4,134 research outputs found
Perceptual Quality Evaluation of 3D Triangle Mesh: A Technical Review
© 2018 IEEE. During mesh processing operations (e.g. simplifications, compression, and watermarking), a 3D triangle mesh is subject to various visible distortions on mesh surface which result in a need to estimate visual quality. The necessity of perceptual quality evaluation is already established, as in most cases, human beings are the end users of 3D meshes. To measure such kinds of distortions, the metrics that consider geometric measures integrating human visual system (HVS) is called perceptual quality metrics. In this paper, we direct an expansive study on 3D mesh quality evaluation mostly focusing on recently proposed perceptual based metrics. We limit our study on greyscale static mesh evaluation and attempt to figure out the most workable method for real-Time evaluation by making a quantitative comparison. This paper also discusses in detail how to evaluate objective metric's performance with existing subjective databases. In this work, we likewise research the utilization of the psychometric function to expel non-linearity between subjective and objective values. Finally, we draw a comparison among some selected quality metrics and it shows that curvature tensor based quality metrics predicts consistent result in terms of correlation
No-Reference Quality Assessment for Colored Point Cloud and Mesh Based on Natural Scene Statistics
To improve the viewer's quality of experience and optimize processing systems
in computer graphics applications, the 3D quality assessment (3D-QA) has become
an important task in the multimedia area. Point cloud and mesh are the two most
widely used electronic representation formats of 3D models, the quality of
which is quite sensitive to operations like simplification and compression.
Therefore, many studies concerning point cloud quality assessment (PCQA) and
mesh quality assessment (MQA) have been carried out to measure the visual
quality degradations caused by lossy operations. However, a large part of
previous studies utilizes full-reference (FR) metrics, which means they may
fail to predict the accurate quality level of 3D models when the reference 3D
model is not available. Furthermore, limited numbers of 3D-QA metrics are
carried out to take color features into consideration, which significantly
restricts the effectiveness and scope of application. In many quality
assessment studies, natural scene statistics (NSS) have shown a good ability to
quantify the distortion of natural scenes to statistical parameters. Therefore,
we propose an NSS-based no-reference quality assessment metric for colored 3D
models. In this paper, quality-aware features are extracted from the aspects of
color and geometry directly from the 3D models. Then the statistic parameters
are estimated using different distribution models to describe the
characteristic of the 3D models. Our method is mainly validated on the colored
point cloud quality assessment database (SJTU-PCQA) and the colored mesh
quality assessment database (CMDM). The experimental results show that the
proposed method outperforms all the state-of-art NR 3D-QA metrics and obtains
an acceptable gap with the state-of-art FR 3D-QA metrics
Virtual simulation of the postsurgical cosmetic outcome in patients with pectus excavatum
Pectus excavatum is the most common congenital deformity of the anterior chest wall, in which several ribs and the sternum grow abnormally. Nowadays, the surgical correction is carried out in children and adults through Nuss technic. This technic has been shown to be safe with major drivers as cosmesis and the prevention of psychological problems and social stress. Nowadays, no application is known to predict the cosmetic outcome of the pectus excavatum surgical correction. Such tool could be used to help the surgeon and the patient in the moment of deciding the need for surgery correction. This work is a first step to predict postsurgical outcome in pectus excavatum surgery correction. Facing this goal, it was firstly determined a point cloud of the skin surface along the thoracic wall using Computed Tomography (before surgical correction) and the Polhemus FastSCAN (after the surgical correction). Then, a surface mesh was reconstructed from the two point clouds using a Radial Basis Function algorithm for further affine registration between the meshes. After registration, one studied the surgical correction influence area (SCIA) of the thoracic wall. This SCIA was used to train, test and validate artificial neural networks in order to predict the surgical outcome of pectus excavatum correction and to determine the degree of convergence of SCIA in different patients. Often, ANN did not converge to a satisfactory solution (each patient had its own deformity characteristics), thus invalidating the creation of a mathematical model capable of estimating, with satisfactory results, the postsurgical outcome.Fundação para a Ciência e a Tecnologia, Portugal (FCT) through the Postdoc grant referenced SFRH/BPD/46851/2008 and R&D project referenced PTDC/SAU-BEB/103368/2008
GMS-3DQA: Projection-based Grid Mini-patch Sampling for 3D Model Quality Assessment
Nowadays, most 3D model quality assessment (3DQA) methods have been aimed at
improving performance. However, little attention has been paid to the
computational cost and inference time required for practical applications.
Model-based 3DQA methods extract features directly from the 3D models, which
are characterized by their high degree of complexity. As a result, many
researchers are inclined towards utilizing projection-based 3DQA methods.
