Appendix - Supplement Materials

Supplement

Materials

I1. Summary

The 3 strategies for bringing the customers the best image quality by:

Adaptive module assembly

Focus alignment with MTF or SFR

OIS module calibration

Active alignment (multiple regions, e.g., 9-region)

Image pipeline

Auto focus

Auto exposure

Color image pipeline, e.g. edge-aware color interpolation

Digital zooming-in

Color aliasing removal

Adaptive tone enhancement

Lens shading correction

Bi-model image modeling

Shading parameter estimation and correction

Quality control

IQ testing

Frequency component analysis

Tilt and field curvature estimation

Optical center estimation

Automatic optical inspection

Simulation of lens shading and through-focus curve

Assessment, e.g. Nokia VUP

In summary, the techniques having been used include:

RANSAC

Watershed

Mean-shift

PCA

Cubic spline

Newton's divided differences

Dynamic time warping

Camera calibration and homography transformation

Kalman filter

Fourier transform

Sigmoid function

Hybrid gamma curve

Encoded finder pattern

Circle and ellipse fitting by LSE

Image processing techniques

Machine and I/O controlling

EE (Ardruino and Raspberry Pi)

SW project management: code documenting tool, version controlling with

remote backup, trouble-shooting manual (for production line), and

knowledge base website (for internal use)

I2. Optical Image Stabilization (OIS) Module Calibration:

Find the optimal gain which can make the best shaking compensation.

I3. 6-Axis Active Alignment (AA):

Eliminate the tilt angle between sensor and lens when assembling the camera

module. In current process, the tilt angle can be decreased to 0.03° by using a

15 degree-of-freedom machine (9 motors: buffer tray + dispensing + 6-axis AA;

5 IOs: dispensing + vacuum+ de-vacuum + Gripper + UV; different machine

states: standby + PnP + dispensing + component loading + AA).

Loop1

Loop2

Loop0

I4. Color Image Pipeline – Color Interpolation:

An interpolation method optimized for edge-like content.

Ref algo.

YC Ref. A Cur

YC

I5. Color Image Pipeline –Color-aliasing Removal:

Remove the false color on the edges.

I6. Color Image Pipeline – Digital Zoom (4x):

Increase the image resolution by a frequency-domain approach.

Before After

Source 4x Res.

I7. Color Image Pipeline – Quick Auto Exposure (AE with 2 input frames):

An AE algorithm for the un-calibrated camera module under stationary

illumination.

I8. Color Image Pipeline – Lens Shading Correction:

Left: the input image (StD: 13.17 DN); right: the result (StD: 0.59 DN).

I9. Color Image Pipeline – Color Correction:

In each color patch, the upper half, the lower half and the small patch in the

lower half are the target color, the sampled color and the corrected color

respectively.

I10. Summary of Image Pipeline:

I11. Stereo Camera Calibration and Online Depth Estimation:

Use two webcams to build a stereo camera and do the online depth estimation.

The camera calibration is used for extraction of intrinsic and extrinsic

parameters. Rectification and make 3D point cloud.

Stereo camera and the camera calibration:

Depth Space Image:

I12. RGBD Camera Module Calibration:

Finding the correspondence between RGB image and depth map is essential to

the depth-related applications, such as re-focusing and generating of 3D point

cloud. To estimate the correspondence, the general idea is to find intrinsic

parameters and the relative orientation between two sensors, and then the

correspondence can be found after the objects captured by the depth sensor are

projected onto the RGB sensor.

I13. Color Transfer between Images:

The method is based on color space transformation. On the left the image

contains the target colors. The upper-right and lower-right are the original

image and the result image respectively.

s:

I14. Unsupervised Learning – Feature Selection:

Use sparse coding to obtain better feature. Spare coding is an iterative method

to find dictionary and feature vector by using matching pursuit and k-SVD

respectively.

The Dictionary:

I15. AOI - Adaptive golden image:

Find the defects on the LED cup and LED die.

Image samples and defects:

Good image samples:

Results:

I16. EE Project – Bluetooth Level Meter:

Use the InvenSense MPU6050 and Kalman filter to estimate the angle, and

then send the measurement to Android phone through Bluetooth.

I17. EE Project – Self Balancing Robot:

Use gyro, Kalman filter, PID control and I2C and PWM to balance the two-

wheel robot (code is ready and now tuning the PID parameters

I18. EE Project – Online Face Detection on Raspberry Pi:

Detecting face and overlapping the glasses image on the detected face.

