A vision-based system for display interaction
Proceedings of the 23rd British HCI Group Annual Conference on People and Computers: Celebrating People and Technology
Co-occurrence of Intensity and Gradient Features for Object Detection
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
Informative frequent assembled feature for face detection
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Cat face detection with two heterogeneous features
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Face detection with effective feature extraction
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
Fast and accurate pedestrian detection using a cascade of multiple features
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
Unfolding a face: from singular to manifold
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
Feature extraction based on co-occurrence of adjacent local binary patterns
PSIVT'11 Proceedings of the 5th Pacific Rim conference on Advances in Image and Video Technology - Volume Part II
Face recognition technology and its real-world application
PerMIn'12 Proceedings of the First Indo-Japan conference on Perception and Machine Intelligence
Target detection of ISAR data by principal component transform on co-occurrence matrix
Pattern Recognition Letters
Computers in Biology and Medicine
Robotics and Autonomous Systems
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This paper describes an object detection framework that learns the discriminative co-occurrence of multiple features. Feature co-occurrences are automatically found by Sequential Forward Selection at each stage of the boosting process. The selected feature co-occurrences are capable of extracting structural similarities of target objects leading to better performance. The proposed method is a generalization of the framework proposed by Viola and Jones, where each weak classifier depends only on a single feature. Experimental results obtained using four object detectors, for finding faces and three different hand gestures, respectively, show that detectors trained with the proposed algorithm yield consistently higher detection rates than those based on their framework while using the same number of features.