ABSTRACT. Keywords: Charge Couple Device, Image Reconstructions, Forward Problem, Inverse Problem, Non Contact Fingerprints Reader. 1.

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1 IMAGE RECONSTRUCTION OF SMALL OBJECTS DETECTED BY FOUR PROJECTION CHARGE COUPLED DEVICE A NON-CONTACT 3D FINGERPRINTS READER TO MANAGE STUDENT S ATTENDANCE RECORD AT KOLEJ KOMUNITI BAYAN BARU. Noor Sarena Mohd Zahid 1, Norhafizah Abdul Rahman 2 and Nik Mohd Noor Faizal Md.Saad 3 Kolej Komuniti Bayan Baru Kementerian Pengajian Tinggi Malaysia 1 noorsarena@kkbba.edu.my, 2 norhafizah@kkbba.edu.my, 3 nikfaizal@kkbba.edu.my ABSTRACT Fingerprint recognition has been applied in most of the education institutions. Fingerprints reader helps to observe student s attendance record which is involving personal identification. These factors influence the product and service quality of an academic instituition itself. However, limitations of the current fingerprint capture technologies including obligatory maintenance of a clean sensor, uncontrollability and non-uniformity of the finger pressure on the device, permanent or semipermanent change of the finger ridge structure due to injuries or heavy manual labors, data distortion under different illumination, environmental, and finger skin conditions, and extra scanning time and motion artifacts. The objective of this project is to analysis the input pattern of several sensitivity matrices and develop a C language program of a sensitivity matrix for attenuation coefficient. Then develop an image reconstruction program in two dimension for four projection CCD linear image sensor. This project will use an experimental rig which is based on four-projection CCD linear image sensor surrounding a measurement section. The shadow of an object will be casted on the CCD linear image sensor, where the output voltage will be acquired by the Keithley DAS. Image reconstruction process for the Charge Coupled Device (CCD)-based optical tomography with four projections is discussed in this report. The high resolution CCD linear image sensor used in this project has 2048 pixels with a pixel size of 14 micron by 14 micron. The Sensitivity Matrix of attenuation coefficient up to 2048 pixel is created using C program. A simulation data of Forward Problem and Inverse Problem from the experimental circuit will be tested on Matlab software. Keywords: Charge Couple Device, Image Reconstructions, Forward Problem, Inverse Problem, Non Contact Fingerprints Reader. 1. Introduction Image reconstruction is a process of generating an image from raw data, or set of unprocessed measurements, made by imaging system. In general, there is a well defined mathematical relationship between the distribution of physical properties in an object and the measurements made by the imaging system. Image reconstruction is the process which inverts this mathematical process to generate an image from the set of measurements. Tomography reconstruction is imaging by sections or sectioning. A device used in tomography is called a tomograph, while the image produced is a tomogram. Organized by 273

2 Tomography is making cross-sectional images of an object. It is use to obtain cross-sectional images of a body or a process. The Greek word tomos means sharp in the sense of making a sharp cut. This method is used in medicine, archeology, biology, geophysics, oceanography, materials science, astrophysics and other sciences. In most cases it is based on the mathematical procedure called tomographic reconstruction. Process tomography may be defined as imaging process parameters in space and time. A number of sensors are installed around a cross-section of the object that is to be imaged. The parameter or characteristic to be imaged is determined by the type of sensor chosen. The sensor signals are amplified, filtered, multiplexed, and digitized then read into computer in which a crosssection of the measured parameter is reconstructed. Sensors can be divided naturally into hard-field and soft-field. A hard-field sensors is equally sensitive to the parameter it measure in all positions throughout the measurement volume. Its sensitivity is also independent of the distribution of the measured parameters inside and outside this measurements region. For a soft-field sensors, sensitivity to the measured parameter depend on the position in the measurement volume, as well as on the distribution of parameters inside and outside this region. One of the earliest applications of tomography is in the field of medicine where a particular plane in a human body is imaged using this technique for diagnosis purposes. Figure 1 Process Tomography System Figure 2 Process Tomography System Organized by 274

