LEVEL-BASED CORRESPONDENCE APPROACH TO COMPUTATIONAL STEREO

One fundamental problem in computational stereo reconstruction is correspondence.
Correspondence is the method of detecting the real world object reflections in two
camera views. This research focuses on correspondence, proposing an algorithm to
improve such detection for low quality cameras (webcams) while trying to achieve
real-time image processing.
Correspondence plays an important role in computational stereo reconstruction and it
has a vast spectrum of applicability. This method is useful in other areas such as
structure from motion reconstruction, object detection, tracking in robot vision and
virtual reality. Due to its importance, a correspondence method needs to be accurate
enough to meet the requirement of such fields but it should be less costly and easy to
use and configure, to be accessible by everyone.
By comparing current local correspondence method and discussing their weakness
and strength, this research tries to enhance an algorithm to improve previous works to
achieve fast detection, less costly and acceptable accuracy to meet the requirement of
reconstruction. In this research, the correspondence is divided into four stages. Two
stages of preprocessing which are noise reduction and edge detection have been
compared with respect to different methods available. In the next stage, the feature
detection process is introduced and discussed focusing on possible solutions to reduce
errors created by system or problem occurring in the scene such as occlusion. Lastly,
in the final stage it elaborates different methods of displaying reconstructed result.
Different sets of data are processed based on the steps involved in correspondence and
the results are discussed and compared in detail. The finding shows how this system
can achieve high speed and acceptable outcome despite of poor quality input. As a
conclusion, some possible improvements are proposed based on ultimate outcome.