The Experts below are selected from a list of 2652 Experts worldwide ranked by ideXlab platform

Jussi Saarinen - One of the best experts on this subject based on the ideXlab platform.

  • visual features underlying perceived brightness as revealed by classification images
    PLOS ONE, 2009
    Co-Authors: Ilmari Kurki, Tarja Peromaa, Aapo Hyvärinen, Jussi Saarinen
    Abstract:

    Along with Physical Luminance, the perceived brightness is known to depend on the spatial structure of the stimulus. Often it is assumed that neural computation of the brightness is based on the analysis of Luminance borders of the stimulus. However, this has not been tested directly. We introduce a new variant of the psychoPhysical reverse-correlation or classification image method to estimate and localize the Physical features of the stimuli which correlate with the perceived brightness, using a brightness-matching task. We derive classification images for the illusory Craik-O'Brien-Cornsweet stimulus and a “real” uniform step stimulus. For both stimuli, classification images reveal a positive peak at the stimulus border, along with a negative peak at the background, but are flat at the center of the stimulus, suggesting that brightness is determined solely by the border information. Features in the perceptually completed area in the Craik-O'Brien-Cornsweet do not contribute to its brightness, nor could we see low-frequency boosting, which has been offered as an explanation for the illusion. Tuning of the classification image profiles changes remarkably little with stimulus size. This supports the idea that only certain spatial scales are used for computing the brightness of a surface.

Ilmari Kurki - One of the best experts on this subject based on the ideXlab platform.

  • visual features underlying perceived brightness as revealed by classification images
    PLOS ONE, 2009
    Co-Authors: Ilmari Kurki, Tarja Peromaa, Aapo Hyvärinen, Jussi Saarinen
    Abstract:

    Along with Physical Luminance, the perceived brightness is known to depend on the spatial structure of the stimulus. Often it is assumed that neural computation of the brightness is based on the analysis of Luminance borders of the stimulus. However, this has not been tested directly. We introduce a new variant of the psychoPhysical reverse-correlation or classification image method to estimate and localize the Physical features of the stimuli which correlate with the perceived brightness, using a brightness-matching task. We derive classification images for the illusory Craik-O'Brien-Cornsweet stimulus and a “real” uniform step stimulus. For both stimuli, classification images reveal a positive peak at the stimulus border, along with a negative peak at the background, but are flat at the center of the stimulus, suggesting that brightness is determined solely by the border information. Features in the perceptually completed area in the Craik-O'Brien-Cornsweet do not contribute to its brightness, nor could we see low-frequency boosting, which has been offered as an explanation for the illusion. Tuning of the classification image profiles changes remarkably little with stimulus size. This supports the idea that only certain spatial scales are used for computing the brightness of a surface.

Wandeto, John M. - One of the best experts on this subject based on the ideXlab platform.

  • Unsupervised classification of cell imaging data using the quantization error in a Self Organizing Map
    HAL CCSD, 2021
    Co-Authors: Dresp Birgitta, Wandeto, John M.
    Abstract:

    International audienceThis study exploits previously demonstrated properties such as sensitivity to the spatial extent and the intensity of local image contrast of the quantization error in the output of a Self Organizing Map (SOM QE). Here, the SOM QE is applied to double color staining based cell viability data in 96 image simulations. The results show that the SOM QE consistently and in only a few seconds detects fine regular spatial increases in relative amounts of RED or GREEN pixel staining across the test images, reflecting small, systematic increases or decreases in the percentage of theoretical cell viability below the critical threshold. Such small changes may carry clinical significance, but are almost impossible to detect by human vision. Moreover, we demonstrate a clear sensitivity of the SOM QE to differences in the relative Physical Luminance (Y) of the colors, which here translates into a RED GREEN color selectivity. Across differences in relative Luminance, the SOM QE exhibits consistently greater sensitivity to the smallest spatial increases in RED image pixels compared with smallest increases of identical spatial extents in GREEN image pixels. Further selective color contrast studies on simulations of biological imaging data will allow generating increasingly larger benchmark datasets and, ultimately, unravel the full potential of fast, economic, and unprecedentedly precise biological data analysis using the SOM QE

  • Unsupervised Classification of Cell Imaging Data Using the Quantization Error in a Self-Organizing Map
    HAL CCSD, 2020
    Co-Authors: Dresp Birgitta, Wandeto, John M.
    Abstract:

    International audienceThis study exploits previously demonstrated properties (i.e. sensitivity to spatial extent and intensity of local image contrasts) of the quantization error in the output of a Self-Organizing Map (SOM-QE). Here, the SOM-QE is applied to double-color-staining based cell viability data in 96 image simulations. The results from this study show that, as expected, SOM-QE consistently and in only a few seconds detects fine regular spatial increase in relative amounts of RED or GREEN pixel staining across the testimages, reflecting small, systematic increase or decrease in the percentage of theoretical cell viability below a critical threshold. While such small changes may carry clinical significance, they are almost impossible to detect by human vision. Moreover,here we demonstrate an expected sensitivity of the SOM-QE to differences in the relative Physical Luminance (Y) of the colors, which translates into a RED-GREEN color selectivity. Across differences in relative Luminance, the SOM-QE exhibits consistently greater sensitivity to the smallest spatial increase in RED image pixels compared with smallest increases of the same spatial magnitude in GREEN image pixels. Further selective color contrast studies on simulations of biological imaging data will allow generating increasingly larger benchmark datasets and, ultimately, unravel the full potential of fast, economic, and unprecedentedly precise predictive imaging data analysis based on SOM-QE

Dresp Birgitta - One of the best experts on this subject based on the ideXlab platform.

