Fast 3D Indoor Scene Synthesis by Learning Spatial Relation Priors of Objects

We present a framework for fast synthesizing indoor scenes, given a room geometry and a list of objects with learnt priors.Unlike existing data-driven solutions, which often learn priors by co-occurrence analysis and statistical model fitting, our methodmeasures the strengths of spatial relations by tests for complete spatial randomness (CSR), and learns discrete priors based onsamples with the ability to accurately represent exact layout patterns. With the learnt priors, our method achieves both acceleration andplausibility by partitioning the input objects into disjoint groups, followed by layout optimization using position-based dynamics (PBD)based on the Hausdorff metric. Experiments show that our framework is capable of measuring more reasonable relations amongobjects and simultaneously generating varied arrangements in seconds compared with the state-of-the-art works.</p

Fast 3D Indoor Scene Synthesis by Learning Spatial Relation Priors of Objects

We present a framework for fast synthesizing indoor scenes, given a room geometry and a list of objects with learnt priors.Unlike existing data-driven solutions, which often learn priors by co-occurrence analysis and statistical model fitting, our methodmeasures the strengths of spatial relations by tests for complete spatial randomness (CSR), and learns discrete priors based onsamples with the ability to accurately represent exact layout patterns. With the learnt priors, our method achieves both acceleration andplausibility by partitioning the input objects into disjoint groups, followed by layout optimization using position-based dynamics (PBD)based on the Hausdorff metric. Experiments show that our framework is capable of measuring more reasonable relations amongobjects and simultaneously generating varied arrangements in seconds compared with the state-of-the-art works.</p

A Framework for the Automatic Analysis and Interactive Exploration of Document Aesthetics. Technical Report April 21, 2016

Modern word processing software and typesetting systems such as TeX enable the quick creation of documents of various kinds. Although the quality of the software packages varies, all can produce aesthetically pleasing documents in terms of layout and type setting. Problems typically originate from the large number of parameters which are exposed to the user. These range from simple settings like typeface, font size and column width to more elaborate ones, such as kerning and leading. Most often default values are modified without grasping the consequences for readability and aesthetic appeal of the resulting document.In this paper, we present a system for interactive visualization and exploration of quantifiable aspects of document aesthetics such as alignment, spacing, gray values, but also of image color harmony. This system also allows for comparative analysis of multiple documents and document versions side-by-side. The documents are rated using an extensible and parameterizable plug-in system allowing the user to define a task-specific processing pipeline interactively. The rating is hierarchically organized such that the user can drill down into the different aspects that influence the final score. Our system takes standard document formats such as Adobe PDF or Microsoft XPS as input. Our system serves as a platform for further research on document aesthetics as well as a utility to sensibilize authors for these often underestimated aspects of scientific publishing

Cuneiform Character Similarity Using Graph Representations

Motivated by the increased demand for computerized analysis of documents within the Digital Humanities we are developing algorithms for cuneiform tablets, which contain the oldest handwritten script used for more than three millennia. These tablets are typically found in the Middle East and contain a total amount of written words comparable to all documents in Latin or ancient Greek. In previous work we have shown how to extract vector drawings from 3D-models similar to those manually drawn over digital photographs. Both types of drawings share the Scalable Vector Graphic (SVG) format representing the cuneiform characters as splines. These splines are transformed into a graph representation and extend these by triangulation. Based on graph kernel methods we show a similarity metric for cuneiform characters, which have higher degrees of freedom than handwriting with ink on paper. An evaluation of the precision and recall of our proposed approach is shown and compared to well-known methods for processing handwriting. Finally a summary and an outlook are given

3D reconstruction for plastic surgery simulation based on statistical shape models

