Abstract
This paper proposes a recognition and count method of leaves on the surface of a river to be used in mangrove ecosystem monitoring. Conventionally counting leaves required considerable manual labor for precise monitoring of material flow in the ecosystem. Therefore an efficient counting method was needed. Our method automatically recognizes and 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 acquire 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.
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Tsutsumi, F., Tateda, Y. (2009). A Method to Recognize and Count Leaves on the Surface of a River Using User’s Knowledge about Color of Leaves. In: Chawla, S., et al. New Frontiers in Applied Data Mining. PAKDD 2008. Lecture Notes in Computer Science(), vol 5433. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00399-8_18
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DOI: https://doi.org/10.1007/978-3-642-00399-8_18
Publisher Name: Springer, Berlin, Heidelberg
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