Three-dimensional object recognition from single two-dimensional images
Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Active shape models—their training and application
Computer Vision and Image Understanding
Planar object recognition using projective shape representation
International Journal of Computer Vision
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Human Detection Using a Cascade of Histograms of Oriented Gradients
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
International Journal of Computer Vision
Groups of Adjacent Contour Segments for Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiscale Categorical Object Recognition Using Contour Fragments
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Images to Shape Models for Object Detection
International Journal of Computer Vision
Weakly supervised shape based object detection with particle filter
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Hough Forests for Object Detection, Tracking, and Action Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object detection by contour segment networks
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
A boundary-fragment-model for object detection
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Connected contours: A new contour completion model that respects the closure effect
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
The Shape Boltzmann Machine: A strong model of object shape
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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In this paper, we try to address the challenging problem of combining local shape features to describe long and continuous shape characteristics. To this end, we firstly propose a novel type of local shape feature, namely Active Contour Fragment (ACF), to encode the shape deformation in a local region. An ACF is automatically learnt from the contours of a specific object class and capable to describe the intra-class shape characteristics based on the point distribution model. Secondly, we combine multiple ACFs into a group, namely Active Contour Group (ACG), to describe the long shape characteristics .We model the ACFs in an ACG using an undirected chain model and estimate the parameters of the chain model in a subspace for accelerating the learning and matching processes of ACGs. Finally, we discriminatively train the classifiers based on ACFs and ACGs in a boosting framework for localizing objects as well as delineating object boundaries. Both qualitative and quantitative evaluations show that our approach is capable of describing long shapes and the proposed recognition algorithm achieves promising performance on the public datasets.