  • Unsupervised classification of cell imaging data using the quantization error in a Self Organizing Map
    HAL CCSD, 2021
    Co-Authors: Dresp Birgitta, Wandeto, John M.
    Abstract:

    International audienceThis study exploits previously demonstrated properties such as sensitivity to the spatial extent and the intensity of local image contrast of the quantization error in the output of a Self Organizing Map (SOM QE). Here, the SOM QE is applied to double color staining based cell viability data in 96 image simulations. The results show that the SOM QE consistently and in only a few seconds detects fine regular spatial increases in relative amounts of RED or GREEN pixel staining across the test images, reflecting small, systematic increases or decreases in the percentage of theoretical cell viability below the critical threshold. Such small changes may carry clinical significance, but are almost impossible to detect by human vision. Moreover, we demonstrate a clear sensitivity of the SOM QE to differences in the relative Physical Luminance (Y) of the colors, which here translates into a RED GREEN color selectivity. Across differences in relative Luminance, the SOM QE exhibits consistently greater sensitivity to the smallest spatial increases in RED image pixels compared with smallest increases of identical spatial extents in GREEN image pixels. Further selective color contrast studies on simulations of biological imaging data will allow generating increasingly larger benchmark datasets and, ultimately, unravel the full potential of fast, economic, and unprecedentedly precise biological data analysis using the SOM QE

  • Unsupervised classification of cell imaging data using the quantization error in a Self Organizing Map
    2021
    Co-Authors: Dresp Birgitta, Jm Wandeto
    Abstract:

    This study exploits previously demonstrated properties such as sensitivity to the spatial extent and the intensity of local image contrast of the quantization error in the output of a Self Organizing Map (SOM QE). Here, the SOM QE is applied to double color staining based cell viability data in 96 image simulations. The results show that the SOM QE consistently and in only a few seconds detects fine regular spatial increases in relative amounts of RED or GREEN pixel staining across the test images, reflecting small, systematic increases or decreases in the percentage of theoretical cell viability below the critical threshold. Such small changes may carry clinical significance, but are almost impossible to detect by human vision. Moreover, we demonstrate a clear sensitivity of the SOM QE to differences in the relative Physical Luminance (Y) of the colors, which here translates into a RED GREEN color selectivity. Across differences in relative Luminance, the SOM QE exhibits consistently greater sensitivity to the smallest spatial increases in RED image pixels compared with smallest increases of identical spatial extents in GREEN image pixels. Further selective color contrast studies on simulations of biological imaging data will allow generating increasingly larger benchmark datasets and, ultimately, unravel the full potential of fast, economic, and unprecedentedly precise biological data analysis using the SOM QE.Comment: arXiv admin note: substantial text overlap with arXiv:2011.05209, arXiv:2011.0397

  • Unsupervised Classification of Cell Imaging Data Using the Quantization Error in a Self-Organizing Map
    HAL CCSD, 2020
    Co-Authors: Dresp Birgitta, Wandeto, John M.
    Abstract:

    International audienceThis study exploits previously demonstrated properties (i.e. sensitivity to spatial extent and intensity of local image contrasts) of the quantization error in the output of a Self-Organizing Map (SOM-QE). Here, the SOM-QE is applied to double-color-staining based cell viability data in 96 image simulations. The results from this study show that, as expected, SOM-QE consistently and in only a few seconds detects fine regular spatial increase in relative amounts of RED or GREEN pixel staining across the testimages, reflecting small, systematic increase or decrease in the percentage of theoretical cell viability below a critical threshold. While such small changes may carry clinical significance, they are almost impossible to detect by human vision. Moreover,here we demonstrate an expected sensitivity of the SOM-QE to differences in the relative Physical Luminance (Y) of the colors, which translates into a RED-GREEN color selectivity. Across differences in relative Luminance, the SOM-QE exhibits consistently greater sensitivity to the smallest spatial increase in RED image pixels compared with smallest increases of the same spatial magnitude in GREEN image pixels. Further selective color contrast studies on simulations of biological imaging data will allow generating increasingly larger benchmark datasets and, ultimately, unravel the full potential of fast, economic, and unprecedentedly precise predictive imaging data analysis based on SOM-QE

Tarja Peromaa - One of the best experts on this subject based on the ideXlab platform.

  • visual features underlying perceived brightness as revealed by classification images
    PLOS ONE, 2009
    Co-Authors: Ilmari Kurki, Tarja Peromaa, Aapo Hyvärinen, Jussi Saarinen
    Abstract:

    Along with Physical Luminance, the perceived brightness is known to depend on the spatial structure of the stimulus. Often it is assumed that neural computation of the brightness is based on the analysis of Luminance borders of the stimulus. However, this has not been tested directly. We introduce a new variant of the psychoPhysical reverse-correlation or classification image method to estimate and localize the Physical features of the stimuli which correlate with the perceived brightness, using a brightness-matching task. We derive classification images for the illusory Craik-O'Brien-Cornsweet stimulus and a “real” uniform step stimulus. For both stimuli, classification images reveal a positive peak at the stimulus border, along with a negative peak at the background, but are flat at the center of the stimulus, suggesting that brightness is determined solely by the border information. Features in the perceptually completed area in the Craik-O'Brien-Cornsweet do not contribute to its brightness, nor could we see low-frequency boosting, which has been offered as an explanation for the illusion. Tuning of the classification image profiles changes remarkably little with stimulus size. This supports the idea that only certain spatial scales are used for computing the brightness of a surface.