General Information:
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No lecture on June, 20
(general assembly of student council)
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Starting on May 2, the lecture will be offered at two alternate
dates with identical content:
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Tuesday, 14-16 hours (regular) and
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Tuesday, 16-18 hours (additional),
both OH 16, room 205.
Tutorials:
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Group A: July 31-August 4, 2017
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Group B: August 7-11, 2017
Location: OH 16, R.U09
Note:
Registration required
(register with
ASSESS,
login, select "Computer Vision" and the desired tutorial)
Introduction:
For the majority of living beeings vision is the most important perception
mechanism for orienting themselves in the environment. Therefore, there exists
a multitude of attempts to recreate this capability in artificial systems.
In contrast to image processing techniques found in industrial applications
the aim of such advanced systems for machine vision is to obtain a task-oriented
interpretation of a complex scene with as few restrictions as possible
concerning the context and the recording conditions.
In this lecture advanced techniques of machine vision are covered
which to some extent are inspired by cognitive processes known from human
visual perception. First, important aspects of imaging processes are introduced
with an emphasis on the perception of colors. Afterwards, methods for the
extraction of image primitives (e.g. regions and edges) and for the calculation
of feature representations (e.g. texture, depth, or motion) are presented.
Finally, the lecture focusses on visual perception processes at the boundary
between image processing and scene interpretation. Several appearance based
object recognition techniques will be covered, e.g., Bag-of-Features approaches,
Eigenimages, and deep Convolutional Neural Networks (CNNs) which define the state-of-the art for many current computer vision problems.
The accompanying tutorials will give students the opportunity to deepen
their knowledge of the theoretical concepts presented in the lecture
by working on relevant practical problems.
Specialization Module (Vertiefungsmodul INF-MSc-502)
for Master (Applied) Computer Science
Topical focus areas (Schwerpunktgebiete):
2 (..., Embedded Systems, ...),
7 (Intelligent Systems)
Bibliography:
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Gonzalez, Rafael C.; Woods, Richard E.:
Digital Image Processing,
Prenctice Hall, 2nd Ed., 2002.
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Forsyth, David A.; Ponce, Jean:
Computer Vision - A Modern Approach,
Prentice Hall, 2003.
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Szeliski, Richard:
Computer Vision,
Springer, 2010.
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Ballard, Dana H.; Brown, Christopher M.:
Computer Vision,
Prentice Hall, 1982.
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Comaniciu, Dorin; Meer, Peter:
Mean Shift: A robust approach toward feature space analysis,
IEEE Trans. on Pattern Analysis and Machine Intelligence,
24(5):603-619, 2002.
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Dalal, Navneet; Bill Triggs, Bill:
Histograms of oriented gradients for human detection,
Proc. IEEE Comp. Soc. Conf. on
Computer Vision and Pattern Recognition,
vol. 2, pp. 886-893, 2005.
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Felzenszwalb, Pedro F.; Huttenlocher, Daniel P.:
Efficient graph-based image segmentation,
Int. J. of Computer Vision, Vol. 59, No. 2, pp. 167-181, 2004.
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Horn, Berthold K. P.; Schunck, Brian G.:
Determining Optical Flow,
Artificial Intelligence,
Vol. 17, pp. 185-203, 1981.
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LeCun, Y.; Bengio, Y.; Hinton, G.:
Deep learning,
Nature, 521:436-444.
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Lewis, J. P.: Fast Normalized Cross-Correlation, web document.
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Lowe, D.:
Distinctive Image Features from Scale-Invariant Keypoints,
Int. Journal of Computer Vision,
Vol. 60, No. 2, pp. 91-110, 2004.
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O'Hara, Stephen; Draper, Bruce A.:
Introduction to the Bag of Features Paradigm for Image Classification and Retrieval,
Computing Research Repository,
arXiv:1101.3354v1,
2011.
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Turk, Matthew; Pentland, Alex:
Eigenfaces for Recognition,
Journal of Cognitive Neuroscience,
Vol. 3, No. 1, pp. 71-86, 1991.
Accompanying Materials:
(For tutorial materials see the
Tutorial
web pages.)
