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12.4 Image and Signal Processing

Hampo and Marko (1992) were among the first people from industry to consider using GP for signal processing. They evolved algorithms for pre-processing electronic motor vehicle signals for possible use in engine monitoring and control.

Several applications of GP for image processing have been for military uses. For example, Tackett (1993) evolved algorithms to find tanks in infrared images. Howard, Roberts, and Brankin (1999);  Howard, Roberts, and Ryan ( 2006) evolved programs to pick out ships from SAR radar mounted on satellites in space and to locate ground vehicles from airborne photo reconnaissance. They also used GP to process surveillance data for civilian purposes, such as predicting motorway traffic jams from subsurface traffic speed measurements (Howard and Roberts2004).

Using satellite SAR radar, Daida, Hommes, Bersano-Begey, Ross, and Vesecky (1996) evolved algorithms to find features in polar sea ice. Optical satellite images can also be used for environmental studies (Chami and Robilliard2002) and for prospecting for valuable minerals (Ross, Gualtieri, Fueten, and Budkewitsch2005).

Alcazar used GP to find recurrent filters (including artificial neural networks (Esparcia-Alcazar and Sharman1996)) for one-dimensional electronic signals (Sharman and Esparcia-Alcazar1993). Local search (simulated annealing or gradient descent) can be used to adjust or fine-tune "constant" values within the structure created by genetic search (Smart and Zhang2004).

? have used GP to preprocess images, particularly of human faces, to find regions of interest for subsequent analysis. See also (?).

Zhang has been particularly active at evolving programs with GP to visually classify objects (typically coins) (?). He has also applied GP to human speech (?).

"Parisian GP" is a system in which the image processing task is split across a swarm of evolving agents ("flies"). In (Louchet2001Louchet, Guyon, Lesot, and Boumaza2002) the flies reconstruct three dimensions from pairs of stereo images. For example, in (Louchet2001), as the flies buzz around in three dimensions their position is projected onto the left and right of a pair of stereo images. The fitness function tries to minimise the discrepancy between the two images, thus encouraging the flies to settle on visible surfaces in the 3-D space. So, the true 3-D space is inferred from pairs of 2-D images taken from slightly different positions.

While the likes of Google have effectively indexed the written word, for speech and pictures indexing has been much less effective. One area where GP might be applied is in the automatic indexing of images. Some initial steps in this direction are given in (Theiler, Harvey, Brumby, Szymanski, Alferink, Perkins, Porter, and Bloch1999).

To some extent, extracting text from images (OCR) can be done fairly reliably, and the accuracy rate on well formed letters and digits is close to 100%. However, many interesting cases remain (Cilibrasi and Vitanyi2005) such as Arabic (Klassen and Heywood2002) and oriental languages, handwriting (De Stefano, Cioppa, and Marcelli2002Gagne and Parizeau2006Krawiec2004Teredesai and Govindaraju2005) (such as the MNIST examples), other texts (Rivero, nal, Dorado, and Pazos2004) and musical scores (Quintana, Poli, and Claridge2006).

The scope for applications of GP to image and signal processing is almost unbounded. A promising area is medical imaging (Poli1996b). GP image techniques can also be used with sonar signals (Martin2006). Off-line work on images includes security and verification. For example, ? have used GP to detect image watermarks which have been tampered with. Recent work by Zhang has incorporated multi-objective fitness into GP image processing (?).

In 1999 Poli, Cagnoni and others founded the annual European Workshop on Evolutionary Computation in Image Analysis and Signal Processing (EvoIASP). EvoIASP is held every year with the EuroGP. Whilst not solely dedicated to GP, many GP applications have been presented at EvoIASP.


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