Robot Vision
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
IEEE Transactions on Image Processing
A practical analytic single scattering model for real time rendering
ACM SIGGRAPH 2005 Papers
Stereo Matching with Linear Superposition of Layers
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
An EM/E-MRF algorithm for adaptive model based tracking in extremely poor visibility
Image and Vision Computing
Novel depth cues from light scattering
Image and Vision Computing
Surface Visibility Probabilities in 3D Cluttered Scenes
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
ACM SIGGRAPH ASIA 2008 courses
ACM SIGGRAPH 2009 Courses
Analysis of Rain and Snow in Frequency Space
International Journal of Computer Vision
Mitigation of visibility loss for advanced camera-based driver assistance
IEEE Transactions on Intelligent Transportation Systems
A novel polychromatic model for light dispersion
ICISP'10 Proceedings of the 4th international conference on Image and signal processing
A content-adaptive method for single image dehazing
PCM'10 Proceedings of the Advances in multimedia information processing, and 11th Pacific Rim conference on Multimedia: Part II
Transmission: a new feature for computer vision based smoke detection
AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part I
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Real-time detection of small surface objects using weather effects
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
Estimating meteorological visibility using cameras: a probabilistic model-driven approach
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV
Rain or Snow Detection in Image Sequences Through Use of a Histogram of Orientation of Streaks
International Journal of Computer Vision
Realistic modeling of water droplets for monocular adherent raindrop recognition using Bézier curves
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume part II
Obstacles detection in dust environment with a single image
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part III
A Combined Theory of Defocused Illumination and Global Light Transport
International Journal of Computer Vision
International Journal of Computer Vision
Visibility cameras: where and how to look
Proceedings of the 1st ACM international workshop on Multimedia analysis for ecological data
Quantitative performance analysis of object detection algorithms on underwater video footage
Proceedings of the 1st ACM international workshop on Multimedia analysis for ecological data
Optimized contrast enhancement for real-time image and video dehazing
Journal of Visual Communication and Image Representation
Object class detection: A survey
ACM Computing Surveys (CSUR)
Weighted haze removal method with halo prevention
Journal of Visual Communication and Image Representation
Evaluating the effect of diffuse light on photometric stereo reconstruction
Machine Vision and Applications
Smoke Detection in Video: An Image Separation Approach
International Journal of Computer Vision
Hi-index | 0.00 |
Current vision systems are designed to perform in clear weather. Needless to say, in any outdoor application, there is no escape from “bad” weather. Ultimately, computer vision systems must include mechanisms that enable them to function (even if somewhat less reliably) in the presence of haze, fog, rain, hail and snow.We begin by studying the visual manifestations of different weather conditions. For this, we draw on what is already known about atmospheric optics, and identify effects caused by bad weather that can be turned to our advantage. Since the atmosphere modulates the information carried from a scene point to the observer, it can be viewed as a mechanism of visual information coding. We exploit two fundamental scattering models and develop methods for recovering pertinent scene properties, such as three-dimensional structure, from one or two images taken under poor weather conditions.Next, we model the chromatic effects of the atmospheric scattering and verify it for fog and haze. Based on this chromatic model we derive several geometric constraints on scene color changes caused by varying atmospheric conditions. Finally, using these constraints we develop algorithms for computing fog or haze color, depth segmentation, extracting three-dimensional structure, and recovering “clear day” scene colors, from two or more images taken under different but unknown weather conditions.