Balkan Journal of Electrical and Computer Engineering

2147-284X 2147-284X

MUSA YILMAZ

10.17694/bajece.677326

Artificial Intelligence

Yapay Zeka

A Hybrid Framework for Matching Printing Design Files to Product Photos

Kaplan

Alper

YEDITEPE UNIVERSITY

https://orcid.org/0000-0002-0792-7306

Akagunduz

Erdem

CANKAYA UNIVERSITY

04 30 2020

8 2 170 180 01 20 2020 04 16 2020

2013

Balkan Journal of Electrical and Computer Engineering

We propose a real-time image matching framework, which is hybrid in the sense that it uses both hand-crafted features and deep features obtained from a well-tuned deep convolutional network. The matching problem, which we concentrate on, is specific to a certain application, that is, printing design to product photo matching. Printing designs are any kind of template image files, created using a design tool, thus are perfect image signals. However, photographs of a printed product suffer many unwanted effects, such as uncontrolled shooting angle, uncontrolled illumination, occlusions, printing deficiencies in color, camera noise, optic blur, et cetera. For this purpose, we create an image set that includes printing design and corresponding product photo pairs with collaboration of an actual printing facility. Using this image set, we benchmark various hand-crafted and deep features for matching performance and propose a framework in which deep learning is utilized with highest contribution, but without disabling real-time operation using an ordinary desktop computer.

image matching hand-crafted features deep features semantic segmentation product image processing

TUBITAK - TEYDEB

7170364

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