Deep Learning for Free-Hand Sketch: A Survey

Free-hand sketches are highly illustrative, and have been widely used by humans to depict objects or stories from ancient times to the present. The recent prevalence of touchscreen devices has made sketch creation a much easier task than ever and consequently made sketch-oriented applications increasingly popular. The progress of deep learning has immensely benefited free-hand sketch research and applications. This paper presents a comprehensive survey of the deep learning techniques oriented at free-hand sketch data, and the applications that they enable. The main contents of this survey include: (i) A discussion of the intrinsic traits and unique challenges of free-hand sketch, to highlight the essential differences between sketch data and other data modalities, e.g., natural photos. (ii) A review of the developments of free-hand sketch research in the deep learning era, by surveying existing datasets, research topics, and the state-of-the-art methods through a detailed taxonomy and experimental evaluation. (iii) Promotion of future work via a discussion of bottlenecks, open problems, and potential research directions for the community.Comment: This paper is accepted by IEEE TPAM

Human interaction with digital ink : legibility measurement and structural analysis

Literature suggests that it is possible to design and implement pen-based computer interfaces that resemble the use of pen and paper. These interfaces appear to allow users freedom in expressing ideas and seem to be familiar and easy to use. Different ideas have been put forward concerning this type of interface, however despite the commonality of aims and problems faced, there does not appear to be a common approach to their design and implementation. This thesis aims to progress the development of pen-based computer interfaces that resemble the use of pen and paper. To do this, a conceptual model is proposed for interfaces that enable interaction with "digital ink". This conceptual model is used to organize and analyse the broad range of literature related to pen-based interfaces, and to identify topics that are not sufficiently addressed by published research. Two issues highlighted by the model: digital ink legibility and digital ink structuring, are then investigated. In the first investigation, methods are devised to objectively and subjectively measure the legibility of handwritten script. These methods are then piloted in experiments that vary the horizontal rendering resolution of handwritten script displayed on a computer screen. Script legibility is shown to decrease with rendering resolution, after it drops below a threshold value. In the second investigation, the clustering of digital ink strokes into words is addressed. A method of rating the accuracy of clustering algorithms is proposed: the percentage of words spoiled. The clustering error rate is found to vary among different writers, for a clustering algorithm using the geometric features of both ink strokes, and the gaps between them. The work contributes a conceptual interface model, methods of measuring digital ink legibility, and techniques for investigating stroke clustering features, to the field of digital ink interaction research

Recognition by directed attention to recursively partitioned images

A learning/recognition model (and instantiating program) is described which recursively combines the learning paradigms of conceptual clustering (Michalski, 1980) and learning-from-examples to resolve the ambiguities of real-world recognition. The model is based on neuropsychological and psychological evidence that the visual system is analytic, hierarchical, and composed of a parallel/serial dichotomy (many, see conclusions by Crick, 1984). Emulating the experimental evidence, parallel processes in the model decompose the image into components and cluster the constituents in much the same way as the image processing technique known as moment analysis (Alt, 1962). Serial, attentive mechanisms then reassemble the decompositions by investigating spatial relationships between components. The use of attentive mechanisms extends the moment analysis technique to handle alterations in structure and solves the contention problem created by combining the two learning paradigms. The contention results from a disagreement between the teacher and the model on what constitutes the salient features at the highest level of the symbol. There are four cases ZBT must handle, two of which result from the disagreement with the teacher. The parallel/serial dichotomy represents a vertical/horizontal tradeoff between the invariant and variant features of a domain. The resultant learned hierarchy allows ZBT to recognize structural differences while avoiding problems of exponential growth

Perceptually-based language to simplify sketch recognition user interface development

