The principles of ergonomics design in fundus cameras should be extending the agreeableness by
automatic control. Firstly, a 3D positional numerical control system is designed for positioning the
eye pupils of the patients who are doing fundus examinations. This system consists of a
electronically controlled chin bracket for moving up and down, a lateral movement of binocular
with the detector and the automatic refocusing of the edges of the eye pupils. Secondly, an
auto-focusing device for the object plane of patient’s fundus is designed, which collects the
patient’s fundus images automatically whether their eyes is ametropic or not. Finally, a moving
visual target is developed for expanding the fields of the fundus images.
Spheroidal graphite cast iron,with excellent mechanical properties,is widely used in manufacturing many advanced
castings,such as crankshaft,gears,pistons,and a variety of machine parts.Its microstructure morphology reflects the
quality performance of the products,which leads to an urgent need for a simple,accurate and automatic microstructure
morphology detection technique for detecting the quality of spheroidal graphite cast iron.In this paper,opto-electrical
detection technique is employed for designing a spheroidal graphite cast iron microstructure automatic detection
system,in which the microstructure is imaged by optical microscopy system,and the digital images are obtained by
industrial cameras and sent to the computer.A series of digital image processing algorithms,including gray transformation,
binarization,edge detection,image morphology and seed filling etc,are adopted to calculate and analyze the
microstructure images.The morphology and microstructure analysis methods are combined to obtain the characteristic
parameters such as the size of the graphite,the ball classification,the number of graphite nodules and so on.The
experiment results show that this method is simple,fast,and accurate and can be employed for assessment of the
spheroidal graphite cast iron metallographic phase instead of manual detection.
KEYWORDS: Digital signal processing, Embedded systems, Biometrics, Signal processing, Light sources and illumination, Light sources, Veins, Sensors, Iris recognition, Dielectrics
The identification technology based on multi-biometric can greatly improve the applicability, reliability and antifalsification.
This paper presents a multi-biometric system bases on embedded system, which includes: three capture
daughter boards are applied to obtain different biometric: one each for fingerprint, iris and vein of the back of hand;
FPGA (Field Programmable Gate Array) is designed as coprocessor, which uses to configure three daughter boards on
request and provides data path between DSP (digital signal processor) and daughter boards; DSP is the master processor
and its functions include: control the biometric information acquisition, extracts feature as required and responsible for
compare the results with the local database or data server through network communication. The advantages of this
system were it can acquire three different biometric in real time, extracts complexity feature flexibly in different
biometrics' raw data according to different purposes and arithmetic and network interface on the core-board will be the
solution of big data scale. Because this embedded system has high stability, reliability, flexibility and fit for different
data scale, it can satisfy the demand of multi-biometric recognition.
The flatness of pins is an important quality indicator for integrated circuit packaging. Almost all of the detection methods
which are currently used can't be successful on efficiency and precision. In this system, the image of IC pins was
captured by an properly optical systems and corresponding CCD sensor. To detect the edge of each pin, traditional
algorithmic, such as Sobel operator and Roberts operator, have some disadvantages: the edge is too thick for system to
accurately measure and the edge show directional character. An image segmentation and border extracting algorithm
focus on the extreme of neighborhood image intensity change was adopted. The advantage of this algorithm was each
pixel's neighborhood image intensity information was considered, so the algorithm is more suitable for accurately
measure. After edge was extracted, how to identify the useful spots is cast as a binary classification task. The support
vector machine (SVM) would be used to identify pin's spots. After proper image characteristics are obtained and a
certain amount of training, SVM provides higher discrimination ratio to distinguish spots of the IC pins. To measure the
flatness of pin, a particular line which can be identified easily should be put in the image as a baseline. Through
calculating the distance between the pins spot and baseline, the flatness of pins is obtained accurately. In this system, the
flatness of IC pins can be accurately and quickly measured, which is worthy of broad application prospect in IC
packaging.
Based on the unique characteristic, the paper currency numbers can be put into record and the automatic identification
equipment for paper currency numbers is supplied to currency circulation market in order to provide convenience for
financial sectors to trace the fiduciary circulation socially and provide effective supervision on paper currency.
Simultaneously it is favorable for identifying forged notes, blacklisting the forged notes numbers and solving the major
social problems, such as armor cash carrier robbery, money laundering. For the purpose of recognizing the paper
currency numbers, a recognition algorithm based on neural network is presented in the paper. Number lines in original
paper currency images can be draw out through image processing, such as image de-noising, skew correction,
segmentation, and image normalization. According to the different characteristics between digits and letters in serial
number, two kinds of classifiers are designed. With the characteristics of associative memory, optimization-compute and
rapid convergence, the Discrete Hopfield Neural Network (DHNN) is utilized to recognize the letters; with the
characteristics of simple structure, quick learning and global optimum, the Radial-Basis Function Neural Network
(RBFNN) is adopted to identify the digits. Then the final recognition results are obtained by combining the two kinds of
recognition results in regular sequence. Through the simulation tests, it is confirmed by simulation results that the
recognition algorithm of combination of two kinds of recognition methods has such advantages as high recognition rate
and faster recognition simultaneously, which is worthy of broad application prospect.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.