Three-dimensional object recognition from single two-dimensional images
Artificial Intelligence
Line-Drawing Interpretation: Straight Lines and Conic Sections
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Efficiently Computable Metric for Comparing Polygonal Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Direct Least Square Fitting of Ellipses
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Line Edge Map
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Object Recognition in High Clutter Images Using Line Features
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Local Features for Object Class Recognition
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Generic Object Recognition with Boosting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Part-Based Object Retrieval in Cluttered Environment
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiscale Categorical Object Recognition Using Contour Fragments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Technical section: A potential-based generalized cylinder representation
Computers and Graphics
Scale-invariant shape features for recognition of object categories
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Edge curvature and convexity based ellipse detection method
Pattern Recognition
Object class detection: A survey
ACM Computing Surveys (CSUR)
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We propose a new class of descriptors which exhibits the ability to yield meaningful structural descriptions of objects. These descriptors are constructed from two types of image primitives: quadrangles and ellipses. The primitives are extracted from an image based on human cognitive psychology and model local parts of objects. Experiments reveal that these primitives densely cover objects in images. In this regard, structural information of an object can be comprehensively described by these primitives. It is found that a combination of simple spatial relationships between primitives plus a small set of geometrical attributes provide rich and accurate local structural descriptions of objects. Category level object detection of four-legged animals, bicycles, and cars images is demonstrated under scaling, moderate viewpoint variations, and background clutter. Promising results are achieved.