Nevertheless, previous projection-based 3DQA methods directly extract features
from multi-projections to ensure quality prediction accuracy, which calls for
more resource consumption and inevitably leads to inefficiency. Thus in this
paper, we address this challenge by proposing a no-reference (NR)
projection-based \textit{\underline{G}rid \underline{M}ini-patch
\underline{S}ampling \underline{3D} Model \underline{Q}uality
\underline{A}ssessment (GMS-3DQA)} method. The projection images are rendered
from six perpendicular viewpoints of the 3D model to cover sufficient quality
information. To reduce redundancy and inference resources, we propose a
multi-projection grid mini-patch sampling strategy (MP-GMS), which samples grid
mini-patches from the multi-projections and forms the sampled grid mini-patches
into one quality mini-patch map (QMM). The Swin-Transformer tiny backbone is
then used to extract quality-aware features from the QMMs. The experimental
results show that the proposed GMS-3DQA outperforms existing state-of-the-art
NR-3DQA methods on the point cloud quality assessment databases. The efficiency
analysis reveals that the proposed GMS-3DQA requires far less computational
resources and inference time than other 3DQA competitors. The code will be
available at https://github.com/zzc-1998/GMS-3DQA
Image database system for glaucoma diagnosis support
Tato práce popisuje přehled standardních a pokročilých metod používaných k diagnose glaukomu v ranném stádiu. Na základě teoretických poznatků je implementován internetově orientovaný informační systém pro oční lékaře, který má tři hlavní cíle. Prvním cílem je možnost sdílení osobních dat konkrétního pacienta bez nutnosti posílat tato data internetem. Druhým cílem je vytvořit účet pacienta založený na kompletním očním vyšetření. Posledním cílem je aplikovat algoritmus pro registraci intenzitního a barevného fundus obrazu a na jeho základě vytvořit internetově orientovanou tři-dimenzionální vizualizaci optického disku. Tato práce je součásti DAAD spolupráce mezi Ústavem Biomedicínského Inženýrství, Vysokého Učení Technického v Brně, Oční klinikou v Erlangenu a Ústavem Informačních Technologií, Friedrich-Alexander University, Erlangen-Nurnberg.This master thesis describes a conception of standard and advanced eye examination methods used for glaucoma diagnosis in its early stage. According to the theoretical knowledge, a web based information system for ophthalmologists with three main aims is implemented. The first aim is the possibility to share medical data of a concrete patient without sending his personal data through the Internet. The second aim is to create a patient account based on a complete eye examination procedure. The last aim is to improve the HRT diagnostic method with an image registration algorithm for the fundus and intensity images and create an optic nerve head web based 3D visualization. This master thesis is a part of project based on DAAD co-operation between Department of Biomedical Engineering, Brno University of Technology, Eye Clinic in Erlangen and Department of Computer Science, Friedrich-Alexander University, Erlangen-Nurnberg.
As-Built 3D Heritage City Modelling to Support Numerical Structural Analysis: Application to the Assessment of an Archaeological Remain
Terrestrial laser scanning is a widely used technology to digitise archaeological, architectural
and cultural heritage. This allows for modelling the assets’ real condition in comparison with
traditional data acquisition methods. This paper, based on the case study of the basilica in the Baelo
Claudia archaeological ensemble (Tarifa, Spain), justifies the need of accurate heritage modelling
against excessively simplified approaches in order to support structural safety analysis. To do this,
after validating the 3Dmeshing process frompoint cloud data, the semi-automatic digital reconstitution
of the basilica columns is performed. Next, a geometric analysis is conducted to calculate the structural
alterations of the columns. In order to determine the structural performance, focusing both on the
accuracy and suitability of the geometric models, static and modal analyses are carried out by means of
the finite element method (FEM) on three different models for the most unfavourable column in terms
of structural damage: (1) as-built (2) simplified and (3) ideal model without deformations. Finally,
the outcomes show that the as-built modelling enhances the conservation status analysis of the 3D
heritage city (in terms of realistic compliance factor values), although further automation still needs to
be implemented in the modelling process
An Image-based model for 3D shape quality measure
In light of increased research on 3D shapes and the increased processing capability of GPUs, there has been a significant
increase in available 3D applications. In many applications, assessment of perceptual quality of 3D shapes is required. Due
to the nature of 3D representation, this quality assessment may take various forms. While it is straightforward to measure
geometric distortions directly on the 3D shape geometry, such measures are often inconsistent with human perception of quality.
In most cases, human viewers tend to perceive 3D shapes from their 2D renderings. It is therefore plausible to measure shape
quality using their 2D renderings. In this paper, we present an image-based quality metric for evaluating 3D shape quality
given the original and distorted shapes. To provide a good coverage of 3D geometry from different views, we render each shape
from 12 equally spaced views, along with a variety of rendering styles to capture different aspects of visual characteristics.
Image-based metrics such as SSIM (Structure Similarity Index Measure) are then used to measure the quality of 3D shapes. Our
experiments show that by effectively selecting a suitable combination of rendering styles and building a neural network based
model, we achieve significantly better prediction for subjective perceptual quality than existing methods
Wize Mirror - a smart, multisensory cardio-metabolic risk monitoring system
In the recent years personal health monitoring systems have been gaining popularity, both as a result of the pull from the general population, keen to improve well-being and early detection of possibly serious health conditions and the push from the industry eager to translate the current significant progress in computer vision and machine learning into commercial products. One of such systems is the Wize Mirror, built as a result of the FP7 funded SEMEOTICONS (SEMEiotic Oriented Technology for Individuals CardiOmetabolic risk self-assessmeNt and Self-monitoring) project. The project aims to translate the semeiotic code of the human face into computational descriptors and measures, automatically extracted from videos, multispectral images, and 3D scans of the face. The multisensory platform, being developed as the result of that project, in the form of a smart mirror, looks for signs related to cardio-metabolic risks. The goal is to enable users to self-monitor their well-being status over time and improve their life-style via tailored user guidance. This paper is focused on the description of the part of that system, utilising computer vision and machine learning techniques to perform 3D morphological analysis of the face and recognition of psycho-somatic status both linked with cardio-metabolic risks. The paper describes the concepts, methods and the developed implementations as well as reports on the results obtained on both real and synthetic datasets
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