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. An efficient rain removal algorithm using meteorological properties of rain is developed. The relation among the orientation of the raindrops, wind velocity and terminal velocity is established. This relation is used in the estimation of shape-based features of the raindrop. Meteorological property-based features helped to discriminate the rain and non-rain pixels. Most of the prior art algorithms are designed for the videos captured by a static camera. The use of global motion compensation with all rain removal algorithms designed for videos captured by static camera results in better accuracy for videos captured by moving camera. Qualitative and quantitative results confirm that probabilistic temporal, spatiotemporal and meteorological algorithms outperformed other prior art algorithms in terms of the perceptual quality, buffer size, execution delay and system cost. The work presented in this book can find wide application in entertainment industries, transportation, tracking and consumer electronics. Table of Contents: Acknowledgments / Introduction / Analysis of Rain / Dataset and Performance Metrics / Important Rain Detection Algorithms / Probabilistic Approach for Detection and Removal of Rain / Impact of Camera Motion on Detection of Rain / Meteorological Approach for Detection and Removal of Rain from Videos / Conclusion and Scope of Future Work / Bibliography / Authors' Biographies

Autorentext

Sudipta Mukhopadhyay is currently Associate Professor in the Electrical and Electrical Communication Engineering, IIT Kharagpur. He received his B.E. degree from Jadavpur University, Kolkata, in 1988. He received his M.Tech. and Ph.D. degrees from IIT Kanpur in 1991 and 1996 respectively. He has served several companies including TCS, Silicon Automation Systems, GE India Technology Centre and Philips Medical Systems before joining IIT Kharagpur in 2005 as Assistant Professor of Electrical and Electrical Communication Engineering, IIT Kharagpur. In 2013 he become Associate Professor in the same department. He has authored or co-authored more than 70 publications in the field of signal and image processing. He has filed seven patents while working in industry and continued the trend after joining academia. He is a senior member of the Institute of Electrical and Electronics Engineers (IEEE), Member of SPIE and corresponding member of Radiological Society of North America (RSNA). He has done many applied projects sponsored by DIT, Intel and GE Medical Systems IT, USA. He is also founder and director of Perceptivo Imaging Technologies Private Ltd., a company under the guidance of S.T.E.P. IIT Kharagpur. The company specializes in developing innovative software for signal and image processing.Abhishek Tripathi is currently working as Senior Engineer at Uurmi Systems Pvt. Ltd., Hyderabad, India. He received his Ph.D. degree from Indian Institute of Technology Kharagpur, India, in 2012. He received the M.Tech. degree from National Institute of Technology, Kurukshetra, India, in 2008. He received the B.Tech. degree from Uttar Pradesh Technical University, Lucknow, India, in 2006. His research interests include computer vision, image-based rendering, nonlinear image processing, physics-based vision, video post processing, recognition, machine learning and medical imaging.

Inhalt
Acknowledgments.- Introduction.- Analysis of Rain.- Dataset and Performance Metrics.- Important Rain Detection Algorithms.- Probabilistic Approach for Detection and Removal of Rain.- Impact of Camera Motion on Detection of Rain.- Meteorological Approach for Detection and Removal of Rain from Videos.- Conclusion and Scope of Future Work.- Bibliography.- Authors' Biographies .