Title
A General Framework for Object Detection
Abstract
This paper presents a general trainable framework for object detection in static images of cluttered scenes. The detection technique we develop is based on a wavelet representation of an object class derived from a statistical analysis of the class instances. By learning an object class in terms of a subset of an overcomplete dictionary of wavelet basis functions, we derive a compact representation of an object class which is used as an input to a support vector machine classifier. This representation overcomes both the problem of in-class variability and provides a low false detection rate in unconstrained environments.We demonstr ate the capabilities of the technique in two domains whose inherent information content differs significantly. The first system is face detection and the second is the domain of people which, in contrast to faces, vary greatly in color, texture, and patterns. Unlike previous approaches, this system learns from examples and does not rely on any a priori (hand-crafted) models or motion-based segmentation. The paper also presents a motion-based extension to enhance the performance of the detection algorithm over video sequences. The results presented here suggest that this architecture may well be quite general.
Year
DOI
Venue
1998
10.1109/ICCV.1998.710772
ICCV
Keywords
Field
DocType
learning (artificial intelligence),object detection,object recognition,cluttered scenes,object detection,static images,trainable framework,unconstrained environments,wavelet representation
Computer vision,Object detection,Viola–Jones object detection framework,Object-class detection,Pattern recognition,Computer science,Segmentation,A priori and a posteriori,Artificial intelligence,Face detection,Wavelet,Cognitive neuroscience of visual object recognition
Conference
Volume
Issue
ISBN
1998
1
81-7319-221-9
Citations 
PageRank 
References 
482
101.99
11
Authors
3
Search Limit
100482
Name
Order
Citations
PageRank
Constantine Papageorgiou11897403.34
Michael Oren21250265.96
Tomaso Poggio3134883380.01