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. 473-495).Diagrammatic sketching is a natural modality of human-computer interaction that can be used for a variety of tasks, for example, conceptual design. Sketch recognition systems are currently being developed for many domains. However, they require signal-processing expertise if they are to handle the intricacies of each domain, and they are time-consuming to build. Our goal is to enable user interface designers and domain experts who may not have expertise in sketch recognition to be able to build these sketch systems. We created and implemented a new framework (FLUID - f acilitating user interface development) in which developers can specify a domain description indicating how domain shapes are to be recognized, displayed, and edited. This description is then automatically transformed into a sketch recognition user interface for that domain. LADDER, a language using a perceptual vocabulary based on Gestalt principles, was developed to describe how to recognize, display, and edit domain shapes. A translator and a customizable recognition system (GUILD - a generator of user interfaces using ladder descriptions) are combined with a domain description to automatically create a domain specific recognition system.(cont.) With this new technology, by writing a domain description, developers are able to create a new sketch interface for a domain, greatly reducing the time and expertise for the task Continuing in pursuit of our goal to facilitate UI development, we noted that 1) human generated descriptions contained syntactic and conceptual errors, and that 2) it is more natural for a user to specify a shape by drawing it than by editing text. However, computer generated descriptions from a single drawn example are also flawed, as one cannot express all allowable variations in a single example. In response, we created a modification of the traditional model of active learning in which the system selectively generates its own near-miss examples and uses the human teacher as a source of labels. System generated near-misses offer a number of advantages. Human generated examples are tedious to create and may not expose problems in the current concept. It seems most effective for the near-miss examples to be generated by whichever learning participant (teacher or student) knows better where the deficiencies lie; this will allow the concepts to be more quickly and effectively refined.(cont.) When working in a closed domain such as this one, the computer learner knows exactly which conceptual uncertainties remain, and which hypotheses need to be tested and confirmed. The system uses these labeled examples to automatically build a LADDER shape description, using a modification of the version spaces algorithm that handles interrelated constraints, and which also has the ability to learn negative and disjunctive constraints.by Tracy Anne Hammond.Ph.D

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

Application of Machine Learning within Visual Content Production

We are living in an era where digital content is being produced at a dazzling pace. The heterogeneity of contents and contexts is so varied that a numerous amount of applications have been created to respond to people and market demands. The visual content production pipeline is the generalisation of the process that allows a content editor to create and evaluate their product, such as a video, an image, a 3D model, etc. Such data is then displayed on one or more devices such as TVs, PC monitors, virtual reality head-mounted displays, tablets, mobiles, or even smartwatches. Content creation can be simple as clicking a button to film a video and then share it into a social network, or complex as managing a dense user interface full of parameters by using keyboard and mouse to generate a realistic 3D model for a VR game. In this second example, such sophistication results in a steep learning curve for beginner-level users. In contrast, expert users regularly need to refine their skills via expensive lessons, time-consuming tutorials, or experience. Thus, user interaction plays an essential role in the diffusion of content creation software, primarily when it is targeted to untrained people. In particular, with the fast spread of virtual reality devices into the consumer market, new opportunities for designing reliable and intuitive interfaces have been created. Such new interactions need to take a step beyond the point and click interaction typical of the 2D desktop environment. The interactions need to be smart, intuitive and reliable, to interpret 3D gestures and therefore, more accurate algorithms are needed to recognise patterns. In recent years, machine learning and in particular deep learning have achieved outstanding results in many branches of computer science, such as computer graphics and human-computer interface, outperforming algorithms that were considered state of the art, however, there are only fleeting efforts to translate this into virtual reality. In this thesis, we seek to apply and take advantage of deep learning models to two different content production pipeline areas embracing the following subjects of interest: advanced methods for user interaction and visual quality assessment. First, we focus on 3D sketching to retrieve models from an extensive database of complex geometries and textures, while the user is immersed in a virtual environment. We explore both 2D and 3D strokes as tools for model retrieval in VR. Therefore, we implement a novel system for improving accuracy in searching for a 3D model. We contribute an efficient method to describe models through 3D sketch via an iterative descriptor generation, focusing both on accuracy and user experience. To evaluate it, we design a user study to compare different interactions for sketch generation. Second, we explore the combination of sketch input and vocal description to correct and fine-tune the search for 3D models in a database containing fine-grained variation. We analyse sketch and speech queries, identifying a way to incorporate both of them into our system's interaction loop. Third, in the context of the visual content production pipeline, we present a detailed study of visual metrics. We propose a novel method for detecting rendering-based artefacts in images. It exploits analogous deep learning algorithms used when extracting features from sketches

Methods for Structural Pattern Recognition: Complexity and Applications

Katedra kybernetik

AutoGraff: towards a computational understanding of graffiti writing and related art forms.

The aim of this thesis is to develop a system that generates letters and pictures with a style that is immediately recognizable as graffiti art or calligraphy. The proposed system can be used similarly to, and in tight integration with, conventional computer-aided geometric design tools and can be used to generate synthetic graffiti content for urban environments in games and in movies, and to guide robotic or fabrication systems that can materialise the output of the system with physical drawing media. The thesis is divided into two main parts. The first part describes a set of stroke primitives, building blocks that can be combined to generate different designs that resemble graffiti or calligraphy. These primitives mimic the process typically used to design graffiti letters and exploit well known principles of motor control to model the way in which an artist moves when incrementally tracing stylised letter forms. The second part demonstrates how these stroke primitives can be automatically recovered from input geometry defined in vector form, such as the digitised traces of writing made by a user, or the glyph outlines in a font. This procedure converts the input geometry into a seed that can be transformed into a variety of calligraphic and graffiti stylisations, which depend on parametric variations of the strokes