IEEE Transactions on Pattern Analysis and Machine Intelligence
Model-based object pose in 25 lines of code
International Journal of Computer Vision - Special issue: image understanding research at the University of Maryland
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Similarity-Based Aspect-Graph Approach to 3D Object Recognition
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Shape Matching and Object Recognition Using Low Distortion Correspondences
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Multiclass Classification with Multi-Prototype Support Vector Machines
The Journal of Machine Learning Research
International Journal of Computer Vision
Towards Multi-View Object Class Detection
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Three-Dimensional Shape Knowledge for Joint Image Segmentation and Pose Tracking
International Journal of Computer Vision
Introduction to Information Retrieval
Introduction to Information Retrieval
A Probabilistic Framework for 3D Visual Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object recognition and full pose registration from a single image for robotic manipulation
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Synthetically trained multi-view object class and viewpoint detection for advanced image retrieval
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Viewpoint-aware object detection and continuous pose estimation
Image and Vision Computing
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Mixture component identification and learning for visual recognition
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
3D2PM - 3d deformable part models
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Multi-component models for object detection
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
How important are "Deformable parts" in the deformable parts model?
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
Learning part-based templates from large collections of 3D shapes
ACM Transactions on Graphics (TOG) - SIGGRAPH 2013 Conference Proceedings
Qualitative pose estimation by discriminative deformable part models
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
Fast detection of multiple textureless 3-D objects
ICVS'13 Proceedings of the 9th international conference on Computer Vision Systems
Hi-index | 0.00 |
Object viewpoint classification aims at predicting an approximate 3D pose of objects in a scene and is receiving increasing attention. State-of-the-art approaches to viewpoint classification use generative models to capture relations between object parts. In this work we propose to use a mixture of holistic templates (e.g. HOG) and discriminative learning for joint viewpoint classification and category detection. Inspired by the work of Felzenszwalb et al 2009, we discriminatively train multiple components simultaneously for each object category. A large number of components are learned in the mixture and they are associated with canonical viewpoints of the object through different levels of supervision, being fully supervised, semi-supervised, or unsupervised. We show that discriminative learning is capable of producing mixture components that directly provide robust viewpoint classification, significantly outperforming the state of the art: we improve the viewpoint accuracy on the Savarese et al 3D Object database from 57% to 74%, and that on the VOC 2006 car database from 73% to 86%. In addition, the mixture-of-templates approach to object viewpoint/pose has a natural extension to the continuous case by discriminatively learning a linear appearance model locally at each discrete view. We evaluate continuous viewpoint estimation on a dataset of everyday objects collected using IMUs for groundtruth annotation: our mixture model shows great promise comparing to a number of baselines including discrete nearest neighbor and linear regression.