By Topic
Skip to Results

Search Results

You Refined by
Topic: Geoscience  Remove   
2 Results returned
Skip to Results
  • Save this Search
  • Download Citations Disabled
  • Save To Project
  • Email
  • Print
  • Export Results
  • Full text access may be available. Click article title to sign in or learn about subscription options.

    Engineers Engaging Community:Water and Energy

    Oldham, C. ; Crebbin, G. ; Dobbs, S. ; Gaynor, A.
    DOI: 10.2200/S00477ED1V01Y201302ETS019
    Copyright Year: 2013

    Morgan and Claypool eBooks

    Water and energy are fundamental elements of community well-being and economic development, and a key focus of engineering efforts the world over. As such, they offer outstanding opportunities for the development of socially just engineering practices. This work examines the engineering of water and energy systems with a focus on issues of social justice and sustainability. A key theme running through the work is engaging community on water and energy engineering projects: How is this achieved in diverse contexts? And, what can we learn from past failures and successes in water and energy engineering? The book includes a detailed case study of issues involved in the provision of water and energy, among other needs, in a developing and newly independent nation, East Timor. View full abstract»

  • Full text access may be available. Click article title to sign in or learn about subscription options.

    Combating Bad Weather Part I:Rain Removal from Video

    Mukhopadhyay, S. ; Tripathi, A.
    DOI: 10.2200/S00601ED1V01Y201410IVM016
    Copyright Year: 2014

    Morgan and Claypool eBooks

    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 ca era 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 met ics. 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 a d 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 View full abstract»

Skip to Results


Search History is available using your personal IEEE account.