This thesis has been accomplished in Crisalix in collaboration with the Universitat Pompeu Fabra within the program of Doctorats Industrials. Crisalix has the mission of enhancing the communication between professionals of plastic surgery and patients by providing a solution to the most common question during the surgery planning process of ``How will I look after the surgery?''. The solution proposed by Crisalix is based in 3D imaging technology. This technology generates the 3D reconstruction that accurately represents the area of the patient that is going to be operated. This is followed by the possibility of creating multiple simulations of the plastic procedure, which results in the representation of the possible outcomes of the surgery. This thesis presents a framework capable to reconstruct 3D shapes of faces and breasts of plastic surgery patients from 2D images and 3D scans. The 3D reconstruction of an object is a challenging problem with many inherent ambiguities. Statistical model based methods are a powerful approach to overcome some of these ambiguities. We follow the intuition of maximizing the use of available prior information by introducing it into statistical model based methods to enhance their properties. First, we explore Active Shape Models (ASM) which are a well known method to perform 2D shapes alignment. However, it is challenging to maintain prior information (e.g. small set of given landmarks) unchanged once the statistical model constraints are applied. We propose a new weighted regularized projection into the parameter space which allows us to obtain shapes that at the same time fulfill the imposed shape constraints and are plausible according to the statistical model. Second, we extend this methodology to be applied to 3D Morphable Models (3DMM), which are a widespread method to perform 3D reconstruction. However, existing methods present some limitations. Some of them are based in non-linear optimizations computationally expensive that can get stuck in local minima. Another limitation is that not all the methods provide enough resolution to represent accurately the anatomy details needed for this application. Given the medical use of the application, the accuracy and robustness of the method, are important factors to take into consideration. We show how 3DMM initialization and 3DMM fitting can be improved using our weighted regularized projection. Finally, we present a framework capable to reconstruct 3D shapes of plastic surgery patients from two possible inputs: 2D images and 3D scans. Our method is used in different stages of the 3D reconstruction pipeline: shape alignment; 3DMM initialization and 3DMM fitting. The developed methods have been integrated in the production environment of Crisalix, proving their validity.Aquesta tesi ha estat realitzada a Crisalix amb la col·laboració de la Universitat Pompeu Fabra sota el pla de Doctorats Industrials. Crisalix té com a objectiu la millora de la comunicació entre els professionals de la cirurgia plàstica i els pacients, proporcionant una solució a la pregunta que sorgeix més freqüentment durant el procés de planificació d'una operació quirúrgica ``Com em veuré després de la cirurgia?''. La solució proposada per Crisalix està basada en la tecnologia d'imatge 3D. Aquesta tecnologia genera la reconstrucció 3D de la zona del pacient operada, seguit de la possibilitat de crear múltiples simulacions obtenint la representació dels possibles resultats de la cirurgia. Aquesta tesi presenta un sistema capaç de reconstruir cares i pits de pacients de cirurgia plàstica a partir de fotos 2D i escanegis. La reconstrucció en 3D d'un objecte és un problema complicat degut a la presència d'ambigüitats. Els mètodes basats en models estadístics son adequats per mitigar-les. En aquest treball, hem seguit la intuïció de maximitzar l'ús d'informació prèvia, introduint-la al model estadístic per millorar les seves propietats. En primer lloc, explorem els Active Shape Models (ASM) que són un conegut mètode fet servir per alinear contorns d'objectes 2D. No obstant, un cop aplicades les correccions de forma del model estadístic, es difícil de mantenir informació de la que es disposava a priori (per exemple, un petit conjunt de punts donat) inalterada. Proposem una nova projecció ponderada amb un terme de regularització, que permet obtenir formes que compleixen les restriccions de forma imposades i alhora són plausibles en concordança amb el model estadístic. En segon lloc, ampliem la metodologia per aplicar-la als anomenats 3D Morphable Models (3DMM) que són un mètode extensivament utilitzat per fer reconstrucció 3D. No obstant, els mètodes de 3DMM existents presenten algunes limitacions. Alguns estan basats en optimitzacions no lineals, computacionalment costoses i que poden quedar atrapades en mínims locals. Una altra limitació, és que no tots el mètodes proporcionen la resolució adequada per representar amb precisió els detalls de l'anatomia. Donat l'ús mèdic de l'aplicació, la precisió i la robustesa són factors molt importants a tenir en compte. Mostrem com la inicialització i l'ajustament de 3DMM poden ser millorats fent servir la projecció ponderada amb regularització proposada. Finalment, es presenta un sistema capaç de reconstruir models 3D de pacients de cirurgia plàstica a partir de dos possibles tipus de dades: imatges 2D i escaneigs en 3D. El nostre mètode es fa servir en diverses etapes del procés de reconstrucció: alineament de formes en imatge, la inicialització i l'ajustament de 3DMM. Els mètodes desenvolupats han estat integrats a l'entorn de producció de Crisalix provant la seva validesa

Satisfaction with Customizable 3D-Printed Finger Orthoses Compared to Commercial SilverRing™ Splints

