IEEE Transactions on Pattern Analysis and Machine Intelligence
HYPER: A New Approach for the Recognition and Positioning of Two-Dimensional Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Detection of Dominant Points on Digital Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
A Theory of Multiscale, Curvature-Based Shape Representation for Planar Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing general curved objects efficiently
Geometric invariance in computer vision
Parts of Visual Form: Computational Aspects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Model-Based Shape Matching with Structural Feature Grouping
IEEE Transactions on Pattern Analysis and Machine Intelligence
3D-2D projective registration of free-form curves and surfaces
Computer Vision and Image Understanding
Efficiently Locating Objects Using the Hausdorff Distance
International Journal of Computer Vision
Computer Vision and Image Understanding
Planar shape recognition by directional flow-change method
Pattern Recognition Letters
Line-Based Recognition Using A Multidimensional Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shock Graphs and Shape Matching
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Locating Perceptually Salient Points on Planar Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Processing, Analysis, and Machine Vision
Image Processing, Analysis, and Machine Vision
Two-dimensional object alignment based on the robust oriented Hausdorff similarity measure
IEEE Transactions on Image Processing
An A Contrario Decision Method for Shape Element Recognition
International Journal of Computer Vision
A coarse-to-fine method for shape recognition
Journal of Computer Science and Technology
Journal of Visual Communication and Image Representation
Shape recognition with coarse-to-fine point correspondence under image deformations
Proceedings of the 2005 joint Chinese-German conference on Cognitive systems
Feature vector field and feature matching
Pattern Recognition
Distributed curve matching in camera networks using projective joint invariant signatures
Proceedings of the Fourth ACM/IEEE International Conference on Distributed Smart Cameras
Shape matching using coarse descriptors
International Journal of Computational Vision and Robotics
CTFDP: an affine invariant method for matching contours
ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
Image content based curve matching using HMCD descriptor
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
Matching noisy outline contours using a descriptor reduction approach
ICISP'12 Proceedings of the 5th international conference on Image and Signal Processing
Shape Codification Indexing and Retrieval Using the Quad-Tree Structure
International Journal of Computer Vision and Image Processing
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This paper describes a method to identify partially occluded shapes which are randomly oriented in 3D space. The goal is to match the object contour present in an image with an object in a database. The approach followed is the alignment method which has been described in detail in the literature. Using this approach the recognition process is divided into two stages: first, the transformation between the viewed object and the model object is determined, and second, the model that best matches the viewed object is found, in the first stage, invariant points under projective transformation (based on bitangency) are used, which drastically reduced the selection space for alignment. Next, the curves are compared after the transformation matrix is estimated between the image and the model in order to determine the pose of the curve that undergoes the perspective projection. The evaluation process is performed using a novel estimation of the Hausdorff distance (HD), called the continuity HD. It evaluates partially occluded curves in the image in relation to the complete contour in the database. The experimental results showed that the present algorithm can cope with noisy figures, projective transformations, and complex occlusions.