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Book ChapterDOI

Automatic Synthesis of Boolean Expression and Error Detection from Logic Circuit Sketches

TL;DR: This paper proposes LEONARDO (Logic Expression fOrmatioN And eRror Detection framewOrk), a hierarchical approach to recognize boolean expression from hand drawn digital logic gate diagram, which proved to be more robust to user variability in design and yielded an accuracy gain over others.
Abstract: Automatic techniques to recognize and evaluate digital logic circuits are more efficient and require less human intervention, as compared to, traditional pen and paper methods. In this paper, we propose LEONARDO (Logic Expression fOrmatioN And eRror Detection framewOrk), a hierarchical approach to recognize boolean expression from hand drawn digital logic gate diagram. The key contributions in the proposed approach are: (i) a novel hierarchical framework to synthesize boolean expression from a hand drawn logic circuit diagram; and (ii) identification of anomalies in drawing. Extensive experimentation was performed through qualitative and quantitative analysis. Results were also compared with existing techniques proposed on the similar problem. Upon experimentation and analysis, our system proved to be more robust to user variability in design and yielded an accuracy of \(95.2\%\), which is a \(4\%\) gain over others.
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Proceedings ArticleDOI
01 Jul 1991
TL;DR: GRANDMA, a toolkit for rapidly adding gestures to direct manipulation interfaces, and the trainable single-stroke gesture recognizer used by GRANDMA are described.
Abstract: Gesture-Based interfaces offer an alternative to traditional keyboard, menu, and direct manipulation interfaces. The ability to specify objects, an operation, and additional parameters with a single intuitive gesture appeals to both novice and experienced users. Unfortunately, gesture-based interfaces have not been extensively researched, partly because they are difficult to create. This paper describes GRANDMA, a toolkit for rapidly adding gestures to direct manipulation interfaces. The trainable single-stroke gesture recognizer used by GRANDMA is also described.

910 citations

Journal ArticleDOI
01 Jul 2012
TL;DR: This paper is the first large scale exploration of human sketches, developing a bag-of-features sketch representation and using multi-class support vector machines, trained on the sketch dataset, to classify sketches.
Abstract: Humans have used sketching to depict our visual world since prehistoric times. Even today, sketching is possibly the only rendering technique readily available to all humans. This paper is the first large scale exploration of human sketches. We analyze the distribution of non-expert sketches of everyday objects such as 'teapot' or 'car'. We ask humans to sketch objects of a given category and gather 20,000 unique sketches evenly distributed over 250 object categories. With this dataset we perform a perceptual study and find that humans can correctly identify the object category of a sketch 73% of the time. We compare human performance against computational recognition methods. We develop a bag-of-features sketch representation and use multi-class support vector machines, trained on our sketch dataset, to classify sketches. The resulting recognition method is able to identify unknown sketches with 56% accuracy (chance is 0.4%). Based on the computational model, we demonstrate an interactive sketch recognition system. We release the complete crowd-sourced dataset of sketches to the community.

874 citations

Journal ArticleDOI
TL;DR: Gesture-Based interfaces offer an alternative to traditional keyboard, menu, and direct manipulation interfaces and provide the ability to specify objects, an operation, and additional parameters with a single command.
Abstract: Gesture-Based interfaces offer an alternative to traditional keyboard, menu, and direct manipulation interfaces. The ability to specify objects, an operation, and additional parameters with a singl...

314 citations

Journal ArticleDOI
TL;DR: Curvature scale space (CSS) image representation along with a small number of global parameters are used for this purpose and the results show the promising performance of the method and its superiority over Fourier descriptors and moment invariants.
Abstract: In many applications, the user of an image database system points to an image, and wishes to retrieve similar images from the database. Computer vision researchers aim to capture image information in feature vectors which describe shape, texture and color properties of the image. These vectors are indexed or compared to one another during query processing to find images from the database. This paper is concerned with the problem of shape similarity retrieval in image databases. Curvature scale space (CSS) image representation along with a small number of global parameters are used for this purpose. The CSS image consists of several arch-shape contours representing the inflection points of the shape as it is smoothed. The maxima of these contours are used to represent a shape. The method is then tested on a database of 1100 images of marine creatures. A classified subset of this database is used to evaluate the method and compare it with other methods. The results show the promising performance of the method and its superiority over Fourier descriptors and moment invariants.

222 citations

Proceedings ArticleDOI
30 Jul 2006
TL;DR: Tahuti is a dual-view sketch recognition environment for class diagrams in UML based on a multi-layer recognition framework which recognizes multi-stroke objects by their geometrical properties allowing users the freedom to draw naturally as they would on paper rather than requiring the user to draw the objects in a pre-defined manner.
Abstract: We have created and tested Tahuti, a dual-view sketch recognition environment for class diagrams in UML. The system is based on a multi-layer recognition framework which recognizes multi-stroke objects by their geometrical properties allowing users the freedom to draw naturally as they would on paper rather than requiring the user to draw the objects in a pre-defined manner. Users can draw and edit while viewing either their original strokes or the interpreted version of their strokes engendering user-autonomy in sketching. The experiments showed that users preferred Tahuti to a paint program and to Rational Rose™ because it combined the ease of drawing found in a paint program with the ease of editing available in a UML editor.

203 citations