Segmentation through Variable-Order Surface Fitting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Structural Indexing: Efficient 3-D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Geometric invariants and object recognition
International Journal of Computer Vision
Local Invariants For Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sensor Modeling, Probabilistic Hypothesis Generation, and Robust Localization for Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Model-Based Recognition of 3D Curves From One View
Journal of Mathematical Imaging and Vision
Recognizing 3D Objects Using Tactile Sensing and Curve Invariants
Journal of Mathematical Imaging and Vision
Model-Based Recognition of 3D Objects from Single Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Noise-Resistant Invariants of Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
A New Paradigm for Recognizing 3-D Object Shapes from Range Data
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Shape Classification Using the Inner-Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
From region based image representation to object discovery and recognition
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
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In this paper, we present a new method for representing and recognizing objects, based on invariants of the object's regions. We apply the method to articulated objects in low-resolution, noisy range images. Articulated objects such as a back-hoe can have many degrees of freedom, in addition to the unknown variables of viewpoint. Recognizing such an object in an image can involve a search in a high-dimensional space that involves all these unknown variables. Here, we use invariance to reduce this search space to a manageable size. The low resolution of our range images makes it hard to use common features such as edges to find invariants. We have thus developed a new "featureless驴 method that does not depend on feature detection. Instead of local features, we deal with whole regions of the object. We define a "transform驴 that converts the image into an invariant representation on a grid, based on invariant descriptors of entire regions centered around the grid points. We use these region-based invariants for indexing and recognition. While the focus here is on articulation, the method can be easily applied to other problems such as the occlusion of fixed objects.