Evaluating preprocessing and interpolation strategies to create moving regions from real-world observations

Abstract

Spatio-temporal data may be used to represent the evolution of real-world objects and phenomena. Such data can be represented in discrete time, which associates spatial information (like position and shape) to time instants, or in continuous time, in which the representation of the evolution of the phenomena is decomposed into slices and interpolation functions are used to estimate the intermediate position and shape at any time. The use of a discrete model may seem more straightforward, but a continuous representation provides potential gains in terms of data management, including in compression and spatio-temporal operations. In this work, we study the use of the continuous model to represent deformable moving regions captured at discrete snapshots. We use a dissimilarity distance-based strategy to select the observations that should be used to define the time slices of the continuous representation, thus transforming data acquired at discrete steps into a continuous model. We also study how the use of geometry simplification algorithms and simplification levels may impact on moving regions interpolation quality. We evaluate our proposals using a dataset composed by thousands of aerial bush-fires images. After applying object simplification and slice decomposition, we use two region interpolation algorithms to generate in-between observations and compare them with geometries representing real images. The results prove the effectiveness of our proposals and their importance in terms of interpolation accuracy.

Benchmark for Automatic Clear-Cut Morphology Detection Methods Derived from Airborne Lidar Data

“…In addition to line simplification, the Douglas-Peucker and Visvalingam-Whyatt algorithms are also suitable for line segmentation [21] or generalization [22]. The Visvalingam-Whyatt algorithm also preserves the geometry of an area while smoothing its contours [23].…”