Transcript PPT - The MESA Lab - University of California, Merced

MESA LAB
Two papers in IFAC14
Guimei Zhang
MESA (Mechatronics, Embedded Systems and Automation)LAB
School of Engineering,
University of California, Merced
E: [email protected] Phone:209-658-4838
Lab: CAS Eng 820 (T: 228-4398)
Sep 08, 2014. Monday 4:00-6:00 PM
Applied Fractional Calculus Workshop Series @ MESA Lab @ UCMerced
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The first paper
Paper title:
09/08/2014
AFC Workshop Series @ MESALAB @ UCMerced
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Motivation
1. This paper describes a software application for
traffic sign recognition (TSR).
2. The main difficulty that TSR (Traffic sign recognition)
systems faces is the poor image quality due to
low resolution, bad weather conditions
or inadequate illumination.
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Overview of the proposed method
Four stages:
1. image preprocessing
 adjust the image size
 a contrast limited adaptive histogram equalization is performed to
enhance the contrast of the image
 Transform the color image to grayscale image.
 edge detection (by the Laplacian of Gaussian (LOG) filter).
2. Image segmentation
Secondly, the traffic signs detection, where
the ROIs (region of intersts) are compared with each
shape pattern.
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Overview of the proposed approach
3. Thirdly, a recognition stage using a crosscorrelation algorithm, where each traffic sign,
is classified according to the data-base of
traffic signs.( feature: normalize signatures)
4. Finally, the previous stages can be managed and
controlled by a graphical user interface (GUI),
which has been designed for this purpose.
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Example
Imput image
09/08/2014
Grayscale image
AFC Workshop Series @ MESALAB @ UCMerced
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Overview of the proposed approach
Laplacian of Gaussian function
Edge detection
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Regions of interest.
Normalized signature
of the ROI
Contour, its centroid and the starting point
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Shape pattern
Normalized signature
Shape pattern
Normalized signature
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Imput image
Rk: Cross-correlation matrix coefficient
GUI
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First interface
second interface
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Conclusion
• A new traffic sign recognition system has been presented in
this paper.
• The image processing techniques used in this software
include a preprocessing stage, regions of interest detection,
the recognition and classification traffic sign, GUI designed.
• The performance of this application depends on the quality
of the input image, in relation to its size, contrast and the way
the signs appear in the image.
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Discuss
• Problems:
I think there are some problems in this paper:
1. The feature is not robust to project transform.
2. Edge detection can be perform after image
segmentation, maybe the efficiency can be improved.
3. Should add some contrast experiments, such accuracy
and efficiency contrast with the existed methods.
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The second paper
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Abstract
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Materials and Feature extraction
Experiment Material (plant)
Sunagoke moss mat was used in this study
Water content was determined as:
where: tmw is the total moss weight (g) and idw is initial dry weight (g)
of Sunagoke moss.
Dry weight of moss was obtained by drying process in the growth
chamber until there is no decrement in the weight of moss.
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Features:
1. Colour Feature (CFs: 22)
2. Textural Feature (TFs: 190)
Colour Co-occurrence Matrix (CCM)
3. Back-Propagation Neural Network (BPNN)
A three layers BPNN performed better than the other
type of ANN to describe the relationship between
moisture content of the moss and the image features.
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4. Multi-Objective Optimization (MOO)
5.
Neural Discrete Hungry Roach Infestation
Optimization (N-DHRIO) algorithm
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The result of precision lighting system
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Conclusion
• The intelligent machine vision for precision irrigation system
using optimized feature selection has been developed. There is an
improvement in optimizing feature selection using NDHRIO
compare to the previous study.
• The intelligent machine vision for precision LED lighting system
has also been developed, and it shows effective to select LED
light intensity which is appropriate to the certain part of the plant
so that all parts of the plant can get enough light and proper
intensity.
• In large scale plant factory, those systems can optimize the plant
growth and reduce the water consumption and energy costs.
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Discuss
•
In my opinion, if possible, we can improve it
as follow:
Many feature are employed to describe the object,
though the authors proposed NDHRIO to select feature,
the efficiency is an important issue. So I think we can
first to use PCA( Principal component analysis) to
reduce the feature dimension and improve
recognition efficiency.
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Thanks