The nature of statistical learning theory
The nature of statistical learning theory
Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape quantization and recognition with randomized trees
Neural Computation
Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A computational model for visual selection
Neural Computation
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
2d Object Detection and Recognition: Models, Algorithms, and Networks
2d Object Detection and Recognition: Models, Algorithms, and Networks
A Probabilistic Approach to Object Recognition Using Local Photometry and Global Geometry
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Finding faces in cluttered scenes using random labeled graph matching
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
A statistical model for computer recognition of sequences of handwritten digits, with applications to zip codes
A Bayesian Approach to Unsupervised One-Shot Learning of Object Categories
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Object Detection Using the Statistics of Parts
International Journal of Computer Vision
Robust Real-Time Face Detection
International Journal of Computer Vision
A Coarse-to-Fine Strategy for Multiclass Shape Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining Top-Down and Bottom-Up Segmentation
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 4 - Volume 04
Image Parsing: Unifying Segmentation, Detection, and Recognition
International Journal of Computer Vision
Spatial Priors for Part-Based Recognition Using Statistical Models
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Part-Based Statistical Models for Object Classification and Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Statistical M-Estimation and Consistency in Large Deformable Models for Image Warping
Journal of Mathematical Imaging and Vision
Weakly supervised learning of component-based hierarchical model for object detection
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
Learning Active Basis Model for Object Detection and Recognition
International Journal of Computer Vision
A Numerical Study of the Bottom-Up and Top-Down Inference Processes in And-Or Graphs
International Journal of Computer Vision
Context, Computation, and Optimal ROC Performance in Hierarchical Models
International Journal of Computer Vision
Technical Section: Neural network-based symbol recognition using a few labeled samples
Computers and Graphics
Statistical methods for data mining and knowledge discovery
ICFCA'10 Proceedings of the 8th international conference on Formal Concept Analysis
Object recognition using discriminative parts
Computer Vision and Image Understanding
Evaluating a color-based active basis model for object recognition
Computer Vision and Image Understanding
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Learning part-based templates from large collections of 3D shapes
ACM Transactions on Graphics (TOG) - SIGGRAPH 2013 Conference Proceedings
Visual object detection with deformable part models
Communications of the ACM
Hough-based tracking of non-rigid objects
Computer Vision and Image Understanding
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We formulate a deformable template model for objects with an efficient mechanism for computation and parameter estimation. The data consists of binary oriented edge features, robust to photometric variation and small local deformations. The template is defined in terms of probability arrays for each edge type. A primary contribution of this paper is the definition of the instantiation of an object in terms of shifts of a moderate number local submodels--parts--which are subsequently recombined using a patchwork operation, to define a coherent statistical model of the data. Object classes are modeled as mixtures of patchwork of parts POP models that are discovered sequentially as more class data is observed. We define the notion of the support associated to an instantiation, and use this to formulate statistical models for multi-object configurations including possible occlusions. All decisions on the labeling of the objects in the image are based on comparing likelihoods. The combination of a deformable model with an efficient estimation procedure yields competitive results in a variety of applications with very small training sets, without need to train decision boundaries--only data from the class being trained is used. Experiments are presented on the MNIST database, reading zipcodes, and face detection.