Senior Software Engineer/Consultant koopcheng@gmail.com

+49 170 716 2482

Profile 4-year industrial experience in design of image processing

algorithms, conventional camera module, depth-sensing

camera module, and embedded Linux software.

Learn things quickly and initiatively – has studied the whole

camera module manufacturing process, and developed the

key algorithms to improve the optical performance and

product quality of camera module.

Good at developing complex system, including algorithm

design and HW/SW integration. The recent project is 6-axis

active alignment – control a 15 degree-of-freedom machine

to align two 7x7mm components and eliminate the tilt angle

between the two components to 0.03˚.

Experience

and Major

Achievements

Lite-On [the global top 4 provider of camera module] 2011 - Present

Senior Software Engineer, Automation Division.

Responsible for developing image processing algorithms,

including OIS module calibration, active alignment, color image

pipeline, auto-focusing, auto-exposure, color correction, color-

aliasing removal, digital zooming and lens shading correction

[appendix I2 ~ I10].

Software Consultant, NPI Team and Surveillance Division.

Responsible for developing algorithm for 3D point cloud analysis,

stereo camera calibration, RGB-ToF camera module calibration,

depth estimation, and point cloud analysis with PCL library.

Additionally, YC also studies color transfer between images, and

unsupervised learning [appendix I11 ~ I14].

Machvision [AOI machines manufacturer] 2010 - 2011

Associate Research Engineer

Develop automated optical inspection (AOI) algorithms. One

major achievement is to design an algorithm to estimate the

golden image for objects which have large variety in shape and

appearance [appendix I15].

YC Cheng 鄭詠成

Certificate &

Award

Ph.D. candidate (passed the qualification exams: algorithm,

computer architecture, operating system and complexity) of

Computer Engineering Institute, National Chiao-Tung

University (35th world-wide*).

One US patent and one Taiwan patent [appendix I20 and I21].

ITRI annual paper award and CVGIP excellent paper award.

IELTS 6.0.

Major Contribution at Work

Part 1: Industrial automation. The conventional manufacturing process of camera

modules. YC’s contributions are labeled with red arrows.

Part 2: Depth-sensing camera module – calibration, depth estimation and 3D

point cloud analysis.

* The ranking is based on Shanghai Jiaotong University’s Academic Ranking of the World Universities in Computer Science Field, 2013.

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. US Patent:

I23. Taiwan Patent: