SketchyDynamics apoio à produção de sistemas baseados em interfaces caligráficas para a simulação da dinâmica de corpos rígidos

Mestrado em Engenharia Informática - Área de Especialização em Sistemas Gráficos e MultimédiaO paradigma de interação proporcionado pelas interfaces caligráficas constitui uma forma natural de interação humano-computador. Esta naturalidade deve-se, sobretudo, à semelhança que este estilo de interação possui com a utilização de um lápis sobre papel, tarefa comum e intuitiva. Apesar disso é ainda pouco frequente o emprego de tais interfaces em aplicações informáticas, sendo o estilo de interação WIMP (Windows, Icons, Menus and Pointers) mais utilizado e favorecido. No entanto, antecipa-se um futuro no qual as interfaces caligráficas estarão cada vez mais presentes, pois é notório o surgimento de um número crescente não só de aplicações que adotam este estilo de interação, mas também de equipamentos que incentivam à sua utilização. Com base nesta premissa, é seguro afirmar a necessidade de investir nesta área, de modo a agilizar e acelerar a adoção do estilo de interação caligráfico e, assim, tornar a interação humano-computador num processo cada vez mais natural. O trabalho descrito neste documento visa um estudo à utilização das interfaces caligráficas orientada para a criação e controlo de um ambiente simulado. Mais concretamente, é apresentado o sistema SketchyDynamics, que integra um módulo de simulação da dinâmica de corpos rígidos em simbiose com uma interface caligráfica munida das ações necessárias para a manipulação da simulação. Recorrendo a este sistema, é facilitada a produção de aplicações que tirem partido destas funcionalidades, sem a necessidade de as reimplementar. É ainda descrita uma avaliação de técnicas de reconhecimento caligráfico realizada com o objetivo de determinar aquela que melhor se integraria no sistema desenvolvido. No âmbito desta avaliação são ainda apresentados alguns pormenores sobre a implementação dessas técnicas, bem como procedimentos que permitem uma maximização da sua eficácia. São também discutidos os resultados de uma avaliação de usabilidade conduzida com o propósito de validar o sistema SketchyDynamics do ponto de vista do utilizador. Os resultados desta avaliação mostram que este sistema foi bem-sucedido e que se encontra preparado para o utilizador final, não obstante a existência de margem para futuras melhorias.The interaction paradigm provided by sketch-based interfaces represents a natural method of human-computer interaction. This naturalness is largely due to the similarity that this interaction style has with the use of a pencil on a paper, an intuitive and common task. Despite that, the implementation of these interfaces on computer applications is still unusual, in favor of the WIMP (Windows, Icons, Menus and Points) interaction style. Nevertheless, we can predict a future where sketch-based interfaces will be increasingly more widespread, based on the recent emergence of not only applications that adopt this interaction style, but also equipment that encourage their use. With this premise in mind, it is safe to assert the need for investment in this area, in order to streamline and accelerate the adoption of the sketch-based interaction style and thus make the human-computer interaction a progressively more natural process. The work described in this document aims the study of the use of sketch-based interfaces in the creation and control of simulated environments. More specifically, we present the SketchyDynamics system, which incorporates a rigid body simulation module in symbiosis with a sketch-based interface provided with the necessary actions for the manipulation of the simulation. Using this system, we hope to ease the production of applications that take advantage of these features, without the need to implement them from scratch. An evaluation of various sketch recognition techniques, performed in order to find the one that best fits in the developed system, is also described. As part of this evaluation, we also present some details on the implementation of these techniques, as well as procedures that allow us to maximize their efficiency. Furthermore, we discuss the results of a usability evaluation that was conducted with the purpose of validating the SketchyDynamics system from the user’s point of view. The results of this evaluation suggest that, despite the existence of room for further improvements, the system was successful and is ready for final users

Geometristen muotojen reaaliaikainen tunnistus

Kynä- ja kosketuskäyttöliittymät vaativat toimiakseen tehokasta ja tarkkaa hahmontunnistusta. Tässä työssä esitellään reaaliaikaisen hahmontunnistuksen käsitteistöä, yleisiä menetelmiä ja aikaisempaa tutkimusta. Lyhyesti käsitellään eri tutkimusryhmien esittämiä hahmontunnistusjärjestelmiä. Lisäksi esitellään geometrisiin piirteisiin perustuva hahmontunnistusjärjestelmä. Työ antaa yksityiskohtaiset kuvaukset piirtoviivan esiprosessointi- ja piirteenirrotusalgoritmeista sekä hahmoluokittelumenetelmästä. Lisäksi kuvaillaan hahmontunnistusheuristiikka kahdelle yksinkertaiselle muodolle (nuoli ja tähti). Joukko koehenkilöitä käytti työssä toteutettua graa_sta käyttöliittymää, minkä tuloksena saatiin realistiset tulokset järjestelmän laskennallisesta suorituskyvystä ja tarkkuudesta: toteutettu järjestelmä on laskennallisesti nopea mutta tunnistustarkkuus monitulkintainen. Lopuksi pohditaan valitun lähestymistavan ongelmia ja rajoitteita.Effective sketch recognition is the basis for pen and touch-based human-computer interfaces. In this thesis the concepts, common methods and earlier work in the research area of online symbol recognition are presented. A set of shape recognition approaches proposed in the past by various research teams are briefly introduced. An online shape recognizer using global geometric features is described. The preprocessing and feature extraction algorithms as well as the shape classification method are described in detail. Recognition heuristics for two simple shapes (arrow and star) are suggested. A graphical user interface was implemented and a group of subjects employed to obtain realistic results of the computational performance and recognition accuracy of the system: the implemented system performs fast but the results on the recognition accuracy were ambiguous. Finally, the problems and restrictions of the approach are discussed

Sketch interpretation using multiscale stochastic models of temporal patterns

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.Includes bibliographical references (p. 102-114).Sketching is a natural mode of interaction used in a variety of settings. For example, people sketch during early design and brainstorming sessions to guide the thought process; when we communicate certain ideas, we use sketching as an additional modality to convey ideas that can not be put in words. The emergence of hardware such as PDAs and Tablet PCs has enabled capturing freehand sketches, enabling the routine use of sketching as an additional human-computer interaction modality. But despite the availability of pen based information capture hardware, relatively little effort has been put into developing software capable of understanding and reasoning about sketches. To date, most approaches to sketch recognition have treated sketches as images (i.e., static finished products) and have applied vision algorithms for recognition. However, unlike images, sketches are produced incrementally and interactively, one stroke at a time and their processing should take advantage of this. This thesis explores ways of doing sketch recognition by extracting as much information as possible from temporal patterns that appear during sketching.(cont.) We present a sketch recognition framework based on hierarchical statistical models of temporal patterns. We show that in certain domains, stroke orderings used in the course of drawing individual objects contain temporal patterns that can aid recognition. We build on this work to show how sketch recognition systems can use knowledge of both common stroke orderings and common object orderings. We describe a statistical framework based on Dynamic Bayesian Networks that can learn temporal models of object-level and stroke-level patterns for recognition. Our framework supports multi-object strokes, multi-stroke objects, and allows interspersed drawing of objects - relaxing the assumption that objects are drawn one at a time. Our system also supports real-valued feature representations using a numerically stable recognition algorithm. We present recognition results for hand-drawn electronic circuit diagrams. The results show that modeling temporal patterns at multiple scales provides a significant increase in correct recognition rates, with no added computational penalties.by Tevfik Metin Sezgin.Ph.D

A Fine Motor Skill Classifying Framework to Support Children's Self-Regulation Skills and School Readiness

Children’s self-regulation skills predict their school-readiness and social behaviors, and assessing these skills enables parents and teachers to target areas for improvement or prepare children to enter school ready to learn and achieve. Assessing these skills enables parents and teachers to target areas for improvement or prepare children to enter school ready to learn and achieve. To assess children’s fine motor skills, current educators are assessing those skills by either determining their shape drawing correctness or measuring their drawing time durations through paper-based assessments. However, the methods involve human experts manually assessing children’s fine motor skills, which are time consuming and prone to human error and bias. As there are many children that use sketch-based applications on mobile and tablet devices, computer-based fine motor skill assessment has high potential to solve the limitations of the paper-based assessments. Furthermore, sketch recognition technology is able to offer more detailed, accurate, and immediate drawing skill information than the paper-based assessments such as drawing time or curvature difference. While a number of educational sketch applications exist for teaching children how to sketch, they are lacking the ability to assess children’s fine motor skills and have not proved the validity of the traditional methods onto tablet-environments. We introduce our fine motor skill classifying framework based on children’s digital drawings on tablet-computers. The framework contains two fine motor skill classifiers and a sketch-based educational interface (EasySketch). The fine motor skill classifiers contain: (1) KimCHI: the classifier that determines children’s fine motor skills based on their overall drawing skills and (2) KimCHI2: the classifier that determines children’s fine motor skills based on their curvature- and corner-drawing skills. Our fine motor skill classifiers determine children’s fine motor skills by generating 131 sketch features, which can analyze their drawing ability (e.g. DCR sketch feature can determine their curvature-drawing skills). We first implemented the KimCHI classifier, which can determine children’s fine motor skills based on their overall drawing skills. From our evaluation with 10- fold cross-validation, we found that the classifier can determine children’s fine motor skills with an f-measure of 0.904. After that, we implemented the KimCHI2 classifier, which can determine children’s fine motor skills based on their curvature- and corner-drawing skills. From our evaluation with 10-fold cross-validation, we found that the classifier can determine children’s curvature-drawing skills with an f-measure of 0.82 and corner-drawing skills with an f-measure of 0.78. The KimCHI2 classifier outperformed the KimCHI classifier during the fine motor skill evaluation. EasySketch is a sketch-based educational interface that (1) determines children’s fine motor skills based on their drawing skills and (2) assists children how to draw basic shapes such as alphabet letters or numbers based on their learning progress. When we evaluated our interface with children, our interface determined children’s fine motor skills more accurately than the conventional methodology by f-measures of 0.907 and 0.744, accordingly. Furthermore, children improved their drawing skills from our pedagogical feedback. Finally, we introduce our findings that sketch features (DCR and Polyline Test) can explain children’s fine motor skill developmental stages. From the sketch feature distributions per each age group, we found that from age 5 years, they show notable fine motor skill development

Rethinking Pen Input Interaction: Enabling Freehand Sketching Through Improved Primitive Recognition

Online sketch recognition uses machine learning and artificial intelligence techniques to interpret markings made by users via an electronic stylus or pen. The goal of sketch recognition is to understand the intention and meaning of a particular user's drawing. Diagramming applications have been the primary beneficiaries of sketch recognition technology, as it is commonplace for the users of these tools to rst create a rough sketch of a diagram on paper before translating it into a machine understandable model, using computer-aided design tools, which can then be used to perform simulations or other meaningful tasks. Traditional methods for performing sketch recognition can be broken down into three distinct categories: appearance-based, gesture-based, and geometric-based. Although each approach has its advantages and disadvantages, geometric-based methods have proven to be the most generalizable for multi-domain recognition. Tools, such as the LADDER symbol description language, have shown to be capable of recognizing sketches from over 30 different domains using generalizable, geometric techniques. The LADDER system is limited, however, in the fact that it uses a low-level recognizer that supports only a few primitive shapes, the building blocks for describing higher-level symbols. Systems which support a larger number of primitive shapes have been shown to have questionable accuracies as the number of primitives increase, or they place constraints on how users must input shapes (e.g. circles can only be drawn in a clockwise motion; rectangles must be drawn starting at the top-left corner). This dissertation allows for a significant growth in the possibility of free-sketch recognition systems, those which place little to no drawing constraints on users. In this dissertation, we describe multiple techniques to recognize upwards of 18 primitive shapes while maintaining high accuracy. We also provide methods for producing confidence values and generating multiple interpretations, and explore the difficulties of recognizing multi-stroke primitives. In addition, we show the need for a standardized data repository for sketch recognition algorithm testing and propose SOUSA (sketch-based online user study application), our online system for performing and sharing user study sketch data. Finally, we will show how the principles we have learned through our work extend to other domains, including activity recognition using trained hand posture cues

Combining representations for improved sketch recognition

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 89-96).Sketching is a common means of conveying, representing, and preserving information, and it has become a subject of research as a method for human-computer interaction, specifically in the area of computer-aided design. Digitally collected sketches contain both spatial and temporal information; additionally, they may contain a conceptual structure of shapes and sub shapes. These multiple aspects suggest several ways of representing sketches, each with advantages and disadvantages for recognition. Most existing sketch recognitions systems are based on a single representation and do not use all available information. We propose combining several representations and systems as a way to improve recognition accuracy. This thesis presents two methods for combining recognition systems. The first improves recognition by improving segmentation, while the second seeks to predict how well systems will recognize a given domain or symbol and combine their outputs accordingly. We show that combining several recognition systems based on different representations can improve the accuracy of existing recognition methods.by Sonya J. Cates.Ph.D

Pen-based Methods For Recognition and Animation of Handwritten Physics Solutions

There has been considerable interest in constructing pen-based intelligent tutoring systems due to the natural interaction metaphor and low cognitive load afforded by pen-based interaction. We believe that pen-based intelligent tutoring systems can be further enhanced by integrating animation techniques. In this work, we explore methods for recognizing and animating sketched physics diagrams. Our methodologies enable an Intelligent Tutoring System (ITS) to understand the scenario and requirements posed by a given problem statement and to couple this knowledge with a computational model of the student\u27s handwritten solution. These pieces of information are used to construct meaningful animations and feedback mechanisms that can highlight errors in student solutions. We have constructed a prototype ITS that can recognize mathematics and diagrams in a handwritten solution and infer implicit relationships among diagram elements, mathematics and annotations such as arrows and dotted lines. We use natural language processing to identify the domain of a given problem, and use this information to select one or more of four domain-specific physics simulators to animate the user\u27s sketched diagram. We enable students to use their answers to guide animation behavior and also describe a novel algorithm for checking recognized student solutions. We provide examples of scenarios that can be modeled using our prototype system and discuss the strengths and weaknesses of our current prototype. Additionally, we present the findings of a user study that aimed to identify animation requirements for physics tutoring systems. We describe a taxonomy for categorizing different types of animations for physics problems and highlight how the taxonomy can be used to define requirements for 50 physics problems chosen from a university textbook. We also present a discussion of 56 handwritten solutions acquired from physics students and describe how suitable animations could be constructed for each of them

Sketch recognition of digital ink diagrams : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science at Massey University, Palmerston North, New Zealand

Figures are either re-used with permission, or abstracted with permission from the source article.Sketch recognition of digital ink diagrams is the process of automatically identifying hand-drawn elements in a diagram. This research focuses on the simultaneous grouping and recognition of shapes in digital ink diagrams. In order to recognise a shape, we need to group strokes belonging to a shape, however, strokes cannot be grouped until the shape is identified. Therefore, we treat grouping and recognition as a simultaneous task. Our grouping technique uses spatial proximity to hypothesise shape candidates. Many of the hypothesised shape candidates are invalid, therefore we need a way to reject them. We present a novel rejection technique based on novelty detection. The rejection method uses proximity measures to validate a shape candidate. In addition, we investigate on improving the accuracy of the current shape recogniser by adding extra features. We also present a novel connector recognition system that localises connector heads around recognised shapes. We perform a full comparative study on two datasets. The results show that our approach is significantly more accurate in finding shapes and faster on process diagram compared to Stahovich et al. (2014), which the results show the superiority of our approach in terms of computation time and accuracy. Furthermore, we evaluate our system on two public datasets and compare our results with other approaches reported in the literature that have used these dataset. The results show that our approach is more accurate in finding and recognising the shapes in the FC dataset (by finding and recognising 91.7% of the shapes) compared to the reported results in the literature

Envisioning sketch recognition : a local feature based approach to recognizing informal sketches

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 89-94).Hand drawn sketches are an important part of the early design process and are an important aspect of creative design. They are used in many fields including electrical engineering, software engineering and web design. Recognizing shapes in these sketches is a challenging task due to the imprecision with which they are drawn. We tackle this challenge with a visual approach to recognition. The approach is based on a representation of a sketched shape in terms of the visual parts it is made of. By taking this part-based visual approach we are able to recognize shapes that are extremely difficult to recognize with current sketch recognition systems that focus on the individual strokes.by Michael Oltmans.Ph.D