Showing papers on "Sketch recognition published in 2001"

Symbol Recognition: Current Advances and Perspectives

Abstract: The recognition of symbols in graphic documents is an intensive research activity in the community of pattern recognition and document analysis. A key issue in the interpretation of maps, engineering drawings, diagrams, etc. is the recognition of domain dependent symbols according to a symbol database. In this work we first review the most outstanding symbol recognition methods from two different points of view: application domains and pattern recognition methods. In the second part of the paper, open and unaddressed problems involved in symbol recognition are described, analyzing their current state of art and discussing future research challenges. Thus, issues such as symbol representation, matching, segmentation, learning, scalability of recognition methods and performance evaluation are addressed in this work. Finally, we discuss the perspectives of symbol recognition concerning to new paradigms such as user interfaces in handheld computers or document database and WWW indexing by graphical content.

Example-based facial sketch generation with non-parametric sampling

Abstract: In this paper, we present an example-based facial sketch system. Our system automatically generates a sketch from an input image, by learning from example sketches drawn with a particular style by an artist. There are two key elements in our system: a non-parametric sampling method and a flexible sketch model. Given an input image pixel and its neighborhood, the conditional distribution of a sketch point is computed by querying the examples and finding all similar neighborhoods. An "expected sketch image" is then drawn from the distribution to reflect the drawing style. Finally, facial sketches are obtained by incorporating the sketch model. Experimental results demonstrate the effectiveness of our techniques.

Face Detection and Gesture Recognition for Human-Computer Interaction

Abstract: With the ubiquity of new information technology and media, more effective and friendly methods for human computer interaction (HCI) are being developed which do not rely on traditional devices such as keyboards, mice and displays. The first step for any intelligent HCI system is face detection, and one of most friendly HCI systems is hand gesture. Face Detection and Gesture Recognition for Human-Computer Interaction introduces the frontiers of vision-based interfaces for intelligent human computer interaction with focus on two main issues: face detection and gesture recognition. The first part of the book reviews and discusses existing face detection methods, followed by a discussion on future research. Performance evaluation issues on the face detection methods are also addressed. The second part discusses an interesting hand gesture recognition method based on a generic motion segmentation algorithm. The system has been tested with gestures from American Sign Language with promising results. We conclude this book with comments on future work in face detection and hand gesture recognition. Face Detection and Gesture Recognition for Human-Computer Interaction will interest those working in vision-based interfaces for intelligent human computer interaction. It also contains a comprehensive survey on existing face detection methods, which will serve as the entry point for new researchers embarking on such topics. Furthermore, this book also covers in-depth discussion on motion segmentation algorithms and applications, which will benefit more seasoned graduate students or researchers interested in motion pattern recognition.

Automatic sketch generation

Abstract: An example-based facial sketch system and process that automatically generates a sketch from an input image depicting a person's face. Sketch generation is accomplished by first training the system using example facial images and sketches of the depicted faces drawn with a particular style by a sketch artist. The trained system is then used to automatically generate a facial sketch that simulates the artist's style from an input image depicting a person's face. Nonparametric sampling and a flexible sketch model are employed to capture the complex statistical characteristics between an image and its sketch.

On-line recognition of UML diagrams

Abstract: Unified Modeling Language (UML) diagrams are widely used by software engineers to describe the structure of software systems. Early in the software design cycle, software engineers informally sketch initial UML diagrams on paper or whiteboards. The information provided by these UML diagrams needs to be made available to computer assisted software engineering (CASE) tools. In order to smooth this transition from paper to electronic form, we have developed an online recognition system for UML diagrams. The system accepts input from an electronic whiteboard, a data tablet or a mouse. Efforts have been made to separate the domain-independent and domain-specific parts of the recognition system. The kernel of the system is retargetable, providing a general front end for online recognition of any glyph-based diagram notation. The kernel is extended with UML-specific routines for segmentation, recognition of glyphs, and recognition of glyph relationships.

