A statistical method for 3D object detection applied to faces and cars

By Henry Schneiderman, Takeo Kanade. Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662), 2000

1,336 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/3565c5a65842f26091578b9d71d496cc1561239d

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In Computer Vision and Pattern Recognition

Citations in the corpus

Multiple Bernoulli relevance models for image and video annotation (2004, neutral): “Our model differs from traditional object recognition approaches in a number of ways (for example [9, 13, 1, 6, 4, 11].”
Learning a Sparse Representation for Object Detection (2002, methodology): “Orientation invariance can also be achieved via an exhaustive approach as in [18].”
Recognizing action at a distance (2003, background): “Matching under noise and positional uncertainty is often done using histograms of features over image regions [20, 13, 1].”
Face recognition vendor test 2002 (2003, positive): “The distances were obtained either by manual location of the eyes, or from an implementation of the Schneiderman algorithm [14].”
Fast and Robust Classification using Asymmetric AdaBoost and a Detector Cascade (2001, background): “[7]) 1 Face detection is a scanning process, in which a face classifier is evaluated at every scale and location within each image.”
FDDB: A benchmark for face detection in unconstrained settings (2010, background): “• single pose: the head is assumed to be in a single, upright pose (frontal [24, 18, 26] or profile [21]);”
Detecting and reading text in natural scenes (2004, positive): “Its performance on detecting faces [19] compares favorably with other successful algorithms for detecting faces [3, 15, 17, 21, 24].”
3D Object Modeling and Recognition Using Local Affine-Invariant Image Descriptors and Multi-View Spatial Constraints (2006, neutral): “…description of local image appearance; (2) the geometric multi-view constraints associated with the corresponding surface patches; and (3) the algorithms that enforce both photometric and geometric consistency constraints while matching groups of afﬁne regions in modeling and recognition tasks.”
Vehicle Detection and Tracking in Car Video Based on Motion Model (2011, background): “The vast amount of vehicle samples is difficult to model or learn [2].”
Human Detection Based on a Probabilistic Assembly of Robust Part Detectors (2004, methodology): “Our approach has been inspired by recent progress in feature extraction [10,18,19, 20], learning classifiers [15,22] and joint probabilistic modelling [3].”
Mixtures of trees for object recognition (2001, methodology): “[1, 3]) and have been applied to faces [10, 13, 14].”
Predicting human gaze using low-level saliency combined with face detection (2007, background): “There are numerous computer-vision models for face detection with good results ([17, 18, 19, 20]).”
Detection Evolution with Multi-order Contextual Co-occurrence (2013, methodology): “Such methods extract image features in each scan window and classify the features to determine the confidence of the presence of the target object [25, 32, 16].”
Object Detection with Discriminatively Trained Part Based Models (2010, background): “For example, it is common to use multiple templates to encode frontal and side views of faces and cars [36].”