14 Underwater Multimodal Survey: Merging Optical and Acoustic Data
227
form, etc.). Nevertheless, there are two main families of methods to build a pattern
recognition system: structural methods and statistical methods.
The first application related to our project is red coral monitoring. From its tentacular form, we are aiming at developing a structural approach which uses the objects’
median (skeleton) axis as form descriptor.
There are many applications for 2D and 3D objects’ skeletons in image processing
(encoding, compression, . . .) and in vision in general (Merad et al. 2006; Thome et al.
2008). Indeed, we retrieve in the object’s skeleton its topological structure; moreover,
most of the information which are contained in the form’s silhouette can be retrieved
in the skeleton. Another advantage that cannot be denied is the fact that, by nature,
skeleton have a graph structure. Hence, after the encoding of the coral’s form under
a graph structure through a 3D skeletisation process, we are going to use powerful
skills from the graph theory so as to complete the matching (Shokoufandeh et al.
2005).
The second application consists in archaeologist objects recognition on an underwater site. Because of the a priori information we have, such as the type of the object
(amphora, bottle, etc . . .), we will choose a statistic recognition method. (Baluja and
Rowley 2005)
In an environment like wreck in 40 m deep, the vision conditions are strongly
damaged. It is then necessary to free ourselves from preliminary treatments such as
edge detection, line detection and other structural primitive.
Recent works showed the interest in using learning methods like adaboost, see
(Freund and Schapire 1997). The advantage of this king of methods is to only need
low level descriptors such as pixels’ (Baluja and Rowley 2005). LBP’s (Ahonen et al.
2006), Haar’s (Viola and Jones 2001), etc.
For our problem, we are going to implement an Adaboost classifier; SIFT (Lowe
2004) and/or SURF (Bay et al. 2008) will stand for weak learners. We will check the
relevancy of this method by comparing its results to other standard classifiers’.
14.1.3 Merging Optical and Acoustic Data: State of Art
Related Work Optic and acoustic data fusion is an extremely promising technique
for mapping underwater objects that has been receiving increasing attention over the
past few years (Shortis et al. 2009). Generally, bathymetry obtained using underwater
sonar is performed at a certain distance from the measured object (generally the
seabed) and the obtained cloud point density is rather low in comparison with the
one obtained by optical means.
Since photogrammetry requires working on a large scale, it therefore makes it
possible to obtain dense 3D models. The merging of photogrammetric and acoustic
models is similar to the fusion of data gathered by a terrestrial laser and photogrammetry. The fusion of optical and acoustic data involves the fusion of 3D models of
very different densities—a task which requires specific precautions (Drap and Long
2005; Hurtós et al. 2010).
227
form, etc.). Nevertheless, there are two main families of methods to build a pattern
recognition system: structural methods and statistical methods.
The first application related to our project is red coral monitoring. From its tentacular form, we are aiming at developing a structural approach which uses the objects’
median (skeleton) axis as form descriptor.
There are many applications for 2D and 3D objects’ skeletons in image processing
(encoding, compression, . . .) and in vision in general (Merad et al. 2006; Thome et al.
2008). Indeed, we retrieve in the object’s skeleton its topological structure; moreover,
most of the information which are contained in the form’s silhouette can be retrieved
in the skeleton. Another advantage that cannot be denied is the fact that, by nature,
skeleton have a graph structure. Hence, after the encoding of the coral’s form under
a graph structure through a 3D skeletisation process, we are going to use powerful
skills from the graph theory so as to complete the matching (Shokoufandeh et al.
2005).
The second application consists in archaeologist objects recognition on an underwater site. Because of the a priori information we have, such as the type of the object
(amphora, bottle, etc . . .), we will choose a statistic recognition method. (Baluja and
Rowley 2005)
In an environment like wreck in 40 m deep, the vision conditions are strongly
damaged. It is then necessary to free ourselves from preliminary treatments such as
edge detection, line detection and other structural primitive.
Recent works showed the interest in using learning methods like adaboost, see
(Freund and Schapire 1997). The advantage of this king of methods is to only need
low level descriptors such as pixels’ (Baluja and Rowley 2005). LBP’s (Ahonen et al.
2006), Haar’s (Viola and Jones 2001), etc.
For our problem, we are going to implement an Adaboost classifier; SIFT (Lowe
2004) and/or SURF (Bay et al. 2008) will stand for weak learners. We will check the
relevancy of this method by comparing its results to other standard classifiers’.
14.1.3 Merging Optical and Acoustic Data: State of Art
Related Work Optic and acoustic data fusion is an extremely promising technique
for mapping underwater objects that has been receiving increasing attention over the
past few years (Shortis et al. 2009). Generally, bathymetry obtained using underwater
sonar is performed at a certain distance from the measured object (generally the
seabed) and the obtained cloud point density is rather low in comparison with the
one obtained by optical means.
Since photogrammetry requires working on a large scale, it therefore makes it
possible to obtain dense 3D models. The merging of photogrammetric and acoustic
models is similar to the fusion of data gathered by a terrestrial laser and photogrammetry. The fusion of optical and acoustic data involves the fusion of 3D models of
very different densities—a task which requires specific precautions (Drap and Long
2005; Hurtós et al. 2010).