Editing Needleman–Wunsch algorithm (section)

==Applications outside bioinformatics==

===Computer stereo vision===
{{Main|Computer stereo vision}}
Stereo matching is an essential step in the process of 3D reconstruction from a pair of stereo images. When images have been rectified, an analogy can be drawn between aligning nucleotide and protein sequences and matching [[pixels]] belonging to [[scan lines]], since both tasks aim at establishing optimal correspondence between two strings of characters. 

Although in many applications image rectification can be performed, e.g. by [[camera resectioning]] or calibration, it is sometimes impossible or impractical since the computational cost of accurate rectification models prohibit their usage in [[Real-time computing|real-time]] applications. Moreover, none of these models is suitable when a camera lens displays unexpected [[distortions]], such as those generated by raindrops, weatherproof covers or dust. By extending the Needleman–Wunsch algorithm, a line in the 'left' image can be associated to a curve in the 'right' image by finding the alignment with the highest score in a three-dimensional array (or matrix). Experiments demonstrated that such extension allows dense pixel matching between unrectified or distorted images.<ref>{{cite conference |author1=Thevenon, J |author2=Martinez-del-Rincon, J |author3=Dieny, R |author4=Nebel, J-C |year=2012|url= https://www.researchgate.net/publication/257928290 |title=Dense pixel matching between unrectified and distorted images using dynamic programming |conference=International Conference on Computer Vision Theory and Applications |location=Rome}}</ref>