Independent thesis Advanced level (degree of Master (Two Years)), 80 credits / 120 HE credits
A new application of the Supervised Descent Method (SDM)  optimization
algorithm in order to find solutions for modeling a structured
environment such as a warehouse is investigated in this work.
For modeling a structured warehouse, a large number of front-view
images of a warehouse are collected. This work investigates basic
computational elements for building a two-dimensional map of the
warehouse by the SDM algorithm suggesting to use a well-known
technique as feature extraction, i.e. Scale Invariant Feature Transform
(SIFT) . The ground-truths are extracted manually on pillar-beam
intersections from real-world warehouse images. To address the problem
of modeling a warehouse, different modeling scenarios ranging
from a complex to a simple model each with increasing the initial
suggested displacement are investigated. As an important contribution,
this work reports statistics concerning the divergence rate of
SDM (combined with SIFT) performance in all scenarios for both
sides of corridors of the warehouse images. This work has shown
that the SDM transformation method in its original form is not sufficient
enough to be used in general visual object location problems.
2016. , 67 p.