Search

In this letter, we propose a novel automatic algorithm for road extraction from remote sensing images. The algorithm includes low- and high-level processing. In the low-level processing, we determine a normalized second derivative map of road profiles of a generalized bar shape, which is width invariant and contrast proportional, and accordingly obtain initial road center pixels. In the high-level processing, using the map and initial center pixels, we initially determine road segments. The segments are then locally refined using their orientation randomness and length-to-width ratio and further refined via global graph-cut optimization. A final road network is thereby extracted in a robust manner. Experimental results demonstrate that the proposed algorithm provides noticeably more robust and higher road extraction performance in various images compared with the existing algorithms. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a novel pixel-level multisensor image fusion algorithm with simultaneous contrast enhancement. In order to accomplish both image fusion and contrast enhancement simultaneously, we suggest a modified framework of the subband-decomposed multiscale retinex (SDMSR), our previous contrast enhancement algorithm. This framework is based on a fusion strategy that reflects the multiscale characteristics of the SDMSR well. We first apply two complementary intensity transfer functions to source images in order to effectively utilize hidden information in both shadows and highlights in the fusion process. We then decompose retinex outputs into nearly nonoverlapping spectral subbands. The decomposed retinex outputs are then fused subband-by-subband, by using global weighting as well as local weighting to overcome the limitations of the pixel-based fusion approach. After the fusion process, we apply a space-varying subband gain to each fused SD retinex output according to the subband characteristic so that the contrast of the fused image can be effectively enhanced. In addition, in order to effectively manage artifacts and noise, we make the degree of enhancement of fused details adjustable by improving a detail adjustment function. From experiments with various multisensor image pairs, the results clearly demonstrate that even if source images have poor contrast, the proposed algorithm makes it possible to generate a fused image with highly enhanced contrast while preserving visually salient information contained in the source images. [ABSTRACT FROM AUTHOR]

Monitoring a degranulation process in a live mast cell is a quite important issue in immunology and pharmacology. Because the size of a granule is normally much smaller than the resolution limit of an optical microscope system, there is no direct real-time live cell imaging technique for observing degranulation processes except for fluorescence imaging techniques. In this research, we propose optical quantitative phase microscopy (QPM) as a new observation tool to study degranulation processes in a live mast cell without any fluorescence labeling. We measure the cell volumes and the cross sectional profiles (x-z plane) of an RBL-2H3 cell and a HeLa cell, before and after they are exposed to calcium ionophore A23187 and silver nanoparticles (AgNPs). We verify that the volume and the cross sectional line profile of the RBL-2H3 cell were changed significantly when it was exposed to A23187. When 50 microg/mL of AgNP is used instead of A23187, the measurements of cell volume and cross sectional profiles indicate that RBL-2H3 cells also follow degranulation processes. Degranulation processes for these cells are verified by monitoring the increase of intracellular calcium ([Ca(2+)](i)) and histamine with fluorescent methods.