Smart Sketchpad-an on-line graphics recognition system

Abstract: An online graphics recognition system is presented, which provides users a natural, convenient, and efficient way to input rigid and regular shapes or graphic objects (e.g., triangles, rectangles, ellipses, straight line, arrowheads, etc.) by quickly drawing their sketchy shapes in single or multiple strokes. An input sketchy (hand-drawn) shape is immediately converted into the user-intended rigid shape based on the shape similarity and the time constraint of the sketchy line. Three different (rule-based, SVM-based, and ANN-based) approaches have been applied and compared in the system. Experiments and evaluation are also presented, which show good performance of the system.

Hand gesture recognition for man-machine interaction

Abstract: Addresses some basic problems of hand gesture recognition. Three steps important in building gestural vision-based interfaces are discussed. The first step is the hand location through detection of skin colored regions. Representative methods based on 2D color histograms are described and compared. The next step is the hand shape (posture) recognition. An approach that uses the morphological hit-miss operation is presented. Finally, application of hidden Markov models in recognition of dynamic gestures is explained. An experimental system that uses the discussed methods in real time is described and recognition results are presented.

Emergence in a Recognition Based Drawing Interface

Abstract: People perceive patterns in representations, patterns that may nothave been initially intended. This phenomenon of emergence is deemed toplay an important role in design. Computer based design assistants canand should support this human perceptual ability, using patternrecognition to anticipate human designers’ perception of emergent shapesand supporting the subsequent manipulation of and reasoning with theseshapes as part of the design. Freehand drawing programs with gesturerecognition are well positioned to implement shape emergence. Supportfor emergent shapes in the Back of an Envelope system is described.

A fuzzy approach to 2D-shape recognition

Abstract: This paper describes a method for fuzzy classification and recognition of 2D shapes, such as handwritten characters, image contours, etc. A fuzzy model is derived for each considered shape from a fuzzy description of a set of instances of this shape. A fuzzy description of a shape instance, in its turn, exploits appropriate fuzzy partitions of the two dimensions of the shape. These fuzzy partitions allow us to identify, and automatically associate an importance degree with the relevant shape zones for classification and recognition purposes. Two significant applications of the method are described, namely, recognition of olfactory signals and recognition of isolated, handwritten characters. In the former case, results are shown concerning the recognition of three different types of waste waters, collected in three different dilutions. In the latter case, results are shown concerning the application of the method to a NIST database, containing the segmented handprinted characters of 500 writers.

Interpreting Sloppy Stick Figures by Graph Rectification and Constraint-Based Matching

Abstract: Programs for understanding hand-drawn sketches and diagrams must interpret curvilinear configurations that are sloppily drawn and highly variable in form. We propose a two-stage subgraph matching framework for sketch recognition that can accommodate great variability in form and yet provide efficient matching and easy extensibility to new configurations. First, a rectification stage corrects the initial data graph for the common deviations of each kind of constituent local configuration from its ideal form. The model graph is then matched directly to the data by a constraint-based subgraph matching process, without the need for complex error-tolerance. We explore the approach in the domain of human stick figures in diverse poses.

Handling ambiguity in constraint-based recognition of stick figure sketches

Abstract: Even seemingly simple drawings, diagrams, and sketches are hard for computer programs to interpret, because these inputs can be highly variable in several respects. This variability corrupts the expected mapping between a prior model of a configuration and an instance of it in the scene. We propose a scheme for representing ambiguity explicitly, within a subgraph matching framework, that limits its impact on the computational and program complexity of matching. First, ambiguous alternative structures in the input are explicitly represented by coupled subgraphs of the data graph, using a class of segmentation post-processing operations termed graph elaboration. Second, the matching process enforces mutual exclusion constraints among these coupled alternatives, and preferences or rankings associated with them enable better matches to be found early on by a constrained optimization process. We describe several elaboration processes, and extend a straightforward constraint-based subgraph matching scheme to elaborated data graphs. The discussion focuses on the domain of human stick figures in diverse poses.

