A buyer's guide to conic fitting
BMVC '95 Proceedings of the 6th British conference on Machine vision (Vol. 2)
Perceptual organization in computer vision: status, challenges, and potential
Computer Vision and Image Understanding - Special issue on perceptual organization in computer vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Planar Geometric Projections and Viewing Transformations
ACM Computing Surveys (CSUR)
Segmentation of Multiple Salient Closed Contours from Real Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finding Perceptually Closed Paths in Sketches and Drawings
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning to Form Large Groups of Salient Image Features
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Perceptual Grouping for Contour Extraction
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Global Detection of Salient Convex Boundaries
International Journal of Computer Vision
Contour grouping and abstraction using simple part models
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Contour Detection and Hierarchical Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
In Search of Perceptually Salient Groupings
IEEE Transactions on Image Processing
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2D perceptual grouping is a well studied area which still has its merits even in the age of powerful object recognizer, namely when no prior object knowledge is available. Often perceptual grouping mechanisms struggle with the runtime complexity stemming from the combinatorial explosion when creating larger assemblies of features, and simple thresholding for pruning hypotheses leads to cumbersome tuning of parameters. In this work we propose an incremental approach instead, which leads to an anytime method, where the system produces more results with longer runtime. Moreover the proposed approach lends itself easily to incorporation of attentional mechanisms. We show how basic 3D object shapes can thus be detected using a table plane assumption.