sWe show that we can optimally represent the set of 2D images producedsby the point features of a rigid 3D model as two lines in twoshigh-dimensional spaces. We then decribe a working recognition systemsin which we represent these spaces discretely in a hash table. We cansaccess this table at run time to find all the groups of model featuressthat could match a group of image features, accounting for the effectssof sensing error. We also use this representation of a model's imagessto demonstrate significant new limitations of two other approaches tosrecognition: invariants, and non-accidental properties
Abstract. We address the problem of 3D object recognition from a sin-gle 2D image using a model data...
Visual object recognition requires the matching of an image with a set of models stored in memory. I...
This paper presents a method for extracting distinctive invariant features from images that can be u...
We show that we can optimally represent the set of 2D images produced by the point features of a r...
In model-based vision, there are a huge number of possible ways to match model features to image f...
Techniques, suitable for parallel implementation, for robust 2D model-based object recognition in ...
This thesis presents representations and corresponding algorithms which learn models to recognize ob...
International audienceAs 3D data is getting more popular, techniques for retrieving a particular 3D ...
Attributed Relational Graph (ARG) is a powerful representation for model based object recognition du...
Geometric hashing (GH) and partial pose clustering are well-known algorithms for pattern recognition...
This paper introduces a novel method, which utilizes local appearance descriptions in a more efficie...
This thesis presents a novel approach to increasing the accuracy and robustness of 3D scene geometry...
Building robust recognition systems requires a careful understanding of the effects of error in sens...
International audienceWe address the problem of 3D object recognition from a single 2D image using a...
This paper presents a model-based vision recognition engine for planar contours that are scale invar...
Abstract. We address the problem of 3D object recognition from a sin-gle 2D image using a model data...
Visual object recognition requires the matching of an image with a set of models stored in memory. I...
This paper presents a method for extracting distinctive invariant features from images that can be u...
We show that we can optimally represent the set of 2D images produced by the point features of a r...
In model-based vision, there are a huge number of possible ways to match model features to image f...
Techniques, suitable for parallel implementation, for robust 2D model-based object recognition in ...
This thesis presents representations and corresponding algorithms which learn models to recognize ob...
International audienceAs 3D data is getting more popular, techniques for retrieving a particular 3D ...
Attributed Relational Graph (ARG) is a powerful representation for model based object recognition du...
Geometric hashing (GH) and partial pose clustering are well-known algorithms for pattern recognition...
This paper introduces a novel method, which utilizes local appearance descriptions in a more efficie...
This thesis presents a novel approach to increasing the accuracy and robustness of 3D scene geometry...
Building robust recognition systems requires a careful understanding of the effects of error in sens...
International audienceWe address the problem of 3D object recognition from a single 2D image using a...
This paper presents a model-based vision recognition engine for planar contours that are scale invar...
Abstract. We address the problem of 3D object recognition from a sin-gle 2D image using a model data...
Visual object recognition requires the matching of an image with a set of models stored in memory. I...
This paper presents a method for extracting distinctive invariant features from images that can be u...