Recovering high dynamic range radiance maps from photographs
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Example-Based Object Detection in Images by Components
IEEE Transactions on Pattern Analysis and Machine Intelligence
Photographic tone reproduction for digital images
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Robust Real-Time Face Detection
International Journal of Computer Vision
Removing photography artifacts using gradient projection and flash-exposure sampling
ACM SIGGRAPH 2005 Papers
Removing photography artifacts using gradient projection and flash-exposure sampling
ACM SIGGRAPH 2005 Papers
VOCUS: A Visual Attention System for Object Detection and Goal-Directed Search (Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence)
2006 Special Issue: Modeling attention to salient proto-objects
Neural Networks
Seam carving for content-aware image resizing
ACM SIGGRAPH 2007 papers
Optimized scale-and-stretch for image resizing
ACM SIGGRAPH Asia 2008 papers
Computational visual attention systems and their cognitive foundations: A survey
ACM Transactions on Applied Perception (TAP)
Attention-based high dynamic range imaging
The Visual Computer: International Journal of Computer Graphics - CGI'2011 Conference
Automatic foveation for video compression using a neurobiological model of visual attention
IEEE Transactions on Image Processing
Saliency maps of high dynamic range images
ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part II
A multi-level depiction method for painterly rendering based on visual perception cue
Multimedia Tools and Applications
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Computational visual attention systems detect regions of interest in images. These systems have a broad range of applications in areas such as computer vision, computational aesthetics, and non-photorealistic rendering. However, almost all the systems to date are designed for low dynamic range (LDR) images and may not be suitable for analyzing saliency in high dynamic range (HDR) images. We propose a novel algorithm for saliency analysis of HDR images that is based on virtual photographs. Taking virtual photographs is the inverse process of generating HDR images from multiple LDR exposures, and the virtual photograph sequence has the capacity to more comprehensively reveal salient content in HDR images. We demonstrate that our method can produce more consistently reliable results than existing methods.