Digital cameras are often used in recent days for photographic documentation in medical sciences. However, color reproducibility of same objects suffers from different illuminations and lighting conditions. This varia...
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ISBN:
(纸本)9781510607170;9781510607187
Digital cameras are often used in recent days for photographic documentation in medical sciences. However, color reproducibility of same objects suffers from different illuminations and lighting conditions. This variation in color representation is problematic when the images are used for segmentation and measurements based on color thresholds. In this paper, motivated by photographic follow-up of chronic wounds, we assess the impact of (i) gamma correction, (ii) white balancing, (iii) background unification, and (iv) reference card-based color correction. Automatic gamma correction and white balancing are applied to support the calibration procedure, where gamma correction is a nonlinear color transform. For unevenly illuminated images, non-uniform illumination correction is applied. In the last step, we apply colorimetric calibration using a reference color card of 24 patches with known colors. A lattice detection algorithm is used for locating the card. The least squares algorithm is applied for affine color calibration in the RGB model. We have tested the algorithm on images with seven different types of illumination: with and without flash using three different off-the-shelf cameras including smartphones. We analyzed the spread of resulting color value of selected color patch before and after applying the calibration. Additionally, we checked the individual contribution of different steps of the whole calibration process. Using all steps, we were able to achieve a maximum of 81% reduction in standard deviation of color patch values in resulting images comparing to the original images. That supports manual as well as automatic quantitative wound assessments with off-the-shelf devices.
Conventional subspace decomposition-based direction of arrival (DOA) estimation methods require eigenvalue decomposition of spatial covariance matrix, therefore these methods are computationally intensive, and their i...
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Conventional subspace decomposition-based direction of arrival (DOA) estimation methods require eigenvalue decomposition of spatial covariance matrix, therefore these methods are computationally intensive, and their implementation is difficult in real-time applications. However, a DOA estimation method based on fixed step size least meansquare (LMS) algorithm has overcome this deficiency but the suitable selection of step size is very difficult. A DOA estimation method based on the variable step size LMS algorithm is proposed in this article. In the proposed method, the step size is updated by using the estimated error signal. The spatial spectrum is obtained by the reciprocal of array pattern, where the peak values indicate the estimated DOAs of signals. The proposed method can provide better performance with low computational cost. The performance of proposed method is verified by the simulations and compared with some existing methods.
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