SBM07: Sketch Based Interfaces and Modeling 2007
https://diglib.eg.org:443/handle/10.2312/419
ISBN 978-3-905674-00-22024-03-29T14:46:04ZInk Features for Diagram Recognition
https://diglib.eg.org:443/handle/10.2312/SBM.SBM07.131-138
Ink Features for Diagram Recognition
Patel, Rachel; Plimmer, Beryl; Grundy, John; Ihaka, Ross
Michiel van de Panne and Eric Saund
The ability to automatically recognize a sketch accurately is important to computer-based diagramming. Many recognition techniques have been proposed but few researchers have reported the use of formal methods to select the most appropriate ink features for recognition algorithms. We have used a statistical approach to identify the most important distinguishing features of ink for dividing text and shapes. We implemented these into an existing recognition engine and conducted a comparative evaluation. Our feature set more successfully classified a range of common diagram elements than two existing dividers.
2007-01-01T00:00:00ZAddressing Class Distribution Issues of the Drawing vs Writing Classification in an Ink Stroke Sequence
https://diglib.eg.org:443/handle/10.2312/SBM.SBM07.139-146
Addressing Class Distribution Issues of the Drawing vs Writing Classification in an Ink Stroke Sequence
Wang, Xin; Biswas, Manoj; Raghupathy, Sashi
Michiel van de Panne and Eric Saund
Complicated by temporal correlations among the strokes and varying distributions of the underlying classes, the drawing/writing classification of ink strokes in a digital ink file poses interesting challenges. In this paper, we present our efforts in addressing some of the issues. First, we describe how we adjust the outputs of the neural network to a priori probabilities of new observations to produce more accurate estimates of the posterior probabilities. Second, we describe how to adapt the parameters of the HMM to new data sets. Albeit the fact that the emission probabilities of the HMM are computed indirectly from the outputs of the neural network, our modified Baum-Welch algorithm still finds the correct estimates for the HMM's parameters. We also present experimental results of our new algorithms on 6 real world data sets. The results show that our methods increase the F Measures of both the drawing and the writing classes on the more ''drawing intensive'' data sets which have stronger temporal correlations. But they do not perform well on the more ''writing intensive'' data sets.
2007-01-01T00:00:00ZScribbles to Vectors: Preparation of Scribble Drawings for CAD Interpretation
https://diglib.eg.org:443/handle/10.2312/SBM.SBM07.123-130
Scribbles to Vectors: Preparation of Scribble Drawings for CAD Interpretation
Bartolo, A.; Camilleri, K. P.; Fabri, S. G.; Borg, J. C.; Farrugia, P. J.
Michiel van de Panne and Eric Saund
This paper describes the work carried out on off-line paper based scribbles such that they can be incorporated into a sketch-based interface without forcing designers to change their natural drawing habits. In this work, the scribbled drawings are converted into a vectorial format which can be recognized by a CAD system. This is achieved by using pattern analysis techniques, namely the Gabor filter to simplify the scribbled drawing. Vector line are then extracted from the resulting drawing by means of Kalman filtering.
2007-01-01T00:00:00ZManaging Ambiguity in Mathematical Matrices
https://diglib.eg.org:443/handle/10.2312/SBM.SBM07.115-122
Managing Ambiguity in Mathematical Matrices
Tausky, David; Labahn, George; Lank, Edward; Marzouk, Mirette
Michiel van de Panne and Eric Saund
In this paper we describe strategies for recognizing and using hand drawn matrices in a pen math system. This includes a new technique to recognize common short-forms of writing matrices using ellipsis (. . . ). Ellipsis are commonly used in sketched matrices to illustrate the structure of a matrix without fully specifying the matrix. A second contribution of this paper is a new method to estimate the parameters of the hand drawn matrix, such as the number and position of the rows and columns. This is done using a modified clustering algorithm, allowing one to reduce the number of hard-coded constraints.
2007-01-01T00:00:00Z