Current vision systems are designed to perform in normal weather condition. However, no one can escape from severe weather conditions. Bad weather reduces scene contrast and visibility, which results in degradation in the performance of various computer vision algorithms such as object tracking, segmentation and recognition. Thus, current vision systems must include some mechanisms that enable them to perform up to the mark in bad weather conditions such as rain and fog. Rain causes the spatial and temporal intensity variations in images or video frames. These intensity changes are due to the random distribution and high velocities of the raindrops. Fog causes low contrast and whiteness in the image and leads to a shift in the color. This book has studied rain and fog from the perspective of vision. The book has two main goals: 1) removal of rain from videos captured by a moving and static camera, 2) removal of the fog from images and videos captured by a moving single uncalibrated camera system. The book begins with a literature survey. Pros and cons of the selected prior art algorithms are described, and a general framework for the development of an efficient rain removal algorithm is explored. Temporal and spatiotemporal properties of rain pixels are analyzed and using these properties, two rain removal algorithms for the videos captured by a static camera are developed. For the removal of rain, temporal and spatiotemporal algorithms require fewer numbers of consecutive frames which reduces buffer size and delay. These algorithms do not assume the shape, size and velocity of raindrops which make it robust to different rain conditions (i.e., heavy rain, light rain and moderate rain). In a practical situation, there is no ground truth available for rain video. Thus, no reference quality metric is very useful in measuring the efficacy of the rain removal algorithms. Temporal variance and spatiotemporal variance are presented in this book as no reference quality metrics
1. Introduction -- 1.1 Motivation --
2. Analysis of rain -- 2.1 Shape of a raindrop -- 2.2 Size of a raindrop -- 2.3 Velocity of raindrop -- 2.4 Rain appearance --
3. Dataset and performance metrics -- 3.1 Rain dataset -- 3.2 Performance metrics -- 3.2.1 Miss, false detection and error -- 3.2.2 Rain removal accuracy -- 3.2.3 Variance --
4. Important rain detection algorithms -- 4.1 Framework -- 4.2 Selected rain detection algorithms -- 4.2.1 Time domain-based approach -- 4.2.2 Frequency domain-based approach --
5. Probabilistic approach for detection and removal of rain -- 5.1 Introduction -- 5.2 Temporal analysis of pixel intensity -- 5.3 Rain removal algorithm -- 5.3.1 Detection of rain -- 5.3.2 Inpainting of rain pixels -- 5.4 Spatiotemporal detection -- 5.5 Database -- 5.6 Simulation and results -- 5.7 Conclusion --
6. Impact of camera motion on detection of rain -- 6.1 Introduction -- 6.2 Rain candidates selection -- 6.3 Global motion parameters -- 6.4 Database -- 6.5 Simulation and results -- 6.6 Conclusion --
7. Meteorological approach for detection and removal of rain from videos -- 7.1 Introduction -- 7.2 Statistical model of raindrop -- 7.2.1 Dynamics of raindrop -- 7.2.2 3D to 2D projection -- 7.3 Detection and removal of rain streaks -- 7.3.1 Rain candidates selection -- 7.3.2 Identifying potential rain candidates -- 7.3.3 Inpainting of rain pixels -- 7.4 Summary of the algorithm -- 7.5 Database -- 7.6 Simulation and results -- 7.7 Conclusion --
8. Conclusion and scope of future work -- Bibliography -- Authors' biographies
Combating bad weather : Part I: Rain removal from video
Rain removal from video
2015
1 Online-Ressource (xiii, 79 Seiten)
Illustrationen
Campusweiter Zugriff (Universität Hannover) - Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots.
Includes bibliographical references (pages 75-78)
Book
Electronic Resource
English
DDC: | 621.367 |
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