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  1. Proceedings of the 1st ACM international workshop on Multimedia analysis for ecological data (MAED '12)
  2. Visibility cameras: where and how to look
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Grass, scrub, trees and random forest
Identification of great apes using gabor features and locality preserving projections
Plant leaves morphological categorization with shared nearest neighbours clustering
Visibility cameras: where and how to look
Texture recognition for frog identification
Multi-organ plant identification
Environmental data extraction from multimedia resources
Event detection in underwater domain by exploiting fish trajectory clustering
Semantic based retrieval system of arctic animal images
An environmental search engine based on interactive visual classification
A visual sensing platform for creating a smarter multi-modal marine monitoring network
Quantitative performance analysis of object detection algorithms on underwater video footage

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Visibility cameras: where and how to look

Content Provider ACM Digital Library
Author Newsam, Shawn Graves, Nathan
Abstract This paper investigates image processing and pattern recognition techniques to estimate light extinction based on the visual content of images from static cameras. We propose two predictive models that incorporate multiple scene regions into the estimation: regression trees and multivariate linear regression. Incorporating multiple regions is important since regions at different distances are effective for estimating light extinction under different visibility regimes. We evaluate our models using a sizable dataset of images and ground truth light extinction values from a visibility camera system in Phoenix, Arizona.
Starting Page 7
Ending Page 12
Page Count 6
File Format PDF
ISBN 9781450315883
DOI 10.1145/2390832.2390835
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2012-11-02
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Regression trees Multivariate linear regression Atmospheric visibility
Content Type Text
Resource Type Article
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