218 research outputs found
Accurate and reliable segmentation of the optic disc in digital fundus images
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)
Matching techniques to compute image motion
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 flow vector estimation and reduce the computational cost by using filters, multi-scale approach and mask sub-sampling. Further, new algorithms to obtain a sub-pixel accuracy of the flow 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 flow computation is suitable for practical and real-time applications.247–260Pubblicat
SIMCO: SIMilarity-based object COunting
We present SIMCO, the first agnostic multi-class object counting approach.
SIMCO starts by detecting foreground objects through a novel Mask RCNN-based
architecture trained beforehand (just once) on a brand-new synthetic 2D shape
dataset, InShape; the idea is to highlight every object resembling a primitive
2D shape (circle, square, rectangle, etc.). Each object detected is described
by a low-dimensional embedding, obtained from a novel similarity-based head
branch; this latter implements a triplet loss, encouraging similar objects
(same 2D shape + color and scale) to map close. Subsequently, SIMCO uses this
embedding for clustering, so that different types of objects can emerge and be
counted, making SIMCO the very first multi-class unsupervised counter.
Experiments show that SIMCO provides state-of-the-art scores on counting
benchmarks and that it can also help in many challenging image understanding
tasks
Fast artifacts-free image interpolation
In this paper we describe a novel general purpose image interpolation method based on the combination of two different procedures. First, an adaptive algorithm is applied interpolating locally pixel values along the direction where second order image derivative is lower. Then interpolated values are modified using an iterative refinement minimizing differences in second order image derivatives, maximizing second order derivative values and smoothing isolevel curves. The first algorithm itself provides edge preserving images that are measurably better than those obtained with similarly fast methods presented in the literature. The full method provides interpolated images with a ”natural ” appearance that do not present the artifacts affecting linear and nonlinear methods. Objective and subjective tests on a wide series of natural images clearly show the advantages of the proposed technique over existing approaches.
Elusive phase transition in the replica limit of monitored systems
We study an exactly solvable model of monitored dynamics in a system of
spin- particles with pairwise all-to-all noisy interactions, where each
spin is constantly perturbed by weak measurements of the spin component in a
random direction. We make use of the replica trick to account for the Born's
rule weighting of the measurement outcomes in the study of purification and
other observables, with an exact description in the large- limit. We find
that the nature of the phase transition strongly depends on the number of
replicas used in the calculation, with the appearance of non-perturbative
logarithmic corrections that destroy the disentangled/purifying phase in the
relevant replica limit. Specifically, we observe that the
purification time of a mixed state in the weak measurement phase is always
exponentially long in the system size for arbitrary strong measurement rates.Comment: 7+16 pages, 3+4 figure
A Novel Framework for Highlight Reflectance Transformation Imaging
We propose a novel pipeline and related software tools for processing the multi-light image collections (MLICs) acquired in different application contexts to obtain shape and appearance information of captured surfaces, as well as to derive compact relightable representations of them. Our pipeline extends the popular Highlight Reflectance Transformation Imaging (H-RTI) framework, which is widely used in the Cultural Heritage domain. We support, in particular, perspective camera modeling, per-pixel interpolated light direction estimation, as well as light normalization correcting vignetting and uneven non-directional illumination. Furthermore, we propose two novel easy-to-use software tools to simplify all processing steps. The tools, in addition to support easy processing and encoding of pixel data, implement a variety of visualizations, as well as multiple reflectance-model-fitting options. Experimental tests on synthetic and real-world MLICs demonstrate the usefulness of the novel algorithmic framework and the potential benefits of the proposed tools for end-user applications.Terms: "European Union (EU)" & "Horizon 2020" / Action: H2020-EU.3.6.3. - Reflective societies - cultural heritage and European identity / Acronym: Scan4Reco / Grant number: 665091DSURF project (PRIN 2015) funded by the Italian Ministry of University and ResearchSardinian Regional Authorities under projects VIGEC and Vis&VideoLa
Dicom image handling for medical analysis and the ViVa project
The aim of this work was to build the basic system for medical image retrieval and elaboration
suitable for the ViVa Project, aiming at building, from clinical data, virtual vascular systems where
also blood flow fields can be simulated and analysed
Eyes on teleporting: comparing locomotion techniques in Virtual Reality with respect to presence, sickness and spatial orientation
This work compares three locomotion techniques for an immersive VR
environment: two different types of teleporting (with and without animation)
and a manual (joystick-based) technique. We tested the effect of these
techniques on visual motion sickness, spatial awareness, presence, subjective
pleasantness, and perceived difficulty of operating the navigation. We
collected eye tracking and head and body orientation data to investigate the
relationships between motion, vection, and sickness. Our study confirms some
results already discussed in the literature regarding the reduced invasiveness
and the high usability of instant teleport while increasing the evidence
against the hypothesis of reduced spatial awareness induced by this technique.
We reinforce the evidence about the issues of extending teleporting with
animation. Furthermore, we offer some new evidence of a benefit to the user
experience of the manual technique and the correlation of the sickness felt in
this condition with head movements. The findings of this study contribute to
the ongoing debate on the development of guidelines on navigation interfaces in
specific VR environments
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