Plant Leaf Image Detection Method Using a Midpoint Circle Algorithm for Shape-Based Feature Extraction

Shape-based feature extraction in content-based image retrieval is an important research area at present. An algorithm is presented, based on shape features, to enhance the set of features useful in a leaf identification system

Evaluating hydrology preservation of simplified terrain representations

We present an error metric based on the potential energy of water flow to evaluate the quality of lossy terrain simplification algorithms. Typically, terrain compression algorithms seek to minimize RMS (root mean square) and maximum error. These metrics fail to capture whether a reconstructed terrain preserves the drainage network. A quantitative measurement of how accurately a drainage network captures the hydrology is important for determining the effectiveness of a terrain simplification technique. Having a measurement for testing and comparing different models has the potential to be widely used in numerous applications (flood prevention, erosion measurement, pollutant propagation, etc). In this paper, we transfer the drainage network computed on reconstructed geometry onto the original uncompressed terrain and use our error metric to measure the level of error created by the simplification. We also present a novel terrain simplification algorithm based on the compression of hydrology features. This method and other terrain compression schemes are then compared using our new metric