Object recognition by computer: the role of geometric constraints
Object recognition by computer: the role of geometric constraints
International Journal of Computer Vision
Context-free attentional operators: the generalized symmetry transform
International Journal of Computer Vision - Special issue on qualitative vision
Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Edge Detection and Ridge Detection with Automatic Scale Selection
International Journal of Computer Vision
Twenty Years of Document Image Analysis in PAMI
IEEE Transactions on Pattern Analysis and Machine Intelligence
A computational model for visual selection
Neural Computation
Algorithms for Defining Visual Regions-of-Interest: Comparison with Eye Fixations
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: Part II
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
2d Object Detection and Recognition: Models, Algorithms, and Networks
2d Object Detection and Recognition: Models, Algorithms, and Networks
Contextual Priming for Object Detection
International Journal of Computer Vision
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
Locating objects using the Hausdorff distance
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Multi-Feature Hierarchical Template Matching Using Distance Transforms
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
A Neural Network Architecture for Visual Selection
Neural Computation
Automatic target recognition by matching oriented edge pixels
IEEE Transactions on Image Processing
A Hierarchy of Support Vector Machines for Pattern Detection
The Journal of Machine Learning Research
A Bayesian, Exemplar-Based Approach to Hierarchical Shape Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast and cheap object recognition by linear combination of views
Proceedings of the 6th ACM international conference on Image and video retrieval
POP: Patchwork of Parts Models for Object Recognition
International Journal of Computer Vision
Learning an Alphabet of Shape and Appearance for Multi-Class Object Detection
International Journal of Computer Vision
Object detection using spatial histogram features
Image and Vision Computing
Lift-button detection and recognition for service robot in buildings
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A coarse-to-fine taxonomy of constellations for fast multi-class object detection
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Identifying a critical threat to privacy through automatic image classification
Proceedings of the first ACM conference on Data and application security and privacy
Recursive Compositional Models for Vision: Description and Review of Recent Work
Journal of Mathematical Imaging and Vision
Shape matching using a binary search tree structure of weak classifiers
Pattern Recognition
Matching flexible polygons to fields of corners extracted from images
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
Hough regions for joining instance localization and segmentation
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
| Hi-index | 0.14 |
Multiclass shape detection, in the sense of recognizing and localizing instances from multiple shape classes, is formulated as a two-step process in which local indexing primes global interpretation. During indexing a list of instantiations (shape identities and poses) is compiled, constrained only by no missed detections at the expense of false positives. Global information, such as expected relationships among poses, is incorporated afterward to remove ambiguities. This division is motivated by computational efficiency. In addition, indexing itself is organized as a coarse-to-fine search simultaneously in class and pose. This search can be interpreted as successive approximations to likelihood ratio tests arising from a simple ("naive Bayes驴) statistical model for the edge maps extracted from the original images. The key to constructing efficient "hypothesis tests驴 for multiple classes and poses is local ORing; in particular, spread edges provide imprecise but common and locally invariant features. Natural tradeoffs then emerge between discrimination and the pattern of spreading. These are analyzed mathematically within the model-based framework and the whole procedure is illustrated by experiments in reading license plates.