Machine Vision and Applications
Multisensor image fusion using the wavelet transform
Graphical Models and Image Processing
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
The steerable pyramid: a flexible architecture for multi-scale derivative computation
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 3)-Volume 3 - Volume 3
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Background-subtraction using contour-based fusion of thermal and visible imagery
Computer Vision and Image Understanding
A Real Time Human Detection System Based on Far Infrared Vision
ICISP '08 Proceedings of the 3rd international conference on Image and Signal Processing
Segmentation-driven image fusion based on alpha-stable modeling of wavelet coefficients
IEEE Transactions on Multimedia
Image Fusion Using Computational Intelligence: A Survey
ICECS '09 Proceedings of the 2009 Second International Conference on Environmental and Computer Science
Esaliency (Extended Saliency): Meaningful Attention Using Stochastic Image Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multimedia Tools and Applications
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
Pedestrian detection and tracking with night vision
IEEE Transactions on Intelligent Transportation Systems
Visible and infrared image registration in man-made environments employing hybrid visual features
Pattern Recognition Letters
Neurocomputing
Hi-index | 0.01 |
This paper proposes a saliency-aware fusion algorithm for integrating infrared (IR) and visible light (ViS) images (or videos) with the aim to enhance the visualization of the latter. Our algorithm involves saliency detection followed by a biased fusion. The goal of the saliency detection is to generate a saliency map for the IR image, highlighting the co-occurrence of high brightness values (''hot spots'') and motion. Markov Random Fields (MRFs) are used to combine these two sources of information. The subsequent fusion step is employed to bias the end result in favor of the ViS image, except when a region shows clear IR saliency, in which case the IR image gains (local) dominance. By doing so, the fused image succeeds in depicting both the salient foreground object (gleaned from the IR image), against as an easily recognizable background as supplied by the ViS image. An evaluation of the proposed saliency detection method indicates improvements in detection accuracy when compared to state-of-the-art alternatives. Moreover, both objective and subjective assessments reveal the effectiveness of the proposed fusion algorithm in terms of visual context enhancement.