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49
Hierarchical Parsing and Recognition of Hand-Sketched Diagrams
- In Proceedings of UIST’04
, 2004
"... A long standing challenge in pen-based computer interaction is the ability to make sense of informal sketches. A main difficulty lies in reliably extracting and recognizing the intended set of visual objects from a continuous stream of pen strokes. Existing pen-based systems either avoid these issue ..."
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Cited by 48 (4 self)
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A long standing challenge in pen-based computer interaction is the ability to make sense of informal sketches. A main difficulty lies in reliably extracting and recognizing the intended set of visual objects from a continuous stream of pen strokes. Existing pen-based systems either avoid these issues altogether, thus resulting in the equivalent of a drawing program, or rely on algorithms that are too constraining to be effective for the average user. As one step toward alleviating these difficulties, we present an integrated sketch parsing and recognition approach designed to enable natural, fluid sketch-based computer interaction. Our approach is based on a hierarchical mark-group-recognize architecture. First, the stream of pen strokes is examined to identify certain delimiter patterns called "markers." These markers then anchor a spatial analysis which groups the uninterpreted strokes into distinct clusters, each representing a single visual object. Finally, a trainable shape recognizer, which is informed by the spatial analysis, is used to find the best interpretations of the clusters. Based on these concepts, we have built SimuSketch, a sketch-based interface for Matlab's Simulink software package. An evaluation of SimuSketch has indicated that even novice users can effectively utilize our system to solve real engineering problems without having to know much about the underlying recognition techniques.
An Image-Based Trainable Symbol Recognizer for Sketch-Based Interfaces
- in AAAI Fall Symposium Series 2004: Making Pen-Based Interaction Intelligent and Natural
, 2004
"... We describe a trainable, hand-drawn symbol recognizer based on a multi-layer recognition scheme. Symbols are internally represented as binary templates. An ensemble of four template classifiers ranks each definition according to similarity with an unknown symbol. Scores from the individual classifie ..."
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Cited by 24 (1 self)
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We describe a trainable, hand-drawn symbol recognizer based on a multi-layer recognition scheme. Symbols are internally represented as binary templates. An ensemble of four template classifiers ranks each definition according to similarity with an unknown symbol. Scores from the individual classifiers are then aggregated to determine the best definition for the unknown. Ordinarily, template-matching is sensitive to rotation, and existing solutions for rotation invariance are too expensive for interactive use. We have developed an efficient technique for achieving rotation invariance based on polar coordinates. This techniques also filters out the bulk of unlikely definitions, thereby simplifying the task of the multiclassifier recognition step.
A Visual Approach to Sketched Symbol Recognition
"... There is increasing interest in building systems that can automatically interpret hand-drawn sketches. However, many challenges remain in terms of recognition accuracy, robustness to different drawing styles, and ability to generalize across multiple domains. To address these challenges, we propose ..."
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Cited by 23 (6 self)
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There is increasing interest in building systems that can automatically interpret hand-drawn sketches. However, many challenges remain in terms of recognition accuracy, robustness to different drawing styles, and ability to generalize across multiple domains. To address these challenges, we propose a new approach to sketched symbol recognition that focuses on the visual appearance of the symbols. This allows us to better handle the range of visual and stroke-level variations found in freehand drawings. We also present a new symbol classifier that is computationally efficient and invariant to rotation and local deformations. We show that our method exceeds state-of-the-art performance on all three domains we evaluated, including handwritten digits, PowerPoint shapes, and electrical circuit symbols. 1
Supporting generic sketching-based input of diagrams in a domainspecific visual language meta-tool
- In ICSE ’07: Proceedings of the 29th International Conference on Software Engineering
, 2007
"... meta-tool ..."
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Spatial recognition and grouping of text and graphics
- In EUROGRAPHICS Workshop on Sketch-Based Interfaces and Modeling
"... We present a framework for simultaneous grouping and recognition of shapes and symbols in free-form ink diagrams. The approach is completely spatial, that is it does not require any ordering on the strokes. Initially each of the strokes on the page is linked in a proximity graph. A discriminative cl ..."
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Cited by 17 (2 self)
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We present a framework for simultaneous grouping and recognition of shapes and symbols in free-form ink diagrams. The approach is completely spatial, that is it does not require any ordering on the strokes. Initially each of the strokes on the page is linked in a proximity graph. A discriminative classifier is used to classify connected subgraphs as either making up one of the known symbols or perhaps as an invalid combination of strokes (e.g. including strokes from two different symbols). This classifier combines the rendered image of the strokes with stroke features such as curvature and endpoints. A small subset of very efficient features is selected, yielding an extremely fast classifier. An A-star search algorithm over connected subsets of the proximity graph is used to simultaneously find the optimal segmentation and recognition of all the strokes on the page. Experiments demonstrate that the system can achieve 97 % segmentation/recognition accuracy on a cross-validated shape dataset from 19 different writers. Categories and Subject Descriptors (according to ACM CCS): I.3.3 [Computer Graphics]: Line and Curve Generation). 1.
