Flexible Object Models for Category-Level 3D Object Recognition
- 1 June 2007
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
Abstract
Today's category-level object recognition systems largely focus on fronto-parallel views of objects with characteristic texture patterns. To overcome these limitations, we propose a novel framework for visual object recognition where object classes are represented by assemblies of partial surface models (PSMs) obeying loose local geometric constraints. The PSMs themselves are formed of dense, locally rigid assemblies of image features. Since our model only enforces local geometric consistency, both at the level of model parts and at the level of individual features within the parts, it is robust to viewpoint changes and intra-class variability. The proposed approach has been implemented, and it outperforms the state-of-the-art algorithms for object detection and localization recently compared in [14] on the Pascal 2005 VOC Challenge Cars Test 1 data.Keywords
This publication has 12 references indexed in Scilit:
- Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive StudyInternational Journal of Computer Vision, 2006
- 3D Object Modeling and Recognition Using Local Affine-Invariant Image Descriptors and Multi-View Spatial ConstraintsInternational Journal of Computer Vision, 2006
- Towards Multi-View Object Class DetectionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2006
- Histograms of Oriented Gradients for Human DetectionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- A maximum entropy framework for part-based texture and object recognitionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Scale-invariant shape features for recognition of object categoriesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2004
- Convolutional face finder: a neural architecture for fast and robust face detectionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2004
- Object class recognition by unsupervised scale-invariant learningPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Object recognition from local scale-invariant featuresPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1999
- The Statistical Analysis of Discrete DataPublished by Springer Nature ,1989