MESA LAB Two papers in IFAC14 Guimei Zhang MESA LAB MESA (Mechatronics, Embedded Systems and...

MESA LABMESA LABMESA LABMESA LAB

Two papers in Two papers in IFAC14IFAC14

Guimei ZhangMESA MESA (Mechatronics, Embedded Systems and Automation)LABLAB

School of Engineering,University of California, Merced

E: [email protected] Phone:209-658-4838Lab: CAS Eng 820 (T: 228-4398)

Sep 08, 2014. Monday 4:00-6:00 PMApplied Fractional Calculus Workshop Series @ MESA Lab @ UCMerced


The first paper

09/08/2014 AFC Workshop Series @ MESALAB @ UCMerced

Slide-2/1024

Paper title:


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.


Overview of the proposed method

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.

Four stages:


Overview of the proposed approach

3. Thirdly, a recognition stage using a cross-

correlation 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.


Imput image Grayscale image

09/08/2014 AFC Workshop Series @ MESALAB @ UCMerced

Example


Overview of the proposed approach

Laplacian of Gaussian function Edge detection


Regions of interest. Contour, its centroid and the starting point

Normalized signature of the ROI


Normalized signatureNormalized signature Shape patternShape pattern


Rk: Cross-correlation matrix coefficient

Imput image


First interface

second interface

GUI


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.


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.


The second paper


Abstract


Materials and Feature extraction

Sunagoke moss mat was used in this study

Experiment Material (plant)

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.


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.

Features:


4. Multi-Objective Optimization (MOO)

5. Neural Discrete Hungry Roach Infestation

Optimization (N-DHRIO) algorithm


The result of precision lighting system


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.


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.


Thanks

MESA LAB Two papers in IFAC14 Guimei Zhang MESA LAB MESA (Mechatronics, Embedded Systems and...

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