C
C. Papageorgiou
Researcher at Massachusetts Institute of Technology
Publications - 11
Citations - 5326
C. Papageorgiou is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Object detection & Face detection. The author has an hindex of 10, co-authored 11 publications receiving 5098 citations.
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Proceedings ArticleDOI
A general framework for object detection
TL;DR: A general trainable framework for object detection in static images of cluttered scenes based on a wavelet representation of an object class derived from a statistical analysis of the class instances and a motion-based extension to enhance the performance of the detection algorithm over video sequences is presented.
Journal ArticleDOI
A Trainable System for Object Detection
C. Papageorgiou,Tomaso Poggio +1 more
TL;DR: A general, trainable system for object detection in unconstrained, cluttered scenes that derives much of its power from a representation that describes an object class in terms of an overcomplete dictionary of local, oriented, multiscale intensity differences between adjacent regions, efficiently computable as a Haar wavelet transform.
Journal ArticleDOI
Example-based object detection in images by components
TL;DR: Results suggest that the improvement in performance is due to the component-based approach and the ACC data classification architecture, which is capable of locating partially occluded views of people and people whose body parts have little contrast with the background.
Proceedings ArticleDOI
Pedestrian detection using wavelet templates
TL;DR: This paper presents a trainable object detection architecture that is applied to detecting people in static images of cluttered scenes and shows how the invariant properties and computational efficiency of the wavelet template make it an effective tool for object detection.
Proceedings ArticleDOI
Trainable pedestrian detection
C. Papageorgiou,Tomaso Poggio +1 more
TL;DR: This paper presents work on a general object detection system that can be trained to detect different types of objects and discusses an extension to the system that takes advantage of dynamical information when processing video sequences to enhance accuracy.