Background: Emerging research primarily supports 3D-printing as a customizable, replicable orthosis option. However, more research emphasizing orthotic users’ viewpoints is necessary to address challenges with orthotic wear adherence and satisfaction. Method: Forty persons were recruited at an academic medical center. After wearing each orthosis for 8 hr (or as long as tolerated), the participants completed post-satisfaction surveys to measure satisfaction with different aspects of both orthoses worn. Results: Forty participants (21 females, 19 males, mean age = 24.98 years) were enrolled in the study. Satisfaction scores (N = 40) were not statistically significant for 3D-printed orthoses compared to SilverRing™ Splints across all domains except for Affordability, which was rated significantly higher for 3D-printed orthoses (M = 10.00, SD = 0.000) compared to SilverRing™ Splints (M = 5.28, SD = 2.35), t(39) = 12.70, p \u3c .001. The mean difference in satisfaction scores was 4.72, with a 95% confidence interval ranging from 3.97 to 5.48. Conclusion: Findings provide novel evidence supporting the use of this customizable 3D-printed prototype as a cost-effective, alternative option to established commercial finger orthoses. This study has potential to assist clinicians’ decision-making as they navigate best orthoses options for individuals with swan-neck deformities

Affective graphs: the visual appeal of linked data

The essence and value of Linked Data lies in the ability of humans and machines to query, access and reason upon highly structured and formalised data. Ontology structures provide an unambiguous description of the structure and content of data. While a multitude of software applications and visualization systems have been developed over the past years for Linked Data, there is still a significant gap that exists between applications that consume Linked Data and interfaces that have been designed with significant focus on aesthetics. Though the importance of aesthetics in affecting the usability, effectiveness and acceptability of user interfaces have long been recognised, little or no explicit attention has been paid to the aesthetics of Linked Data applications. In this paper, we introduce a formalised approach to developing aesthetically pleasing semantic web interfaces by following aesthetic principles and guidelines identified from literature. We apply such principles to design and develop a generic approach of using visualizations to support exploration of Linked Data, in an interface that is pleasing to users. This provides users with means to browse ontology structures, enriched with statistics of the underlying data, facilitating exploratory activities and enabling visual query for highly precise information needs. We evaluated our approach in three ways: an initial objective evaluation comparing our approach with other well-known interfaces for the semantic web and two user evaluations with semantic web researchers

Image-Driven Automated End-to-End Testing for Mobile Applications

The increasing complexity and demand of software systems and the greater availability of test automation software is quickly rendering manual end-to-end (E2E) testing techniques for mobile platforms obsolete. This research seeks to explore the potential increase in automated test efficacy and maintainability through the use of computer vision algorithms when applied with Appium, a leading cross-platform mobile test automation framework. A testing framework written in a Node.js environment was created to support the development of E2E test scripts that examine and report the functional capabilities of a mobile test app. The test framework provides a suite of functions that connect with an Appium server and provide interaction with the mobile test app to perform actions and assertions like clicking and verifying text. To do this without modifying the test app source code, the system employs image templates representing specific app components and identifies them within the test app by utilizing feature detection, matching, and filtering. From experimentation on three test scripts across multiple iOS and Android device simulators, iOS test script runs had a pass rate of 38% on average, while Android test runs had a pass rate of 74.5% on average. The test scripts ran perfectly only on the device simulators from which the template images were extracted via screenshots, while failures were mostly due to invalid or mismatched templates. Therefore, more generic templates that appeal to a variety of different device renderings are necessary for the test framework to be completely reliable

Quantifying aesthetics of visual design applied to automatic design

In today\u27s Instagram world, with advances in ubiquitous computing and access to social networks, digital media is adopted by art and culture. In this dissertation, we study what makes a good design by investigating mechanisms to bring aesthetics of design from realm of subjection to objection. These mechanisms are a combination of three main approaches: learning theories and principles of design by collaborating with professional designers, mathematically and statistically modeling good designs from large scale datasets, and crowdscourcing to model perceived aesthetics of designs from general public responses. We then apply the knowledge gained in automatic design creation tools to help non-designers in self-publishing, and designers in inspiration and creativity. Arguably, unlike visual arts where the main goals may be abstract, visual design is conceptualized and created to convey a message and communicate with audiences. Therefore, we develop a semantic design mining framework to automatically link the design elements, layout, color, typography, and photos to linguistic concepts. The inferred semantics are applied to a design expert system to leverage user interactions in order to create personalized designs via recommendation algorithms based on the user\u27s preferences