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Cheng, Xin
Publications (7 of 7) Show all publications
Cheng, X. (2012). Hardware centric machine vision for high precision measurement of reference structures in optical navigation. (Licentiate dissertation). Sundsvall: Mid Sweden University
Open this publication in new window or tab >>Hardware centric machine vision for high precision measurement of reference structures in optical navigation
2012 (English)Licentiate thesis, comprehensive summary (Other academic)
Place, publisher, year, edition, pages
Sundsvall: Mid Sweden University, 2012. p. 78
Series
Mid Sweden University licentiate thesis, ISSN 1652-8948 ; 77
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:miun:diva-16176 (URN)STC (Local ID)978-91-87103-05-6 (ISBN)STC (Archive number)STC (OAI)
Supervisors
Available from: 2012-05-04 Created: 2012-05-04 Last updated: 2016-10-20Bibliographically approved
Cheng, X., Thörnberg, B. & Abdul Waheed, M. (2011). Optimized Color Pair Selection for Label Design. In: Proceedings Elmar - International Symposium Electronics in Marine: . Paper presented at 53rd International Symposium ELMAR-2011; Zadar; 14 September 2011 through 16 September 2011 (pp. 115-118). Zadar, Croatia: IEEE conference proceedings
Open this publication in new window or tab >>Optimized Color Pair Selection for Label Design
2011 (English)In: Proceedings Elmar - International Symposium Electronics in Marine, Zadar, Croatia: IEEE conference proceedings, 2011, p. 115-118Conference paper, Published paper (Refereed)
Abstract [en]

We present in this paper a technique for designing reference labels that can be used for optical navigation. We optimize the selection of foreground and background colors used for the printed reference labels. This optimization calibrates for individual color responses among printers and cameras such that the Signal to Noise Ratio (SNR) is maximized. Experiments show that we get slightly smaller SNR for the color labels compared to using a monochrome technique. However, the number of segmented image components is reduced significantly by as much as 78 percent. This reduction of number of image components will in turn reduce the memory storage requirement for the computing embedded system.

Place, publisher, year, edition, pages
Zadar, Croatia: IEEE conference proceedings, 2011
Keywords
Label, Recognition, Position Measurement, COG, Subpixel Precision, RGB, HSI, YCbCr
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:miun:diva-14531 (URN)2-s2.0-80055085889 (Scopus ID)STC (Local ID)978-953-7044-12-1 (ISBN)STC (Archive number)STC (OAI)
Conference
53rd International Symposium ELMAR-2011; Zadar; 14 September 2011 through 16 September 2011
Projects
Optipos
Available from: 2011-09-26 Created: 2011-09-26 Last updated: 2016-10-19Bibliographically approved
Malik, A. W., Thörnberg, B., Cheng, X. & Lawal, N. (2011). Real-time Component Labelling with Centre of Gravity Calculation on FPGA. In: 2011 Proceedings of Sixth International Conference on Systems: . Paper presented at Sixth International Conference on Systems ICONS 2011.
Open this publication in new window or tab >>Real-time Component Labelling with Centre of Gravity Calculation on FPGA
2011 (English)In: 2011 Proceedings of Sixth International Conference on Systems, 2011Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we present a hardware unit for real time component labelling with Centre of Gravity (COG) calculation. The main targeted application area is light spots used as references for robotic navigation. COG calculation can be done in parallel with a single pass component labelling unit without first having to resolve merged labels. We present hardware architecture suitable for implementation of this COG unit on Field programmable Gate Arrays (FPGA). As result, we get high frame speed, low power and low latency. The device utilization and estimated power dissipation are reported for Xilinx Virtex II pro device simulated at 86 VGA sized frames per second. Maximum speed is 410 frames per second at 126 MHz clock.

Identifiers
urn:nbn:se:miun:diva-12170 (URN)STC (Local ID)STC (Archive number)STC (OAI)
Conference
Sixth International Conference on Systems ICONS 2011
Projects
OptiPos
Available from: 2010-10-29 Created: 2010-10-29 Last updated: 2016-10-19Bibliographically approved
Cheng, X., Thörnberg, B., Malik, W. & Lawal, N. (2010). Hardware centric machine vision for high precision center of gravity calculation. World Academy of Science, Engineering and Technology: An International Journal of Science, Engineering and Technology, 40, 576-583
Open this publication in new window or tab >>Hardware centric machine vision for high precision center of gravity calculation
2010 (English)In: World Academy of Science, Engineering and Technology: An International Journal of Science, Engineering and Technology, ISSN 2010-376X, E-ISSN 2070-3740, Vol. 40, p. 576-583Article in journal (Refereed) Published
Abstract [en]

We present a hardware oriented method for real-time measurements of object's position in video. The targeted application area is light spots used as references for robotic navigation. Different algorithms for dynamic thresholding are explored in combination with component labeling and Center Of Gravity (COG) for highest possible precision versus Signal-to-Noise Ratio (SNR). This method was developed with a low hardware cost in focus having only one convolution operation required for preprocessing of data.

