Specifying gestures by example
Proceedings of the 18th annual conference on Computer graphics and interactive techniques
A formula for incorporating weights into scoring rules
Theoretical Computer Science - Special issue on the 6th International Conference on Database Theory—ICDT '97
Symbol Recognition by Error-Tolerant Subgraph Matching between Region Adjacency Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Introduction to Algorithms
A Linear Programming Approach for the Weighted Graph Matching Problem
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust sketched symbol fragmentation using templates
Proceedings of the 9th international conference on Intelligent user interfaces
Hierarchical parsing and recognition of hand-sketched diagrams
Proceedings of the 17th annual ACM symposium on User interface software and technology
HMM-based efficient sketch recognition
Proceedings of the 10th international conference on Intelligent user interfaces
Multi-domain sketch understanding
Multi-domain sketch understanding
LADDER: a language to describe drawing, display, and editing in sketch recognition
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
LADDER, a sketching language for user interface developers
Computers and Graphics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Constellation models for sketch recognition
SBM'06 Proceedings of the Third Eurographics conference on Sketch-Based Interfaces and Modeling
Feature extraction and classifier combination for image-based sketch recognition
Proceedings of the Seventh Sketch-Based Interfaces and Modeling Symposium
Sketch recognition by fusion of temporal and image-based features
Pattern Recognition
From engineering diagrams to engineering models: Visual recognition and applications
Computer-Aided Design
HBF49 feature set: A first unified baseline for online symbol recognition
Pattern Recognition
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In this paper we propose a combinatorial model for sketch recognition. Two fundamental problems, the evaluation of individual symbols and the interpretation of a complete sketch scene possibly containing several symbols, are expressed as combinatorial optimization problems. We settle the computational complexity of the combinatorial problems and present a branch and bound algorithm for computing optimal symbol confidences. To handle sketch scenes in practice we propose a modest restriction of drawing freedom and present an algorithm which only needs to compute a polynomial number of symbol confidences.