In this paper we analyze some problems related to the acquisition of multiple illumination images for Polynomial Texture Maps (PTM) or generic Reflectance Transform Imaging (RTI). We show that intensity and directionality nonuniformity can be a relevant issue when acquiring manual sets of images with the standard highlight-based setup both using a flash lamp and a LED light. To maintain a cheap and flexible acquisition setup that can be used on field and by non-experienced users we propose to use a dynamic calibration and correction of the lights based on multiple intensity and direction estimation around the imaged object during the acquisition.
Preliminary tests on the results obtained have been performed by acquiring a specifically designed 3D printed pattern in order to see the accuracy of the acquisition obtained both for spatial discrimination of small structures and normal estimation, and on samples of different types of paper in order to evaluate material discrimination.
We plan to design and build from our analysis and from the tools developed and under development a set of novel procedures and guidelines that can be used to turn the cheap and common RTI acquisition setup from a simple way to enrich object visualization into a powerful method for extracting quantitative characterization both of surface geometry and of reflective properties of different materials. These results could have relevant applications in the Cultural Heritage domain, in order to recognize different materials used in paintings or investigate the ageing status of artifacts’ surface.
We describe a complete pipeline for the detection and accurate automatic segmentation of the optic disc in digital fundus images. This procedure provides separation of vascular information and accurate inpainting of vessel-removed images, symmetry-based optic disc localization, and fitting of incrementally complex contour models at increasing resolutions using information related to inpainted images and vessel masks. Validation experiments, performed on a large dataset of images of healthy and pathological eyes, annotated by experts and partially graded with a quality label, demonstrate the good performances of the proposed approach. The method is able to detect the optic disc and trace its contours better than the other systems presented in the literature and tested on the same data. The average error in the obtained contour masks is reasonably close to the interoperator errors and suitable for practical applications. The optic disc segmentation pipeline is currently integrated in a complete software suite for the semiautomatic quantification of retinal vessel properties from fundus camera images (VAMPIRE).