National Category
Engineering and Technology
Identifiers
urn:nbn:se:miun:diva-28876 (URN)2-s2.0-84871233891 (Scopus ID)
Available from: 2016-09-22 Created: 2016-09-22 Last updated: 2017-11-21Bibliographically approved
Cheng, X., Thörnberg, B., Malik, W. & Lawal, N. (2010). Hardware Centric Machine Vision for High Precision Center of Gravity Calculation. In: PROCEEDINGS OF WORLD ACADEMY OF SCIENCE, ENGINEERING AND TECHNOLOGY: . Paper presented at WASET Int. conference on Digital Image Processing ICDIP 2010, Rome (pp. 736-743).
Open this publication in new window or tab >>Hardware Centric Machine Vision for High Precision Center of Gravity Calculation
2010 (English)In: PROCEEDINGS OF WORLD ACADEMY OF SCIENCE, ENGINEERING AND TECHNOLOGY, 2010, p. 736-743Conference paper, Published paper (Refereed)
Abstract [en]

We present a hardware oriented method for real-time measurements of object’s position in video. The targeted application area is light spots used as references for robotic navigation. Different algorithms for dynamic thresholding are explored in combination with component labeling and Center Of Gravity (COG) for highest possible precision versus Signal-to-Noise Ratio (SNR). This method was developed with a low hardware cost in focus having only one convolution operation required for preprocessing of data.

Series
PROCEEDINGS OF WORLD ACADEMY OF SCIENCE, ENGINEERING AND TECHNOLOGY, ISSN 2070-3724 ; Vol 64
Keywords
Dynamic thresholding, segmentation, position measurement, sub-pixel precision, center of gravity
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:miun:diva-11804 (URN)
Conference
WASET Int. conference on Digital Image Processing ICDIP 2010, Rome
Projects
OptiPos - Optical position measurement in real-time for consumer products
Available from: 2010-07-01 Created: 2010-07-01 Last updated: 2016-09-22Bibliographically approved
Cheng, X., Abdul Waheed, M. & Thörnberg, B.Color Symbol Design and Its Classification for Optical Navigation.
Open this publication in new window or tab >>Color Symbol Design and Its Classification for Optical Navigation
(English)Manuscript (preprint) (Other academic)
Abstract [en]

We explored the color symbol design and its recognition in image as reference structure for optical navigation. A colors pair was first determined as foreground and background from HSI color palette and then a color symbol was designed as reference structure. The advantage of using this selected color symbol is a significant reduction, up to 97%, of segmented image components as compared to the grey scale image used. The reduction of segmented components in image will result in saving the hardware resources e.g. memory and processing power which are very important constraint for embedded platforms. A color symbol pattern was designed, comprising of three concentric circles with selected color pair. Inside the inner most circle is the Area Of Interest (AOI), the contents of AOI depends on the particular application. A hardware centric image analysis algorithm is developed for easy and robust recognition. Image components are identified after preprocessing, segmentation and labeling. The color symbol can be recognized at a classification step. Evaluating a variety of viewing angles and reading distances ranging from 30 to 150 degrees and from 1 to 10 meters gives a classification success rate of 72 percent of the positions.

Keywords
Reference symbol, SNR, Classification, Nearest-neighbors rule, COG, Recognition, Position Measurement, Robotic Navigation, Machine Vision
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:miun:diva-15200 (URN)
Projects
STCIndustriella IT-system
Available from: 2011-12-13 Created: 2011-12-13 Last updated: 2012-08-10Bibliographically approved
Abdul Waheed, M., Thörnberg, B., Cheng, X., Lawal, N., Imran, M. & Kjeldsberg, P. G.Generalized Architecture for a Real-time Computation of an Image Component Features on a FPGA.
Open this publication in new window or tab >>Generalized Architecture for a Real-time Computation of an Image Component Features on a FPGA
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(English)Manuscript (preprint) (Other academic)
Abstract [en]

This paper describes a generalized architecture for real-time component labeling and computation of image component features. Computing real-time image component features is one of the most important paradigms for modern machine vision systems. Embedded machine vision systems demand robust performance, power efficiency as well as minimum area utilization. The presented architecture can easily be extended with additional modules for parallel computation of arbitrary image component features. Hardware modules for component labeling and feature calculation run in parallel. This modularization makes the architecture suitable for design automation. Our architecture is capable of processing 390 video frames per second of size 640x480 pixels. Dynamic power consumption is 24.20mW at 86 frames per second on a Xilinx Spartran6 FPGA.

National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:miun:diva-14536 (URN)
Projects
STC
Available from: 2011-09-26 Created: 2011-09-26 Last updated: 2013-11-11Bibliographically approved
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