How Easy is Matching 2D Line Models Using Local Search?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Subgraph isomorphism in planar graphs and related problems
Proceedings of the sixth annual ACM-SIAM symposium on Discrete algorithms
Interpreting Sloppy Stick Figures by Graph Rectification and Constraint-Based Matching
GREC '01 Selected Papers from the Fourth International Workshop on Graphics Recognition Algorithms and Applications
An Open-Ended Finite Domain Constraint Solver
PLILP '97 Proceedings of the9th International Symposium on Programming Languages: Implementations, Logics, and Programs: Including a Special Trach on Declarative Programming Languages in Education
Design, Implementation, and Evaluation of the Constraint Language cc(FD)
Selected Papers from Constraint Programming: Basics and Trends
Constraint satisfaction algorithms for graph pattern matching
Mathematical Structures in Computer Science
Interpreting Sloppy Stick Figures by Graph Rectification and Constraint-Based Matching
GREC '01 Selected Papers from the Fourth International Workshop on Graphics Recognition Algorithms and Applications
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Machine systems for understanding hand-drawn sketches must reliably interpret common but sloppy curvilinear configurations. The task is commonly expressed as finding an image model in the image data, but few approaches exist for recognizing drawings with missing model parts and noisy data. In this paper, we propose a two-stage structural modeling approach that combines computer vision techniques with constraint-based recognition. The first stage produces a data graph through standard image analysis techniques augmented by rectification operations that account for common forms of drawing variability and noise. The second stage combines CLP(FD) with concurrent constraint programming for efficient and optimal matching of attributed model and data graphs. This approach offers considerable ease in stating model constraints and objectives, and also leads to an efficient algorithm that scales well with increasing image complexity.