Machine Learning
Example-Based Object Detection in Images by Components
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finding faces in cluttered scenes using random labeled graph matching
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Learning to Detect Objects in Images via a Sparse, Part-Based Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Database-Guided Segmentation of Anatomical Structures with Complex Appearance
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Keypoint Recognition Using Randomized Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluating bag-of-visual-words representations in scene classification
Proceedings of the international workshop on Workshop on multimedia information retrieval
Robust Object Detection with Interleaved Categorization and Segmentation
International Journal of Computer Vision
Semantics and CBIR: a medical imaging perspective
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Discriminative cue integration for medical image annotation
Pattern Recognition Letters
Deformations, patches, and discriminative models for automatic annotation of medical radiographs
Pattern Recognition Letters
Automatic alignment of brain MR scout scans using data-adaptive multi-structural model
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part II
Computer Vision and Image Understanding
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Although redundancy reduction is the key for visual coding in the mammalian visual system [1,2], at a higher level, the visual understanding step, a central component of intelligence, achieves high robustness by exploiting redundancies in the images, in order to resolve uncertainty, ambiguity, or contradiction [3,4]. In this paper, an algorithmic framework, Learning Ensembles of Anatomical Patterns (LEAP), is presented for the purpose of automatic localization and parsing of human anatomy from medical images. It achieves high robustness by exploiting statistical redundancies at three levels: the anatomical level, the parts-whole level, and the voxel level in the scale space. The recognition-by-parts intuition is formulated in a more principled way as a spatial ensemble, with added redundancy and less parameter tuning for medical imaging applications. Different use cases were tested using 2D and 3D medical images, including X-ray, CT, and MRI images, for different purposes such as view identication, organ and body parts localization, and MR imaging plane detection. LEAP is shown to significantly outperform existing methods or its "non-redundant" counterparts.