1. MESA LAB Two papers in IFAC14 Guimei Zhang MESA LAB MESA (Mechatronics, Embedded Systems and Automation) LAB School of Engineering, University of California, Merced E: guimei.zh@163.com 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

2. MESA LAB The first paper 09/08/2014 AFC Workshop Series @ MESALAB @ UCMerced Slide-2/1024 Paper title:

3. MESA LAB 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.

4. MESA LAB 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:

5. MESA LAB 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.

6. MESA LAB Imput imageGrayscale image 09/08/2014 AFC Workshop Series @ MESALAB @ UCMerced Example

7. MESA LAB Overview of the proposed approach Laplacian of Gaussian function Edge detection

8. MESA LAB Regions of interest. Contour, its centroid and the starting point Normalized signature of the ROI

9. MESA LAB Normalized signature Shape pattern

10. MESA LAB R k : Cross-correlation matrix coefficient Imput image

11. MESA LAB First interface second interface GUI

12. MESA LAB 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.

13. MESA LAB 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.

14. MESA LAB The second paper

15. MESA LAB Abstract

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18. Materials and Feature extraction Sunagoke moss mat was used in this study Experiment Material (plant) Water content was determined as: where: t mw is the total moss weight (g) and i dw 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.

19. MESA LAB 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:

20. MESA LAB 4.Multi-Objective Optimization (MOO) 5. Neural Discrete Hungry Roach Infestation Optimization (N-DHRIO) algorithm

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23. The result of precision lighting system

24. MESA LAB 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.

25. MESA LAB 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.

26. MESA LAB Thanks