Intelligent computational sketching support for conceptual design

Abstract: Sketches, with their flexibility and suggestiveness, are in many ways ideal for expressing emerging design concepts. This can be seen from the fact that the process of representing early designs by free-hand drawings was used as far back as in the early 15th century [1]. On the other hand, CAD systems have become widely accepted as an essential design tool in recent years, not least because they provide a base on which design analysis can be carried out. Efficient transfer of sketches into a CAD representation, therefore, is a powerful addition to the designers' armoury. It has been pointed out by many that a pen-on-paper system is the best tool for sketching. One of the crucial requirements of a computer aided sketching system is its ability to recognise and interpret the elements of sketches. 'Sketch recognition', as it has come to be known, has been widely studied by people working in such fields: as artificial intelligence to human-computer interaction and robotic vision. Despite the continuing efforts to solve the problem of appropriate conceptual design modelling, it is difficult to achieve completely accurate recognition of sketches because usually sketches implicate vague information, and the idiosyncratic expression and understanding differ from each designer.

Pen-Based Gesture Recognition in Multimodal Human-Computer Interaction

Abstract: This paper proposes a fast, one stroke pen gesture recognition approach to the studying of multimodal human computer interaction theory and building method. In the approach, a pen gesture is characterized by a sequence of dominant points along the gesture trajectory and a sequence of writing directions between consecutive dominant points. The recognition result can be obtained by matching the feature code of the input gesture with the various possible feature codes of each standard gesture. The directional feature is used for gesture pre-classification and the positional information is used for fine classification. Experimental results show that this approach is fast and can get a high recognition rate.

Natural Sketch Recognition in UML Class Diagrams

Abstract: I created a natural sketch recognition environment for UML (Unified Modeling Language) (Alhir, 1998) My system differs from graffiti-based approaches to this task, in that it recognizes objects by how they look, not by how they are drawn My goal is a system where the user can sketch UML diagrams on a tablet or whiteboard in the same way they would on paper, but the diagrams would then be recognized by the computer to provide clean interpreted diagrams, stub code, and enhanced editing ability

A k-nearest neighbour method for managing the evolution of a learning base

Abstract: A character recognition system with continuous learning seeks to constantly enhance its base representation models in order to provide the best recognition rate. The method we are presenting enables the system to enhance its base with models, which are performant in recognition. This method also enables to get rid of models regularly doubtable in efficiency when it comes to interpretation of the characters studied. This rule is similar to the one used in the "Death by suffocation" game of life of Conway. We based ourselves on the theory of k-nearest neighbours to develop a new approach we named /spl epsiv/-adaptive neighbourhood. It makes an adjustment of classes possible, according to confidence rate in each model of the learning base. These rates which are practically represented as weights are taken into account by the stage of the recognition system during the character recognition phase. The use of weight as a model selection factor, useful for recognition, enables the system to manage the evolution of the learning base.

Human-computer interaction through computer vision

Abstract: In this paper, we describe the work of our group on the use of computer vision to complement existing modes of human-interaction. Our main achievements have been the development of an iris tracker and a hand tracker for HCI similar to a "visual mouse", and a deformable model of a generic face for facial feature extraction, for emotion recognition.

Contextual focus for improved recognition of hand-filled forms

Abstract: The advances in Optical Character Recognition (OCR) technology over the past decade have enabled the development of many automatic document-processing systems capable of 99% correct recognition on printed text However, similar advances in Cursive Script Recognition (CSR) technology have not been forthcoming due, principally, to the vast variability of human handwriting This paper investigates a method by which the more reliable OCR technology can be used to improve the CSR performance in a form processing application A novel method is proposed to link handwriting data to contextual cue words that have been automatically obtained from an OCR process This information is then used to select appropriate 'focused' lexicons to achieve better CSR results The method was tested on 30 forms that were filled by 10 different writers The experimental results together with a comparison to the base line recognition performance are presented

Off-line constrained vocabulary cursive script recognition using visible features

Abstract: This paper presents a model for off-line cursive script recognition. The method proposed combines both analytical and holistic approaches to solve the problem of cursive script recognition. The emphasis is to create a fast and reliable model for recognition. The holistic approach of extracting feature is used with the analytical approach of segmenting and recognizing the first character. Pre-processing, feature extraction, classifier, and phrase recognition are explained and used in this system. Results from a test set of 1294 images are presented based on three different word recognition methods that are experimented. This system is being used as a system to sort mails that are directed overseas, however, it can also be used for other requirements like word spotting in unconstrained text.