Citation : http://dl.acm.org/citation.cfm?id=332458
Authors: A. Chris Long, Jr., James A. Landay, Lawrence A. Rowe, and Joseph Michiels
This paper describes about a tools to help designers of sketch recognition systems to improve their gesture set and make it easier for the user. It will notify designer about the similar gestures, the gestures that are difficult to learn and remember and those that may be misrecognized by computer.
Two experiments were conducted to determine the features that affect gesture similarity.
Experiment 1: A wide range of gestures of different orientation was used. The user had to choose one of the three gestures which looked different from the other two in a triad. MDS and regression was used to analyse the data. From analysis using MDS it was found that ordinal model and eucledian distance gave the best fit and hence they were used for subsequent analysis.Regression analysis provided weights for each feature and correlation of the feature with similarity.
Experiment 2: Three new gesture sets, to explore total absolute angle and aspect, length and area, rotation related features, each of nine gestures were created to determine how a feature would affect perceived similarity. A fourth set consisting of two gestures from these three was created to compare three sets against one another. Data from each of the sets were analysed independently to determine how targeted features affected similarity.
Results of experiment 1 were used to predict similarities of gestures of experiment 2.
One important observation from both experiments was that neither length nor area were a significant feature for similarity. Experiment 1 predicts the data slightly better and uses more features helping capture more about underlying psychological model
Authors: A. Chris Long, Jr., James A. Landay, Lawrence A. Rowe, and Joseph Michiels
This paper describes about a tools to help designers of sketch recognition systems to improve their gesture set and make it easier for the user. It will notify designer about the similar gestures, the gestures that are difficult to learn and remember and those that may be misrecognized by computer.
Two experiments were conducted to determine the features that affect gesture similarity.
Experiment 1: A wide range of gestures of different orientation was used. The user had to choose one of the three gestures which looked different from the other two in a triad. MDS and regression was used to analyse the data. From analysis using MDS it was found that ordinal model and eucledian distance gave the best fit and hence they were used for subsequent analysis.Regression analysis provided weights for each feature and correlation of the feature with similarity.
Experiment 2: Three new gesture sets, to explore total absolute angle and aspect, length and area, rotation related features, each of nine gestures were created to determine how a feature would affect perceived similarity. A fourth set consisting of two gestures from these three was created to compare three sets against one another. Data from each of the sets were analysed independently to determine how targeted features affected similarity.
Results of experiment 1 were used to predict similarities of gestures of experiment 2.
One important observation from both experiments was that neither length nor area were a significant feature for similarity. Experiment 1 predicts the data slightly better and uses more features helping capture more about underlying psychological model
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