{"_id":"M5eAPwemFYw7eSAbi","bibbaseid":"flasseur-denis-thibaut-olivier-fournier-expacodetectionofanextendedpatternundernonstationarycorrelatednoisebypatchcovariancemodeling-2019","authorIDs":[],"author_short":["Flasseur, O.","Denis, L.","Thiébaut, É.","Olivier, T.","Fournier, C."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["O."],"propositions":[],"lastnames":["Flasseur"],"suffixes":[]},{"firstnames":["L."],"propositions":[],"lastnames":["Denis"],"suffixes":[]},{"firstnames":["É."],"propositions":[],"lastnames":["Thiébaut"],"suffixes":[]},{"firstnames":["T."],"propositions":[],"lastnames":["Olivier"],"suffixes":[]},{"firstnames":["C."],"propositions":[],"lastnames":["Fournier"],"suffixes":[]}],"booktitle":"2019 27th European Signal Processing Conference (EUSIPCO)","title":"ExPACO: detection of an extended pattern under nonstationary correlated noise by patch covariance modeling","year":"2019","pages":"1-5","abstract":"In several areas of imaging, it is necessary to detect the weak signal of a known pattern superimposed over a background. Because of its temporal fluctuations, the background may be difficult to suppress. Detection of the pattern then requires a statistical modeling of the background. Due to difficulties related to (i) the estimation of the spatial correlations of the background, and (ii) the application of an optimal detector that accounts for these correlations, it is common practice to neglect them.In this work, spatial correlations at the scale of an image patch are locally estimated based on several background images. A fast algorithm for the computation of detection maps is derived. The proposed approach is evaluated on images obtained from a holographic microscope.","keywords":"correlation methods;covariance matrices;image denoising;object detection;statistical analysis;ExPACO;extended pattern;nonstationary correlated noise;temporal fluctuations;statistical modeling;spatial correlations;optimal detector;image patch;background images;patch covariance modeling;holographic microscope;Correlation;Covariance matrices;Computational modeling;Two dimensional displays;Microscopy;Task analysis;Diffraction;matched filter;patch;shrinkage covariance estimator;correlation","doi":"10.23919/EUSIPCO.2019.8903021","issn":"2076-1465","month":"Sep.","url":"https://www.eurasip.org/proceedings/eusipco/eusipco2019/proceedings/papers/1570532796.pdf","bibtex":"@InProceedings{8903021,\n author = {O. Flasseur and L. Denis and É. Thiébaut and T. Olivier and C. Fournier},\n booktitle = {2019 27th European Signal Processing Conference (EUSIPCO)},\n title = {ExPACO: detection of an extended pattern under nonstationary correlated noise by patch covariance modeling},\n year = {2019},\n pages = {1-5},\n abstract = {In several areas of imaging, it is necessary to detect the weak signal of a known pattern superimposed over a background. Because of its temporal fluctuations, the background may be difficult to suppress. Detection of the pattern then requires a statistical modeling of the background. Due to difficulties related to (i) the estimation of the spatial correlations of the background, and (ii) the application of an optimal detector that accounts for these correlations, it is common practice to neglect them.In this work, spatial correlations at the scale of an image patch are locally estimated based on several background images. A fast algorithm for the computation of detection maps is derived. The proposed approach is evaluated on images obtained from a holographic microscope.},\n keywords = {correlation methods;covariance matrices;image denoising;object detection;statistical analysis;ExPACO;extended pattern;nonstationary correlated noise;temporal fluctuations;statistical modeling;spatial correlations;optimal detector;image patch;background images;patch covariance modeling;holographic microscope;Correlation;Covariance matrices;Computational modeling;Two dimensional displays;Microscopy;Task analysis;Diffraction;matched filter;patch;shrinkage covariance estimator;correlation},\n doi = {10.23919/EUSIPCO.2019.8903021},\n issn = {2076-1465},\n month = {Sep.},\n url = {https://www.eurasip.org/proceedings/eusipco/eusipco2019/proceedings/papers/1570532796.pdf},\n}\n\n","author_short":["Flasseur, O.","Denis, L.","Thiébaut, É.","Olivier, T.","Fournier, C."],"key":"8903021","id":"8903021","bibbaseid":"flasseur-denis-thibaut-olivier-fournier-expacodetectionofanextendedpatternundernonstationarycorrelatednoisebypatchcovariancemodeling-2019","role":"author","urls":{"Paper":"https://www.eurasip.org/proceedings/eusipco/eusipco2019/proceedings/papers/1570532796.pdf"},"keyword":["correlation methods;covariance matrices;image denoising;object detection;statistical analysis;ExPACO;extended pattern;nonstationary correlated noise;temporal fluctuations;statistical modeling;spatial correlations;optimal detector;image patch;background images;patch covariance modeling;holographic microscope;Correlation;Covariance matrices;Computational modeling;Two dimensional displays;Microscopy;Task analysis;Diffraction;matched filter;patch;shrinkage covariance estimator;correlation"],"metadata":{"authorlinks":{}},"downloads":0},"bibtype":"inproceedings","biburl":"https://raw.githubusercontent.com/Roznn/EUSIPCO/main/eusipco2019url.bib","creationDate":"2021-02-11T19:15:22.097Z","downloads":0,"keywords":["correlation methods;covariance matrices;image denoising;object detection;statistical analysis;expaco;extended pattern;nonstationary correlated noise;temporal fluctuations;statistical modeling;spatial correlations;optimal detector;image patch;background images;patch covariance modeling;holographic microscope;correlation;covariance matrices;computational modeling;two dimensional displays;microscopy;task analysis;diffraction;matched filter;patch;shrinkage covariance estimator;correlation"],"search_terms":["expaco","detection","extended","pattern","under","nonstationary","correlated","noise","patch","covariance","modeling","flasseur","denis","thiébaut","olivier","fournier"],"title":"ExPACO: detection of an extended pattern under nonstationary correlated noise by patch covariance modeling","year":2019,"dataSources":["NqWTiMfRR56v86wRs","r6oz3cMyC99QfiuHW"]}