An introduction to splines for use in computer graphics & geometric modeling
An introduction to splines for use in computer graphics & geometric modeling
Introduction to algorithms
On minimal energy trajectories
Computer Vision, Graphics, and Image Processing
Computer graphics: principles and practice (2nd ed.)
Computer graphics: principles and practice (2nd ed.)
Finding convex edge groupings in an image
International Journal of Computer Vision
Computing perceptual organization in computer vision
Computing perceptual organization in computer vision
Robust and Efficient Detection of Salient Convex Groups
IEEE Transactions on Pattern Analysis and Machine Intelligence
Inferring global perceptual contours from local features
International Journal of Computer Vision - Special issue on computer vision research at the University of Southern California
A Framework for Performance Characterization of Intermediate-Level Grouping Modules
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Generic Grouping Algorithm and Its Quantitative Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extracting Salient Curves from Images: An Analysis of the Saliency Network
International Journal of Computer Vision
A Comparison of Measures for Detecting Natural Shapes in Cluttered Backgrounds
International Journal of Computer Vision - Special issue on computer vision research at NEC Research Institute
IEEE Transactions on Pattern Analysis and Machine Intelligence
Perceptual Organization and Visual Recognition
Perceptual Organization and Visual Recognition
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
Bezier and B-Spline Techniques
Bezier and B-Spline Techniques
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
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
Quantitative Measures of Change based on Feature Organization: Eigenvalues and Eigenvectors
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Perceptual organization in an interactive sketch editing application
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
ISCV '95 Proceedings of the International Symposium on Computer Vision
Recognizing 3-D Objects Using 2-D Images
Recognizing 3-D Objects Using 2-D Images
Perceptual Grouping for Contour Extraction
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Salient Closed Boundary Extraction with Ratio Contour
IEEE Transactions on Pattern Analysis and Machine Intelligence
Convex Grouping Combining Boundary and Region Information
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Contour grouping with prior models
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Approach to the Parameterization of Structure for Fast Categorization
International Journal of Computer Vision
Convexity grouping of salient contours
GbRPR'11 Proceedings of the 8th international conference on Graph-based representations in pattern recognition
Anytime perceptual grouping of 2D features into 3D basic shapes
ICVS'13 Proceedings of the 9th international conference on Computer Vision Systems
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As an important geometric property of many structures or structural components, convexity plays an important role in computer vision and image understanding. In this paper, we describe a general approach that can force various edge-grouping algorithms to detect only convex structures from a set of boundary fragments. The basic idea is to remove some fragments and fragment connections so that, on the remaining ones, a prototype edge-grouping algorithm that detects closed boundaries without the convexity constraint can only produce convex closed boundaries. We show that this approach takes polynomial time and preserves the grouping optimality by not excluding any valid convex boundary from the search space. Choosing the recently developed ratio-contour algorithm as the prototype grouping algorithm, we develop a new convex-grouping algorithm, which can detect convex salient boundaries with good continuity and proximity in a globally optimal fashion. To facilitate the application of this convex-grouping algorithm, we develop a new fragment-connection method based on four-point Bezier curves. We demonstrate the performance of this convex-grouping algorithm by conducting experiments on both synthetic and real images. In addition, we provide a comparison with some prior edge-grouping algorithms. Finally, we show that the proposed convex-grouping algorithm can be further extended to detect convex open boundaries, derive region-based image hierarchies, and incorporate some simple human-computer interactions.