Perceptual Organization and Curve Partitioning
IEEE Transactions on Pattern Analysis and Machine Intelligence
The diversity of perceptual grouping
Vision, brain, and cooperative computation
Algorithms for clustering data
Algorithms for clustering data
Trace Inference, Curvature Consistency, and Curve Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision, Graphics, and Image Processing
A hierarchical approach to line extraction based on the Hough transform
Computer Vision, Graphics, and Image Processing
Perceptual grouping of curved lines
Proceedings of a workshop on Image understanding workshop
The Combinatorics of Heuristic Search Termination for Object Recognition in Cluttered Environments
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Verification of Hypothesized Matches in Model-Based Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Perceptual Organization for Scene Segmentation and Description
IEEE Transactions on Pattern Analysis and Machine Intelligence
Identifying salient circular arcs on curves
CVGIP: Image Understanding
A Bayesian multiple-hypothesis approach to edge grouping and contour segmentation
International Journal of Computer Vision
Robust and Efficient Detection of Salient Convex Groups
IEEE Transactions on Pattern Analysis and Machine Intelligence
Inference of Surfaces, 3D Curves, and Junctions from Sparse, Noisy, 3D Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Generic Grouping Algorithm and Its Quantitative Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical Image Segmentation—Part I: Detection of Regular Curves in a Vector Graph
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
Filtering, Segmentation, and Depth
Filtering, Segmentation, and Depth
Artificial Neural Networks and Statistical Pattern Recognition
Artificial Neural Networks and Statistical Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
An axiomatic approach to clustering line-segments
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1
Grouping for Recognition
Perceptual organization and visual recognition
Perceptual organization and visual recognition
The curve indicator random field
The curve indicator random field
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Some General Grouping Principles: Line Perception from Points as an Example
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Detection of unexpected multi-part objects from segmented contour maps
Pattern Recognition
Grounded semantic composition for visual scenes
Journal of Artificial Intelligence Research
Review article: Edge and line oriented contour detection: State of the art
Image and Vision Computing
View-Dependent line drawings for 3d scenes
Transactions on Edutainment VII
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Grouping in vision can be seen as the process that organizes image entities into higher-level structures. Despite its importance, there is little consistency in the statement of the grouping problem in literature. In addition, most grouping algorithms in vision are inspired on a specific technique, rather than being based on desired characteristics, making it cumbersome to compare the behavior of various methods. This paper discusses six precisely formulated considerations for the design of generic grouping algorithms in vision: proper definition, invariance, multiple interpretations, multiple solutions, simplicity and robustness. We observe none of the existing algorithms for grouping in vision meet all the considerations. We present a simple algorithm as an extension of a classical algorithm, where the extension is based on taking the considerations into account. The algorithm is applied to three examples: grouping point sets, grouping poly-lines, and grouping flow-field vectors. The complexity of the greedy algorithm is{\cal{O}}(n{\cal O}_G) , where{\cal{O}}_Gis the complexity of the grouping measure.