International audienceWe present a novel 3-D recovery method based on structured light. This method unifies depth from focus (DFF) and depth from defocus (DFD) techniques with the use of a dynamic (de)focused projection. With this approach, the image acquisition system is specifically constructed to keep a whole object sharp in all the captured images. Therefore, only the projected patterns experience different defocused deformations according to the object's depths. When the projected patterns are out of focus, their point-spread function (PSF) is assumed to follow a Gaussian distribution. The final depth is computed by the analysis of the relationship between the sets of PSFs obtained from different blurs and the variation of the object's depths. Our new depth estimation can be employed as a stand-alone strategy. It has no problem with occlusion and correspondence issues. Moreover, it handles textureless and partially reflective surfaces. The experimental results on real objects demonstrate the effective performance of our approach, providing reliable depth estimation and competitive time consumption. It uses fewer input images than DFF, and unlike DFD, it ensures that the PSF is locally unique
In this paper, we propose a novel active 3D recovery method based on dynamic (de)focused light. The method combines both depth from focus (DFF) and depth from defocus (DFD) techniques. With this approach, optimized illumination pattern is projected on the object in order to enforce strong dominant texture on the surface. The imaging system is specifically constructed to keep the whole object sharp in all captured images. Consequently, only projected patterns experience the defocused deformation according to an object depth. Projected light pattern images are acquired within certain focused ranges similar to DFF approach, while the focus measures across these images are calculated for depth estimation by using DFD manner. This guarantees that at least one focus or near-focus image within depth of field exists in the computation. Therefore, the final reconstruction is supposed to be prominent to the one obtained from DFD and also less computational extensive compared to DFF provided.
Abstract. To tackle thorax movement from CT images, we have developed a platform to simulate a customized breathing cycle, where the pulmonary movement has been considered only at the rough border of the whole lung by artificial neural networks (ANN). The goal of this work is to include additional information of the lung lobe. Thus, more ANN will be used and future simulation will be able to take into consideration the impact of tumor on lobe movement. We present a new automatic segmentation algorithm that enables the extraction of lobar contour data using sliding mask and direction estimation. These improvements enhance the overall system performance in which higher precision and more accurate treatments can be expected.
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