Sick Ag

Pascal Schüler

Sascha Burghardt

Dietram Rinklin

Romain Müller

Marcel Hampf

Ulrich Zwölfer

Dirk Strohmeier

Thorsten Falk

Christofer Roser

Stephan Walter

Fredrik Olsson

Frank Schütz

Steffen Zopf

Stefan Walter

A method of locating code image zones in an output image of a code bearing object (), wherein first candidates for code image zones are determined in a first segmentation process using a process of classical image processing without machine learning and second candidates for code image zones are determined in a second segmentation process using machine learning, with the first and second candidates being fused to locate the code image zones.

Locating code image zones in an image of a code bearing object

A method is proposed of reading an optical code that encodes a message that has a character chain having a plurality of characters, said method comprising the steps: recording image data having the optical code; evaluating the image data with a reading of the optical code; comparing the read characters of the message with a scheme that contains a fixed character for at least one position of the message that is expected at this position in codes to be read; and recognizing an incorrectly read message and/or correcting the read message with reference to the comparison with the scheme. The scheme contains a variable character that is expected at this position in codes to be read for at least one position of the message, with a variable character being set to a partial range of the possible characters, but not to a fixed character.

Reading optical codes

A method of automatically setting up a code reading device that has an image sensor and a control and evaluation unit, wherein an example image of an example object arranged in the field of view of the image sensor and having an example code is recorded by the image sensor and at least one recording parameter and/or at least one decoding parameter for the operation of the code reading device is set with reference to an evaluation of the example image. In this respect, further example images are generated from the example image by calculational variation and the at least one recording parameter and/or the at least one decoding parameter is/are set with reference to an evaluation of the further example images.

Method of automatically setting up a code reading device and camera-based code reading device

A method for determining a module size of an optical code (), wherein image data with the code () are detected, a brightness distribution is determined from the image data, and the module size is determined from the brightness distribution. The brightness distribution for example is a greyscale histogram.

Determining a module size of an optical code

An optoelectronic code reader () having at least one light receiving element () for generating image data from reception light and an evaluation unit () with a classifier () being implemented in the evaluation unit () for assigning code information to code regions () of the image date, wherein the classifier () is configured for machine learning and is trained by means of supervised learning based on codes read by a classic decoder () which does not make use of methods of machine learning.

Optoelectronic code reader and method for reading optical codes

A code reader for the reading of optical codes is provided that has an image sensor for the detection of image data with the code and that has a control and evaluation unit that is configured to read the code with at least one decoding method, wherein the control and evaluation unit is connected to a distance sensor that determines a distance value for the distance of the code. The control and evaluation unit is further configured for the purpose of setting at least one parameter and/or including at least one additional algorithm of the decoding method for the decoding method in dependence on the distance value.

Code reader and method for reading of optical codes

A method of reading an optical code () is provided that has a plurality of code words, wherein image data having the optical code () are recorded and evaluated to read out the code words, and wherein it is determined by a test process whether the code is read correctly. In this respect, a code word at at least one position of the code () is replaced with a code word known for this position in a pre-correction and the test process is carried out after the pre-correction.

Method of reading an optical code and optoelectronic code reader

An optoelectronic device () is provided having an image sensor () for generating pixel images of a detection area () and a brightness correction unit () configured to modify brightness values of the pixels with a correction factor (H) to obtain a more homogeneously illuminated image. A respective correction factor (H) is calculated for individual pixels or groups of pixels from a perspective transformation (M) which converts geometries of an object plane () in the detection area () into geometries of the image plane ().

Optoelectronic device and method for brightness correction

An optoelectronic apparatus is set forth for measuring structural sizes or object sizes which has a light reception element for converting received light into image data, a reception optics arranged before the light reception element as well as an evaluation unit which is configured to identify structures or objects in the image data, to determine its dimensions in picture elements and to convert the dimensions into absolute, in particular metric, units of length with reference to a scaling factor, wherein the scaling factor can be determined in a calibration procedure. In this respect, the evaluation unit is configured to locate code regions in the image data and to read out code information from the code regions and to calculate the scaling factor during the calibration procedure from dimensions of a calibration code whose code information includes a size indication for the calibration code in the absolute units of length.

Optoelectronic apparatus for measuring structural sizes or object sizes and method of calibration

A camera system () is provided for generating an image presegmented into regions (-) of interest and of no interest, having an evaluation unit () which is designed to divide the raw image into part regions (-) to calculate a contrast value for each part region (-) and to decide with reference to the contrast value whether the respective part region (-) is a region of interest () or a region of no interest (). In this respect, the evaluation unit () has a preprocessing unit () which is implemented on an FPGA, which respectively accesses the pixels of a part region (-) and generates summed values (a), b) for the respective part region (-) and has a structure recognition unit () which calculates the contrast value of the part region (-) from its summed values (a, b) without accessing pixels of the part region (-).

Generating an image presegmented into regions of interest and regions of no interest

A code reader () having an image sensor () for the generation of images of a detection zone () resolved into pixels is provided having a decoding unit () in order to identify code regions () in the images and to readout their coded information, as well as having a verification unit () for evaluating the code quality in accordance with predefined criteria. In this connection the verification () is configured to initially generate a normalized verification image of the code from the code regions () by means of image processing for the verification.

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Waldkirch, Germany

Pascal Schüler