Identifying objects in digital images is not always easy, even for human beings.
Developing a computer algorithm for it is a great challenge. Hovewer, in the past
decades many work has been done in this field, which could help object recognition
happen in certain situations.
Objects can be identified if we have a reference picture of them, and we use the
features in this reference to find similarities. In other cases we may only have statistical
data about the objects' features, although this does not mean that the their recognition in
other pictures is not possible.
Robustness is a key to every artificial intelligence algorithm, and it has a huge
significance in the field of image processing, because two pictures showing almost the
same scene can differ in many ways.
One feature based recognition technique which is robust enough is Scale
Invariant Feature Transform (SIFT). In this work I will give details about how it works.
The system I have developed is also based on this method.
I will also provide details about my work in the field of statistical image
processing. I have developed a method for localizing price tags in pictures taken in
I implemented a software which demonstrates the use of the aformentioned
methods, with the help of the OpenCV library. Comparing this program to other similar
software have shown that my results are similar, and in some fields they are even better.