Abstract
Focal blur segmentation is one of the interesting topics in computer vision. With recent improvements of camera devices, multiple focal blur images of different focal settings can be obtained by a single shooting. Utilizing the information of multiple focal blur images is expected to improve the segmentation performance. We propose one of the automatic focal blur segmentation using a pair of two focal blur images with different focal settings. Difference of blur features can be obtained from an image pair which are focused on an object and background, respectively. A theoretical threshold identifies the object and background in the difference of blur feature space. The proposed method consists of (i) the theoretical thresholding in the blur feature space; and (ii) energy minimization based on Graphcuts using color and blur features. We evaluate the proposed method using 12 and 48 image pairs, including single objects and flowers, respectively. As results of the evaluation, the averaged Informedness of the initial and the final segmentation are 0.897 and 0.972 for the single object images, and 0.730 and 0.827 for the flower images, respectively.
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Takayama, N., Takahashi, H. (2020). Automatic Focal Blur Segmentation Based on Difference of Blur Feature Using Theoretical Thresholding and Graphcuts. In: Blanc-Talon, J., Delmas, P., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2020. Lecture Notes in Computer Science(), vol 12002. Springer, Cham. https://doi.org/10.1007/978-3-030-40605-9_29
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