Papers and thesis about image processing research.
Conception of 2D-symbolic codes reader by image analysis.
PhD thesis: overview
Étude sur la lecture automatique de codes bi-dimensionnels par traitement d'image (A study on automatic reading of bi-dimensional
symbolic codes by image processing).
Specialization: Image processing (Traitement du signal)
Subject: conception of 2D-symbolic codes reader by image analysis.
Oral presentation did occur on 17 December 1997 at ENSEEIHT.
Data Matrix is a new kind of bi-dimensional symbolic code issued
from the classic bar codes symbols. It uses the 2 directions of
the plane, like a chess pad where each cell represents one binary
digit. The point of the study is to design reading algorithms
for these codes by image processing.
The main constraints are:
- The reading must be insensitive to the distortion when the
symbol does not perfectly face the camera. This requires robust
- The reading must be performed in a very short time, short
enough to be considered as instantaneous by a human operator.
This requires fast and simple algorithms.
Many papers have been reviewed, aiming to find and understand
the existing methods for these kind of problem.
Comparison of the methods led us to conclude on a exhaustive and
coherent method based on edge detection by laplacian detectors,
edge localisation by gradient operator, detection validation by texture analysis.
The developed method is:
- Edge detection and aggregation in chains and lines.
- Search for couple of lines having the Data Matrix L
shape pattern using geometric properties and local texture properties.
- Analysis of the inside near border of the symbol to figure
the number of cells in the matrix.
- Sampling of the symbol according to the number and position
of the peripheral cells.
The method has been implemented in an image processing software.
Many tests have been performed to validate and figure the limits
of the method against cell size, contrast, heterogeneous background
level, optical and projective distortions, focalisation. A detailed
study on time processing of the different steps is presented.
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219 21 27 149 171 166 71 118 43 67 246 87 4 8 170 249 73 191 21 185 26 219
The oral presentation report is only (available in French...)
This research has been conducted in group
Signaux Images & Communications
() du Laboratoire d'Électronique de l'ENSEEIHT (LEN7 ) - INPT.
The thesis has been supervised by Pr. Michel Cattoen, professor at ENSEEIHT
and founded by
Intermec - Technologies Corporation (), part of the group Unova.
Jury: Dominique Barba - Rapporteur, Jean Bajon - Examinateur, Maurice Briot - Rapporteur, Philippe Marthon - Examinateur, Michel Cattoën - Directeur de thèse, Jean-Louis Massieu - Examinateur.
SIC - LEN7 - ENSEEIHT
2 rue Charles Camichel
31071 Toulouse CEDEX 7 (France)
Tel.: +33 (0) 5 61 58 83 20
Contact : Pr Cattoën (email@example.com)
I do not have any more copies of the final report (132 pages), and there will be no reprints. However, I made it available online. You will have to download separately the text (858 kB in zip-file) and the graphics (8,9 MB). In the text, the images are inserted as hard-coded references to an absolute path; you will have to either change the references, either lay the graphics under a location which path would be identical as the one in the document. The report is in French.
Publication: Edge and line detection in low level analysis
This work is part of a study on 2D-symbolic codes reader by image
analysis. You may
get the full length version.
Michel Cattoen, « Edge and line
detection in low level analysis », 3rd Workshop
on Electronic Control and Measuring System, 2-3 June 1997,
« Edge detection and linear feature extraction are important and
components in an image understanding system, as the result of the
will be the basis of the high level processes.
In this paper, we have described a new straight line detection
using traditional derivative-based edge operators, which works
internal thresholding, and thus delay detection decisions in the main
of the image analysis system rather than in the low-level line
The algorithm includes two derivative processing by well-known
and gradient filters to determine edge location and orientation, then
the lines by three steps which are: edge linking, edge straightening
line correction. The usual smoothing and thinning steps are not
as they are implicit in the algorithm by the choice of the derivative
The algorithm is executed in 5 phases. In phase 1, thin edges are
detected on zero crossings of the laplacian filter's response. The
of the laplacian filter determines the bandwidth of the implicit
in the filter process. In phase 2, the orientation of each detected
is computed by 2 gradient filters. In phase 3, the edges are linked
to the direction of their gradient vector, and the short chains of
i.e. smaller then 3 or 4 pixels, are eliminated. This elimination
from the response all the noise which had been detected by the first
and thus eliminate the spurious edges, keeping the low contrast
In phase 4, the chains are broken to obtain straight lines. In phase
lines are corrected to recover isolated missing edge element and to
lines on corner if necessary.
The algorithm works well for detection and localization of
Data-Matrix symbols (kind of 2 dimensionnal bar-codes) in images with
bimodal intensity distribution.
Several important parts of this method are based on linear filters.
Thes computations which usually occur in low-level layers of the
system should be able to process fast on hardware embodiment,
although this has not yet be verified. »
- edge detection;
- line detection;
- image analysis;
- laplacian filtering.
Publication: Calcul de translation et rotation par la transformée de
« Estimation of Translation and Rotation by Fourier Transform » .
This work is the result of a six mounths work on LAAS (CNRS) about image
translation and rotation, and is part of a robotic project for
autonomous robots. Autonomous robots are up to date, with success of
Baptiste Marcel (), Maurice Briot and Rafael Murrieta,
« Calcul de translation et rotation par la transformée de Fourier »,
Traitement du signal
(), Vol. 14, n°2, mars 1997,
« In the research area of vision-aided motion sensors,
the rotation parameters can be computed from the motion in the
picture. The properties of translation and rotation in the frequency
domain of the Fourier transform are used here.
This study is restricted to rigid-body transformations,
but other application domains, such as matching of rigidly misaligned
images, also exist. »
- Mobile robotics;
- Image processing;
- Image rotation and translation compensation;
- Fourier transform.
I do not believe this code is still usable in an industrial context because it's quite old. However...
For those who whish to have a look, be informed that the application had a temporary work name: Iris.
It started as a C application under M$-DOS, then in the middle, we migrated to C++ under M$*Windows.
We had a framework which was able to acquire snapshots from a camera,
then the framework was calling functions to process whatever.
In the soup that I leave online, you will have to thread your way around.
- File : thesis_code_A005-accpp+awbc45.zip (17.8 MB)
- Auteur : ENSEEIHT+Baptiste Marcel
- Ayant-droit : ENSEEIHT+Baptiste Marcel+UBI/Intermec-STC
- Licence : © tous droits réservés, toléré de circuler pour des raisons pédagogique, interdit d'utiliser à des fins d'exécution
- Source : http://www.dunwich.org/baptiste/sic/indexe.html#thesis
Abreviations and links
- CNRS: Centre
National de Recherche Scientifique.
Nationale Supérieure d'Électronique,
et Hydraulique de Toulouse.
Institut National Polytechnique de Toulouse.
- INSA () :
Institut National des Sciences Appliquées.
- LAAS: Laboratoire
et d'Architecture des Systèmes.
(): Laboratoire d'Électronique de l'ENSEEIHT.
(): Signaux, Images et Communication.
Engineer in computer science and doctor in electronics.
I graduated engineer at INSA (),
in Toulouse, and I made my first research study (D.E.A.) at LAAS
- CNRS (Toulouse).
I made my PhD thesis at ENSEEIHT
(École Nationale Supérieure d'Électronique,
et Hydraulique de Toulouse), within the group S.I.C.
(Signaux, Images et Communication), part of LEN7 ()
(Laboratoire d'Électronique de l'ENSEEIHT), in Toulouse
(France), in the field of image analysis.
File created July 1996, last update
by by Baptiste MARCEL (see page Contact),
located in Asnières-sur-Seine (France).
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