%P 249-260
%R MTMT:3210082 10.1007/s10044-017-0619-6
%A  BodnĂĄr PĂŠter
%A  GrĂłsz TamĂĄs
%A  TĂłth LĂĄszlĂł
%A  NyĂşl LĂĄszlĂł GĂĄbor
%T Efficient visual code localization with neural networks
%X The use of computer-readable visual codes became common in our 
everyday life both in industrial environments and for private 
use. The reading process of visual codes consists of two 
steps, namely, localization and data decoding. In this paper 
we examine the localization step of visual codes using 
conventional and deep rectifier neural networks. They are also 
evaluated in the discrete cosine transform domain and shown to 
be efficient, which makes full decompression unnecessary for 
setups involving JPEG images. This approach is also efficient 
from a storage viewpoint and computation cost viewpoint, since 
camera hardware can provide a JPEG stream as output in many 
cases. The use of neural networks implemented on graphics 
processing unit allows real-time automatic code object 
localization. In our earlier studies, the proposed approach 
was evaluated on the most popular code type, quick response 
code, and some other 2D codes as well. Here, we also prove 
that deep rectifier networks are also suitable for 1D barcode 
localization and present extensive evaluation and comparison 
to state-of-the-art approaches.
%L publicatio20911
%I szte
%N 1
%J PATTERN ANALYSIS AND APPLICATIONS
%V 21
%D 2018