I19. Linux Project – Streaming on embedded Linux

I20. Master’s dissertation:

Use camera to estimate the pose of subject’s head.

(a) (b) (c)

(d)

The goal of the system is to estimate the coordinate transformation HEADTMEG

between subject’s head CHEAD and the machine CMEG. The conventional way to do

that is to use positioning coils which are attached on the subject’s head, but only can

be used before the experiments otherwise it will affect the Magnetoencephalography

(MEG) measurement. The proposed system can track the 3D coordinates of

subject’s head during experiments, and that can be used to estimate HEADTMEG and

compensate the artifacts caused by the head movement during the experiments. (a)

The MEG machine and the setup of camera calibration. (b) The pattern for CAMTMEG

estimation. (c) The pattern for HEADTCAM estimation.

I21. Patent – I3 (Integrated, Interactive, and Immersive) Surveillance System

http://www.youtube.com/watch?v=LAcAkLDRIY0

I22. Paper list:

Journal Papers:

1. "Integration of multiple views for a 3-D indoor surveillance system," Information,

vol. 13, no. 6, pp. 2039-2057, 2010. Yi-Yuan Chen, Yung-Huang Huang, Yung-Cheng

Cheng, and Yong-Sheng Chen

2. "Camera-guided coordinate system alignment for neuromagnetic source

estimation," International Journal of Bioelectromagnetism, vol. 7, no. 2, pp. 86-89,

2005. Yung-Cheng Cheng, Yong-Sheng Chen, Jen-Chuen Hsieh, and Li-Fen Chen

International Conference Papers:

1. "A 3-D surveillance system using multiple integrated cameras," Proceedings of the

IEEE International Conference on Information and Automation, Harbin, China, Jun.

2010. Yi-Yuan Chen, Yung-Huang Huang, Yung-Cheng Cheng, and Yong-Sheng Chen

2. "Accurate planar image registration for an integrated video surveillance system,"

Proceedings of the IEEE Workshop on Computational Intelligence for Visual

Intelligence, Nashville, Tennessee, USA, Mar. 2009. Yung-Cheng Cheng, Kai-Ying Lin,

Yong-Sheng Chen, Jenn-Hwan Tarng, Chii-Yah Yuan, and Chen-Ying Kao

3. "Camera-guided coordinate system alignment for neuromagnetic source

estimation," Proceedings of the Joint Meeting of 5th International Conference on

Bioelectromagnetism and 5th International Symposium on Noninvasive Functional

Source Imaging within the Human Brain and Heart, Minneapolis, Minnesota, May

2005. Yung-Cheng Cheng, Yong-Sheng Chen, Jen-Chuen Hsieh, and Li-Fen Chen

Taiwan Conference Papers:

1. "3-D environment model construction and adaptive foreground detection for multi-

camera surveillance system," Proceedings of 2010 IPPR Conference on Computer

Vision, Graphics, and Image Processing, Kaohsiung, Taiwan, Aug. 2010. Yi-Yuan

Chen, Hung-I Pai, Yung-Huang Huang, Yung-Cheng Cheng, Yong-Sheng Chen, Jian-

Ren Chen, Shang-Chih Hung, Yueh-Hsun Hsieh, Shen-Zheng Wang, and San-Lung

Zhao

2. "I3: Integrated, interactive, and immersive surveillance system," Proceedings of

2008 IPPR Conference on Computer Vision, Graphics, and Image Processing, Yilan,

Taiwan, Aug. 2008. Yung-Cheng Cheng and Yong-Sheng Chen

3. "Real-time adaptive functional brain imaging," Proceedings of the International

Symposium on Biomedical Engineering, Taipei, Taiwan, Dec. 2006. Li-Fen Chen,

Yong-Sheng Chen, Jen-Chuen Hsieh, and Yung-Cheng Cheng

4. "Coordinate system alignment using single camera for functional brain imaging,"

Proceedings of 2005 IPPR Conference on Computer Vision, Graphics, and Image

Processing, Taipei, Taiwan, Aug. 2005. Yung-Cheng Cheng, Yong-Sheng Chen, Jen-

Chuen Hsieh, and Li-Fen Chen

I23. US Patent:

I24. Taiwan Patent: