A general framework for fuzzy morphological associative memories
Fuzzy Sets and Systems
Image and Vision Computing
Lossy compression through segmentation on low depth-of-field images
Digital Signal Processing
Development of expert system for extraction of the objects of interest
Expert Systems with Applications: An International Journal
Automatic segmentation of focused objects from images with low depth of field
Pattern Recognition Letters
Single image defocus map estimation using local contrast prior
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A two-level strategy for segmenting center of interest from pictures
Expert Systems with Applications: An International Journal
Journal of Mathematical Imaging and Vision
Segmenting focused objects based on the Amplitude Decomposition Model
Pattern Recognition Letters
Hi-index | 0.02 |
We propose a novel algorithm to partition an image with low depth-of-field (DOF) into focused object-of-interest (OOI) and defocused background. The proposed algorithm unfolds into three steps. In the first step, we transform the low-DOF image into an appropriate feature space, in which the spatial distribution of the high-frequency components is represented. This is conducted by computing higher order statistics (HOS) for all pixels in the low-DOF image. Next, the obtained feature space, which is called HOS map in this paper, is simplified by removing small dark holes and bright patches using a morphological filter by reconstruction. Finally, the OOI is extracted by applying region merging to the simplified image and by thresholding. Unlike the previous methods that rely on sharp details of OOI only, the proposed algorithm complements the limitation of them by using morphological filters, which also allows perfect preservation of the contour information. Compared with the previous methods, the proposed method yields more accurate segmentation results, supporting faster processing.