust stabilization of the closed-loop system. The second control module introduces an internal model of the periodic disturbances cascaded with a loop-shaping filter, which not only further reduces the tracking error but also improves parametric adaptation. The overall spatial domain output feedback adaptive control system is robust to model uncertainties and state estimated error and capable of rejecting spatially periodic disturbances under varying system speeds. Stability proof of the overall system is given. A design example with simulation demonstrates the applicability of the proposed design.

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43

Surcel, Dorina, and René Laprise. "A General Filter for Stretched-Grid Models: Application in Cartesian Geometry." Monthly Weather Review 139, no. 5 (May 2011): 1637–53. http://dx.doi.org/10.1175/2010mwr3531.1.

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Global climate models with variable resolution are effective means to represent regional scales over an area of interest while avoiding the nesting issues of limited-area models. The stretched-grid approach provides a dynamical downscaling approach that naturally allows two-way interactions between the regional and global scales of motion. Concentrating the resolution over a subset of the earth’s surface increases computational efficiency and reduces the computational costs compared to global uniform high-resolution models; however, it does not come free of some problems related to the variation of resolution. To address the issues associated with the stretching and anisotropy of the computational grid, a general convolution filter with a flexible response function is developed. The main feature of this filter is to locally remove scales shorter than a user-prescribed spatially varying length scale. The filtering effectiveness and computational efficiency of the filter can be custom tailored by an appropriate compromise between the filtering response and the width of the convolution stencil. This approach has been tested in one- and two-dimensional Cartesian geometry. It is shown that an effective filter can be obtained using a limited spatial stencil for the convolution to reduce computational cost, and that an adjustable spatially variable and nearly isotropic response can be obtained for application on variable grids.

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44

Ying, Yue. "Assimilating Observations with Spatially Correlated Errors Using a Serial Ensemble Filter with a Multiscale Approach." Monthly Weather Review 148, no. 8 (July 24, 2020): 3397–412. http://dx.doi.org/10.1175/mwr-d-19-0387.1.

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Abstract The serial ensemble square root filter (EnSRF) typically assumes observation errors to be uncorrelated when assimilating the observations one at a time. This assumption causes the filter solution to be suboptimal when the observation errors are spatially correlated. Using the Lorenz-96 model, this study evaluates the suboptimality due to mischaracterization of observation error spatial correlations. Neglecting spatial correlations in observation errors results in mismatches between the specified and true observation error variances in spectral space, which cannot be resolved by inflating the overall observation error variance. As a remedy, a multiscale observation (MSO) method is proposed to decompose the observations into multiple scale components and assimilate each component with separately adjusted spectral error variance. Experimental results using the Lorenz-96 model show that the serial EnSRF, with the help from the MSO method, can produce solutions that approach the solution from the EnSRF with correctly specified observation error correlations as the number of scale components increases. The MSO method is further tested in a two-layer quasigeostrophic (QG) model framework. In this case, the MSO method is combined with the multiscale localization (MSL) method to allow the use of different localization radii when updating the model state at different scales. The combined method (MSOL) improves the serial EnSRF performance when assimilating observations with spatially correlated errors. With adjusted observation error spectral variances and localization radii, the combined MSOL method provides the best solution in terms of analysis accuracy and filter consistency. Prospects and challenges are also discussed for the implementation of the MSO method for more complex models and observing networks.

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45

Tao, Lu, Yousuke Watanabe, and Hiroaki Takada. "A Lightweight Long-Term Vehicular Motion Prediction Method Leveraging Spatial Database and Kinematic Trajectory Data." ISPRS International Journal of Geo-Information 11, no. 9 (August 29, 2022): 463. http://dx.doi.org/10.3390/ijgi11090463.

