Variational methods in image segmentation
Variational methods in image segmentation
A Local Visual Operator Which Recognizes Edges and Lines
Journal of the ACM (JACM)
Kona: A Multi-junction Detector Using Minimum Description Length Principle
EMMCVPR '97 Proceedings of the First International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
Evaluation of Interest Point Detectors
International Journal of Computer Vision - Special issue on a special section on visual surveillance
Uncertainty Propagation and the Matching of Junctions as Feature Groupings
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition Letters
A Local Algorithm for Real-Time Junction Detection in Contour Images
CAIP '01 Proceedings of the 9th International Conference on Computer Analysis of Images and Patterns
Bayesian Models for Finding and Grouping Junctions
EMMCVPR '99 Proceedings of the Second International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
Detection and characterization of junctions in a 2D image
Computer Vision and Image Understanding
Neural mechanisms for the robust representation of junctions
Neural Computation
Parsing Images into Regions, Curves, and Curve Groups
International Journal of Computer Vision
A model-based approach to junction detection using radial energy
Pattern Recognition
Feature point detection utilizing the empirical mode decomposition
EURASIP Journal on Advances in Signal Processing
A simple corner orientation detector
ISCGAV'08 Proceedings of the 8th conference on Signal processing, computational geometry and artificial vision
Continuous dimensionality characterization of image structures
Image and Vision Computing
Application of Kohonen network for automatic point correspondence in 2D medical images
Computers in Biology and Medicine
Spatial-Temporal Junction Extraction and Semantic Interpretation
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
Feature points detection using combined character along principal orientation
MIRAGE'07 Proceedings of the 3rd international conference on Computer vision/computer graphics collaboration techniques
Multimodal genetic algorithms-based algorithm for automatic point correspondence
Pattern Recognition
Local occlusion detection under deformations using topological invariants
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Visual cortex inspired junction detection
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
Feature point extraction from the local frequency map of an image
Journal of Electrical and Computer Engineering
International Journal of Computer Vision
Junction assisted 3D pose retrieval of untextured 3D models in monocular images
Computer Vision and Image Understanding
Accurate junction detection and characterization in line-drawing images
Pattern Recognition
Accurate Junction Detection and Characterization in Natural Images
International Journal of Computer Vision
Context-aware features and robust image representations
Journal of Visual Communication and Image Representation
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Junctions are important features for image analysis and form a critical aspect of image understanding tasks such as object recognition. We present a unified approach to detecting (location of the center of the junction), classifying (by the number of wedges驴lines, corners, three-junctions such as T or Y junctions, or four-junctions such as X-junctions), and reconstructing junctions (in terms of radius size, the angles of each wedge and the intensity in each of the wedges) in images. Our main contribution is a modeling of the junction which is complex enough to handle all these issues and yet simple enough to admit an effective dynamic programming solution. Broadly, we use a template deformation framework along with a gradient criterium to detect radial partitions of the template. We use the minimum description length principle to obtain the optimal number of partitions that best describes the junction. Kona [27] is an implementation of this model. We (quantitatively) demonstrate the stability and robustness of the detector by analyzing its behavior in the presence of noise, using synthetic/controlled apparatus. We also present a qualitative study of its behavior on real images.