Adaptive histogram equalization and its variations
Computer Vision, Graphics, and Image Processing
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
International Journal of Computer Vision
Fast Human Detection Using a Cascade of Histograms of Oriented Gradients
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
LabelMe: A Database and Web-Based Tool for Image Annotation
International Journal of Computer Vision
Learning to Detect a Salient Object
IEEE Transactions on Pattern Analysis and Machine Intelligence
Global contrast based salient region detection
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
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Using an object detector over a whole image can require significant processing time. This is so since the majority of the images, in common scenarios, is composed of non-trivial amounts of background information, such as sky, ground and water. To alleviate this computational load, image search space reduction methods can make the detection procedure focus on more distinctive image regions. In this sense, we propose here the use of saliency information to organize regions based on their probability of containing objects. The proposed method was grounded on a multi-scale spectral residue (MSR) analysis for saliency detection. For better search space reduction, our method enables fine control of search scale, presents more robustness to variations on saliency intensity along an object length, and also a straightforward way to control the balance between search space reduction and false negatives, both being a consequence of region selection. MSR was capable of making object detection three to five times faster compared to the same detector without MSR. A thorough analysis was accomplished to demonstrate the effectiveness of the proposed method using a custom LabelMe dataset of person images, and also a Pascal VOC 2007 dataset, containing several distinct object classes.