Chapter 9

Shape Recognition 1

In this chapter, we consider applications that aim to recognize shapes starting from a dictionary or a library. A library is a set of object classes, each object characterizing a parametric object. So, here it is a question of not only finding the objects that compose the scene, and of estimating their parameters, but also of associating them with a precise class. We will see two examples, which concern the choice of a particular distribution to model each of the modes of a light detection and ranging (LIDAR) signal and the choice of a building type according to the geometry of the roof. Our target applications therefore no longer relate only to the detection of objects, but also to pattern recognition.

9.1. Modeling of a LIDAR signal

We describe a model that represents waveforms acquired with LIDAR systems using a sum of parametric functions where the most suited function is chosen, from a given library, for each mode of the LIDAR waveform. Indeed, the latest generation of airborne or spatial full-waveform LIDAR no longer provides an unstructured cloud of three-dimensional points, as was previously the case. Instead for each emitted laser pulse, the received signal, called waveform, is recorded. This signal is digitized at constant frequency: LIDAR waveforms are therefore a one-dimensional sequence of samples (between 60 and 200) of the amplitude of the received signal. Such a sequence represents the progress of the laser beam as it interacts with ...

Get Stochastic Geometry for Image Analysis now with O’Reilly online learning.

O’Reilly members experience live online training, plus books, videos, and digital content from 200+ publishers.