Count method of leaves on the surface of a river using interactive color indication of coloring picture style

Authors:
Fujio Tsutsumi;Yutaka Tateda
Affiliations:
Central Research Institute of Electric Power Industry, Tokyo, Japan;Central Research Institute of Electric Power Industry, Chiba, Japan
Venue:
EuroIMSA '08 Proceedings of the IASTED International Conference on Internet and Multimedia Systems and Applications
Year:
2008

Citing 8
Cited 0

Interactive machine learning

Proceedings of the 8th international conference on Intelligent user interfaces
Robust tracking of human body parts for collaborative human computer interaction

Computer Vision and Image Understanding
Recognizing Plant Species by Leaf Shapes-A Case Study of the Acer Family

ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Detecting Pedestrians Using Patterns of Motion and Appearance

International Journal of Computer Vision
Approximate Bayesian Multibody Tracking

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Framework for Model-Based Tracking Experiments in Image Sequences

International Journal of Computer Vision
Detection and Tracking of Multiple, Partially Occluded Humans by Bayesian Combination of Edgelet based Part Detectors

International Journal of Computer Vision
Robust real-time vision for a personal service robot

Computer Vision and Image Understanding

Quantified Score

Hi-index	0.00

Visualization

Abstract

We propose a count method for leaves on the surface of a river to be used in ecosystem monitoring. Since conventional count methods require considerable manual labor for precise monitoring of material flow in the ecosystem, an efficient counting method was needed. Our method automatically counts the number of floating leaves in recorded video using color and motion features. The color feature is represented by 3 dimensional histogram of a color space. We have developed a user interface based on the interactive machine learning model to extract the color feature from video images. The user can easily produce a huge number of sample data to extract the color feature by the user interface in the same way as coloring a picture. For the motion feature, speed and acceleration of the targets are used. The counting method proposed in this paper has been applied to three videos (total five hours) which recorded about 20,000 leaves, and high recall and precision rates of 96% and 94%, respectively, have been achieved.