Learning invariance from transformation sequences
Neural Computation
Recognition by Linear Combinations of Models
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part I
Geometric invariance in computer vision
Geometric invariance in computer vision
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
The nature of statistical learning theory
The nature of statistical learning theory
Face Recognition: The Problem of Compensating for Changes in Illumination Direction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Linear Object Classes and Image Synthesis From a Single Example Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape quantization and recognition with randomized trees
Neural Computation
Three-dimensional object recognition based on the combination of views
Object recognition in man, monkey, and machine
Computation of pattern invariance in brain-like structures
Neural Networks - Special issue on organisation of computation in brain-like systems
Perceptual Organization and Visual Recognition
Perceptual Organization and Visual Recognition
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
An Affine Invariant Interest Point Detector
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Learning a Sparse Representation for Object Detection
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Probabilistic Modeling of Local Appearance and Spatial Relationships for Object Recognition
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Face Recognition Under Varying Pose
Face Recognition Under Varying Pose
Object Recognition with Informative Features and Linear Classification
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Object recognition by artificial cortical maps
Neural Networks
Computer Vision and Image Understanding
A comparison of features in parts-based object recognition hierarchies
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Invariant object recognition and pose estimation with slow feature analysis
Neural Computation
Class-Specific sparse coding for learning of object representations
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
| Hi-index | 0.00 |
In performing recognition, the visual system shows a remarkable capacity to distinguish between significant and immaterial image changes, to learn from examples to recognize new classes of objects, and to generalize from known to novel objects. Here we focus on one aspect of this problem, the ability to recognize novel objects from different viewing directions. This problem of view-invariant recognition is difficult because the image of an object seen from a novel viewing direction can be substantially different from all previously seen images of the same object.We describe an approach to view-invariant recognition that uses extended features to generalize across changes in viewing directions. Extended features are equivalence classes of informative image fragments, which represent object parts under different viewing conditions. This representation is extracted during learning from images of moving objects, and it allows the visual system to generalize from a single view of a novel object, and to compensate for large changes in the viewing direction, without using three-dimensional information. We describe the model, its implementation and performance on natural face images, compare it to alternative approaches, discuss its biological plausibility, and its extension to other aspects of visual recognition. The results of the study suggest that the capacity of the recognition system to generalize to novel conditions in an efficient and flexible manner depends on the ongoing extraction of different families of informative features, acquired for different tasks and different object classes.