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Long-term vehicular motion prediction is a crucial function for both autonomous driving and advanced driver-assistant systems. However, due to the uncertainties of vehicle dynamics and complexities of surroundings, long-term motion prediction is never trivial work. As they combine effects of humans, vehicles and environments, kinematic trajectory data reflect several aspects of vehicles’ spatial behaviors. In this paper, we propose a novel method that leverages spatial database and kinematic trajectory data to achieve long-term vehicular motion prediction in a lightweight way. In our system, a spatial database system is initially embedded in an extended Kalman filter (EKF) framework. The spatial kinematic trajectory data are managed through the database and directly used in motion prediction; namely, weighted means are derived from the spatially retrieved kinematic data and used to update EKF predictions. The proposed method is validated in the real world. The experiments indicate that different weighting methods make a slight accuracy difference. Our method is not data-and-computation-consumed; its performance is acceptable in the limited data conditions and its prediction accuracy is improved as the size of used data sets increases; our method can predict in real time. The efficiency of an unscented Kalman filter (UKF) is compared with that of the EKF. The results show that the UKF can hardly meet real-time requirements.

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46

Sharfaa, Peeroo Azraa Beebee, and Fakhruddin Shamsheer Barodawala. "Effect of Yellow Filter On Contrast Sensitivity Function In High Myopes." Journal of Multidisciplinary Research in Healthcare 9, no. 2 (April 30, 2023): 75–79. http://dx.doi.org/10.15415/jmrh.2023.92008.

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Background: This study examines the influence of yellow filter on the contrast sensitivity function in individuals with high myopia. Through a comprehensive analysis, we explore the potential enhancement of visual perception and contrast sensitivity in this population. The findings con-tribute to our understanding of effective interventions to optimize visual outcomes for high myopes. Purpose: A cross-sectional study was conducted where thirty-four (n=34) healthy young subjects (age range 17-25 years old) having high myopia (SER ≥ 6.00D) without any ocular pathology. Contrast sensitivity func-tion (CSF) was assessed using the Functional Acuity Contrast Test (FACT). First, the CSF was measured without the filter (baseline) then immediately after using a yellow filter (50% transmittance) and then after five minutes of adaptation of the yellow filter. The room illumination was constant at 150lux across all the subjects. Results: One-way repeated measure ANOVA showed that there was no statistically significant difference in contrast sensitivity function for spa-tial frequencies 1.5cpd, and 12cpd. However, there was a statistically significant difference in contrast sensitivity for spatial frequencies 3cpd; F(1.46, 48.20) =28.80, 6cpd; F (1.44, 47.43) =39.99 and 18cpd F (1.53, 50.47) = 44.26. Conclusion: There was an improvement at the moderate and high spatial frequencies (3cpd, 6cpd and 18cpd) but no changes were found at other spatial frequencies. This shows that yellow filter can help to improve the contrast sensitivity function in high myopes at moderate and high spatial frequencies.

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47

INAGAKI, Shintaro, Tomio YAMAURA, and Akira KOBAYASHI. "Spatial Filter Detector with Weighting Function Variability." Transactions of the Society of Instrument and Control Engineers 23, no. 6 (1987): 633–35. http://dx.doi.org/10.9746/sicetr1965.23.633.

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48

OKA, Kazuhiko, Wataru MITSUHASHI, and Hiro YAMASAKI. "Flexible Spatial Filter Built by Electronic Circuits." Transactions of the Society of Instrument and Control Engineers 25, no. 3 (1989): 271–77. http://dx.doi.org/10.9746/sicetr1965.25.271.

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49

Zhu, Ming, JonY Hardeberg, Na Wang, and Bangyong Sun. "Spatial Gamut Mapping based on Guided Filter." Electronic Imaging 2016, no. 20 (February 14, 2016): 1–4. http://dx.doi.org/10.2352/issn.2470-1173.2016.20.color-321.

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50

Cortes, J. "Distributed Kriged Kalman Filter for Spatial Estimation." IEEE Transactions on Automatic Control 54, no. 12 (December 2009): 2816–27. http://dx.doi.org/10.1109/tac.2009.2034192.

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