Ev ve Ofis taşıma sektöründe lider olmak.Teknolojiyi takip ederek bunu müşteri menuniyeti amacı için kullanmak.Sektörde marka olmak.
İstanbul evden eve nakliyat
Misyonumuz sayesinde edindiğimiz müşteri memnuniyeti ve güven ile müşterilerimizin bizi tavsiye etmelerini sağlamak.
Learning Integrated Visual Databases
for
Image Exploitation
SCIENTIFIC GOALS
There is a critical need for robust high performance automated
systems that can recognize objects in reconnaissance imagery
acquired under dynamically changing conditions and for systems
that can efficiently extract information from enormous image
databases. Our research addresses two interrelated problems
with the effectiveness and efficiency of automated/semi-automated
techniques for image understanding.
First, the lack of robustness in algorithms and systems for
object recognition with changing environments and extended
operating conditions.
Second, the lack of scalable intelligent strategies for quickly
extracting meaningful information from enormous, dynamically
changing image databases.
Our research is aimed at developing IU algorithms and systems
that have performance prediction and learning capabilities
and that can improve their performance with experience, in
terms of quality of results, processing speed and matching
with the user's perception.
The overall scientific goal of our research is to demonstrate
that the conjunction of recognition, learning and context
and content-based retrieval (CCBR) is necessary and sufficient
for reliable IU. We believe that for the development of robust
and reliable IU systems we need a new generation of IU research
that integrates target recognition, learning and CCBR technologies.
Each alone or any combination of two is not sufficient to
develop reliable IU systems operating in dynamic real-world
environments. We must combine them in an integrated system
to develop the science for image recognition.
|