In contrast to the extensively researched modeling of plant architecture, the modeling of plant organs largely remains an open problem. We propose a method for modeling lobed leaves. This method extends the concept of sweeps to branched skeletons. The input of the model is a 2D leaf silhouette, which can be defined interactively or derived from a scanned leaf image. The algorithm computes the skeleton (medial axis) of the leaf and approximates it using spline curves interconnected into a branching structure (sticky splines). The leaf surface is then constructed by sweeping a generating curve along these splines. The orientation of the generating curve is adjusted to properly capture the shape of the leaf blade near the extremities and branching points of the skeleton, and to avoid self-intersections of the surface. The leaf model can be interactively modified by editing the shape of the silhouette and the skeleton. It can be further manipulated in 3D using functions that control turning, bending, and twisting of each lobe.

This contest involved the running and evaluation of computer vision and pattern recognition techniques on different data sets with known groundwidth. The contest included three areas; binary shape recognition, symbol recognition and image flow estimation. A package was made available for each area. Each package contained either real images with manual groundtruth or programs to generate data sets of ideal as well as noisy images with known groundtruth. They also contained programs to evaluate the results of an algorithm according to the given groundtruth. These evaluation criteria included the generation of confusion matrices, computation of the misdetection and false alarm rates and other performance measures suitable for the problems. The paper summarizes the data generation for each area and experimental results for a total of six participating algorithms.

Template matching is rarely used for symbol recognition in practice because of the variation in scale, orientation, and line thickness and position found in actual images. An answer to this is to use an adaptable template, one that can be scaled and rotated easily. Here the use of such a template, based on vector representation of symbols, is explored. Results from handprinted digits and electronic symbols are presented.