A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Attribute openings, thinnings, and granulometries
Computer Vision and Image Understanding
International Journal of Computer Vision
Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
Embedding Gestalt Laws in Markov Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Topographic Maps and Local Contrast Changes in Natural Images
International Journal of Computer Vision
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Edge Detection by Helmholtz Principle
Journal of Mathematical Imaging and Vision
Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation
International Journal of Computer Vision
Regularized Laplacian Zero Crossings as Optimal Edge Integrators
International Journal of Computer Vision
A Grouping Principle and Four Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Segmentation Using Local Variation
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Good continuations in digital image level lines
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
Fast computation of a contrast-invariant image representation
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
The curvelet transform for image denoising
IEEE Transactions on Image Processing
Sparse geometric image representations with bandelets
IEEE Transactions on Image Processing
Flat zones filtering, connected operators, and filters by reconstruction
IEEE Transactions on Image Processing
An A Contrario Decision Method for Shape Element Recognition
International Journal of Computer Vision
Level Lines Selection with Variational Models for Segmentation and Encoding
Journal of Mathematical Imaging and Vision
A Unified Framework for Detecting Groups and Application to Shape Recognition
Journal of Mathematical Imaging and Vision
Significant edges in the case of non-stationary Gaussian noise
Pattern Recognition
A color topographic map based on the dichromatic reflectance model
Journal on Image and Video Processing - Color in Image and Video Processing
On Straight Line Segment Detection
Journal of Mathematical Imaging and Vision
3D Edge Detection by Selection of Level Surface Patches
Journal of Mathematical Imaging and Vision
Pigmented Skin Lesions Classification Using Dermatoscopic Images
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
A contrario hierarchical image segmentation
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Body color sets: A compact and reliable representation of images
Journal of Visual Communication and Image Representation
SIAM Journal on Imaging Sciences
Adaptive vision leveraging digital retinas: extracting meaningful segments
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
Geometrically Guided Exemplar-Based Inpainting
SIAM Journal on Imaging Sciences
Detecting pedestrians on a Movement Feature Space
Pattern Recognition
On the Role of Contrast and Regularity in Perceptual Boundary Saliency
Journal of Mathematical Imaging and Vision
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Since the beginning, Mathematical Morphology has proposed to extract shapesfrom images as connected components of level sets. These methods have proved veryefficient in shape recognition and shape analysis. In this paper, we present an improved method to select the most meaningful level lines (boundaries of level sets) from an image. This extraction can be based on statistical arguments, leading to a parameter free algorithm. It permits to roughly extract all pieces of level lines of an image, that coincide with pieces of edges. By this method, the numberof encoded level lines is reduced by a factor 100, without any loss of shape contents. In contrast to edge detection algorithms or snakes methods, such a level lines selection method delivers accurate shape elements, without user parameter since selection parameters can be computed by the Helmholtz Principle. The paper aims at improving the original method proposed in [10]. We give a mathematicalinterpretation of the model, which explains why some pieces of curve are overdetected. We introduce a multiscale approach that makes the method more robust to noise. A more local algorithm is introduced, taking local contrast variations into account. Finally, we empirically prove that regularity makes detection more robust but does not qualitatively change the results.