Recognition and beautification of multi-stroke symbols
- in digital ink, Computers & Graphics 29
, 2005
"... Sketch-based user interfaces provide a more direct and convenient way for interacting with computers, especially for performing graphical tasks. Most computer programs provide a mouse-and-palette based user interface for editing shapes which can be cumbersome to use. In order to draw a shape, a user ..."
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Cited by 16 (0 self)
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Sketch-based user interfaces provide a more direct and convenient way for interacting with computers, especially for performing graphical tasks. Most computer programs provide a mouse-and-palette based user interface for editing shapes which can be cumbersome to use. In order to draw a shape, a user must first select the desired shape from a menu or from a hierarchy of menus, and then make a series of adjustments to the shape (i.e. rotation, scaling, horizontal/vertical flip, etc.). A more convenient approach to this task is to allow the user to sketch the desired shape directly and then replace it with a ‘beautified ’ symbol with the correct transformation, all in one step. In this paper, we present a complete system for recognizing and beautifying sketched symbols. We have implemented this system as an interface to the Microsoft PowerPoint application to enable a user to sketch symbols directly onto a presentation slide.
SUMLOW: Early Design-Stage Sketching of UML Diagrams on an E-whiteboard
"... Most visual diagramming tools provide point-and-click construction of computer-drawn diagram elements using a conventional desktop computer and mouse. SUMLOW is a Unified Modelling Language (UML) diagramming tool that uses an E-whiteboard and sketching-based user interface to support collaborative s ..."
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Cited by 9 (2 self)
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Most visual diagramming tools provide point-and-click construction of computer-drawn diagram elements using a conventional desktop computer and mouse. SUMLOW is a Unified Modelling Language (UML) diagramming tool that uses an E-whiteboard and sketching-based user interface to support collaborative software design. SUMLOW allows designers to sketch UML constructs, mixing different UML diagram elements, diagram annotations and hand-drawn text. A key novelty of the tool is the preservation of hand-drawn diagrams and support for manipulation of these sketches using pen-based actions. Sketched diagrams can be automatically "formalized " into computer-recognised and drawn UML diagrams and then exported to a 3rd party CASE tool for further extension and use. We describe the motivation for SUMLOW, illustrate use of the tool to sketch various UML diagram types, describe its key architecture abstractions and implementation approaches, and report on two evaluations of the toolset. We hope our experiences will be useful for others developing sketching-based design tools or those looking to leverage pen-based interfaces in software applications. Keywords: sketch-based user interfaces, E-whiteboards, CASE tools, unified modelling language, hand-drawn visual language recognition
Combining Representations for Improved Sketch Recognition
, 2009
"... 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; addit ..."
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Cited by 6 (0 self)
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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 subshapes. 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.
A Comparative Study on Using Zernike Velocity Moments and Hidden Markov Models for Hand Gesture Recognition. Articulated Motion and Deformable Objects
"... Abstract. Hand-gesture recognition presents a challenging problem for computer vision due to the articulated structure of the human hand and the complexity of the environments in which it is typically applied. Solving such a problem requires a robust tracking mechanism which in turn depends on an ef ..."
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Cited by 4 (0 self)
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Abstract. Hand-gesture recognition presents a challenging problem for computer vision due to the articulated structure of the human hand and the complexity of the environments in which it is typically applied. Solving such a problem requires a robust tracking mechanism which in turn depends on an effective feature descriptor and classifier. Moment invariants, as discriminative feature descriptors, have been used for shape representation for many years. Zernike moments have been particularly attractive for their scale, translation and rotation invariance. More recently, Zernike moments have been extended to a spatio-temporal descriptor, known as the Zernike velocity moment, through combining with the displacement vector of the centre of mass of the target object between video frames. This descriptor has hitherto been demonstrated successfully in human gait analysis. In this paper, we introduce and evaluate the application of Zernike velocity moments in hand-gesture recognition, and compare with a bank of hidden Markov models with Zernike moments as observations. We demonstrate good performance for both approaches, with a substantial increase in performance for the latter method. Key words: Spatio-temporal description, hand gesture recognition, skin-colour segmentation, Zernike velocity moments, HMM 1
