The problem of creating artifact-free upscaled images appearing sharp and natural to the human observer is probably more interesting and less trivial than it may appear. The solution to the problem, often referred to also as "single-image super-resolution," is related both to the statistical relationship between low-resolution and high-resolution image sampling and to the human perception of image quality. In many practical applications, simple linear or cubic interpolation algorithms are applied for this task, but the results obtained are not really satisfactory, being affected by relevant artifacts like blurring and jaggies. Several methods have been proposed to obtain better results, involving simple heuristics, edge modeling, or statistical learning. The most powerful ones, however, present a high computational complexity and are not suitable for real-time applications, while fast methods, even if edge adaptive, are not able to provide artifacts-free images. In this paper, we describe a new upscaling method (iterative curvature-based interpolation) based on a two-step grid filling and an iterative correction of the interpolated pixels obtained by minimizing an objective function depending on the second-order directional derivatives of the image intensity. We show that the constraints used to derive the function are related with those applied in another well-known interpolation method, providing good results but computationally heavy (i.e., new edge-directed interpolation (NEDI). The high quality of the images enlarged with the new method is demonstrated with objective and subjective tests, while the computation time is reduced of one to two orders of magnitude with respect to NEDI so that we were able, using a graphics processing unit implementation based on the nVidia Compute Unified Device Architecture technology, to obtain real-time performances.

We present VAMPIRE, a software application for efficient, semi-automatic quantification of retinal vessel properties with large collections of fundus camera images. VAMPIRE is also an international collaborative project of four image processing groups and five clinical centres. The system provides automatic detection of retinal landmarks (optic disc, vasculature), and quantifies key parameters used frequently in investigative studies: vessel width, vessel branching coefficients, and tortuosity. The ultimate vision is to make VAMPIRE available as a public tool, to support quantification and analysis of large collections of fundus camera images.

Abstract-This paper describes procedures for obtaining a reliable and dense optical flow from image sequences taken by a television (TV) camera mounted on a car moving in usual outdoor scenarios. The optical flow can be computed from these image sequences by using several techniques. Differential techniques to compute the optical flow do not provide adequate results, because of a poor texture in images and the presence of shocks and vibrations experienced by the TV camera during image acquisition. By using correlation based techniques and by correcting the optical flows for shocks and vibrations, useful sequences of optical flows can be obtained. When the car is moving along a flat road and the optical axis of the TV camera is parallel to the ground, the motion field is expected to be almost quadratic and have a specific structure. As a consequence the egomotion can be estimated from this optical flow and information on the speed and the angular velocity of the moving vehicle are obtained. By analyzing the optical flow it is possible to recover also a coarse segmentation of the flow, in which objects moving with a different speed are identified. By combining information from intensity edges a better localization of motion boundaries are obtained. These results suggest that the optical flow can be successfully used by a vision system for assisting a driver in a vehicle moving in usual streets and motorways.

This paper describes a thorough analysis of the pattern matching techniques used to compute image motion from a sequence of two or more images. Several correlation/distance measures are tested, and problems in displacement estimation are investigated. As a byproduct of this analysis, several novel techniques are presented which improve the accuracy of ow vector estimation and reduce the computational cost by using lters, multi-scale approach and mask sub-sampling. Furthermore new algorithms to get a sub-pixel accuracy of the ow are proposed. A large amount of experimental tests have been performed to compare all the techniques proposed, in order to understand which are the most useful for practical applications, and the results obtained are very accurate, showing that correlation-based ow computation is suitable for practical and real-time applications.

3D models of humans are commonly used within computer graphics and vision, and so the ability to distinguish between body shapes is an important shape retrieval problem. We extend our recent paper which provided a benchmark for testing non-rigid 3D shape retrieval algorithms on 3D human models. This benchmark provided a far stricter challenge than previous shape benchmarks. We

Interactions within virtual environments often require manipulating 3D virtual objects. To this end, researchers have endeavoured to find efficient solutions using either traditional input devices or focusing on different input modalities, such as touch and mid‐air gestures. Different virtual environments and diverse input modalities present specific issues to control object position, orientation and scaling: traditional mouse input, for example, presents non‐trivial challenges because of the need to map between 2D input and 3D actions. While interactive surfaces enable more natural approaches, they still require smart mappings. Mid‐air gestures can be exploited to offer natural manipulations mimicking interactions with physical objects. However, these approaches often lack precision and control. All these issues and many others have been addressed in a large body of work. In this article, we survey the state‐of‐the‐art in 3D object manipulation, ranging from traditional desktop approaches to touch and mid‐air interfaces, to interact in diverse virtual environments. We propose a new taxonomy to better classify manipulation properties. Using our taxonomy, we discuss the techniques presented in the surveyed literature, highlighting trends, guidelines and open challenges, that can be useful both to future research and to developers of 3D user interfaces.

Abstract. 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).

This paper summarizes three recent, novel algorithms developed\ud within VAMPIRE, namely optic disc and macula detection, arteryvein\ud classification, and enhancement of binary vessel masks, and\ud their performance assessment. VAMPIRE is an international collaboration\ud growing a suite of software tools to allow efficient quantification\ud of morphological properties of the retinal vasculature in large\ud collections of fundus camera images. VAMPIRE measurements\ud are currently mostly used in biomarker research, i.e., investigating\ud associations between the morphology of the retinal vasculature and\ud a number of clinical and cognitive conditions