3 1.1 Statement of the Problem Fingerprint recognition has been applied in most of the education institutions. Fingerprints reader helps to observe student s attendance record which is involving personal identification. However, limitations of the current fingerprint capture technologies including obligatory maintenance of a clean sensor, uncontrollability and non-uniformity of the finger pressure on the device, permanent or semi-permanent change of the finger ridge structure due to injuries or heavy manual labors, data distortion under different illumination, environmental, and finger skin conditions, and extra scanning time and motion artifacts incurred in technologies that require finger rolling. The advantages of a non-contact 3D fingerprint reader includes: non-contact defuses distortion, simultaneous acquisition of both texture and ridge depth information of fingers, automated fingerprint entry in no need of interaction with the operator, fast scanning, robustness to contamination of fingers and residues of previous users, robustness to clutter and fraud because of the difficulties in faking 3D fingerprints, real-time feedback for users to make position adjustment, and low cost by using the off-the-shelf commodity. 1.2 Objective of the Study The objective of this project is to analysis the input pattern of several sensitivity matrices and develop a C language program of a sensitivity matrix for attenuation coefficient. Then develop an image reconstruction program in two dimension for four projection CCD linear image sensor. The program will be based on Matlab software. 1.3 Scope and Significance of the Study In this project, a study is carried out on the optical aspect of the interaction between the light source (low power laser) and the CCD linear image sensor, in order to produce a simple optical attenuation model. Then, a suitable mapping of sensitivity matrix (obtained from the model configuration) into the system is designed based on C language. A program will be developed based on Matlab software to reconstruct an image from four projections CCD by using simulation data. The final stage is testing the program with experimental data i.e. small objects that can be detected by the CCD sensor. The algorithm used in the program will be based on the Linear Back Projection (LBP) and Layergram Back Projection (LYBP) 2. Literature Review 2.1 Image Reconstruction Algorithm A tomography system must have the image reconstruction algorithm to compute cross sectional image. Most of the image reconstruction algorithms applied in industry tomography comes directly from medical tomography. Among them, back projection algorithm can be said to be the favorite among most of the researchers and resulting in many variations, such as linear back projection, filtered back projection, convolution back projection and graphical back projection. The other algorithms of image reconstruction include the hybrid reconstruction algorithm and algebraic reconstruction techniques (ART). Organized by 275

4 2.2 Tomography Image Reconstruction According to Beer Lambert Law, In an optical imaging system, the light intensity is exponentially attenuated by the object density along the optical path, x I I e (1) out in I in ln x I (2) out where I in is the incident intensity, I out is the output intensity, α is the linear attenuation coefficient, and x is the distance the light traversed. The natural logarithm of the ratio of the incident intensity to the transmitted intensity is equal to the line integral or ray sum of the distribution of linear attenuation coefficient within the object along the path. An image of the object density distribution can be created using a projection reconstruction algorithm. In order to obtain the expected output from the sensors of known attenuation coefficient of water and the particle, a forward problem has to be performed first. The calculated output from the forward problem will then be used in the backprojection process (inverse problem). 2.3 Forward problem In this experiment, the particle used is opaque and solid surrounded with water with its linear attenuation of mm -1. The particle used in the experiment is steel with its linear attenuation of 10mm -1. For this experiment, the area of imaging in the pipe is divided into7x7 array of pixels. The reconstructed image of the particle in water is based on four projecttions. The arrangement of pixels is shown in Figure 3 P3 PROJECTION 2 (P2) α 00 α 01 α 02 α 0 3 (P1) α 10 α 11 α 12 α 1 3 PROJECTION α 20 α 21 1 α 22 α 2 3 α 04 α 05 α 06 α 14 α 15 α 16 α 24 α 25 α 26 α 30 α 31 α 32 α 3 3 α 34 α 35 α 36 α 40 α 41 α 42 α 4 3 α 44 α 45 α 46 α 50 α 51 α 52 α 5 3 α 54 α 55 α 56 α 60 α 61 α 62 α 6 3 α 64 α 65 α 66 P4 Figure 3 A 7x7 array of pixels with four projections From Figure 3, each projection has seven optical sensors and hence the total number of sensors for four projections is 28. So, there are seven pixels associated with each sensor when the incoming light passes through the pipe. The linear attenuation of the light is modelled by assuming that each Organized by 276

5 pixel has an attenuation coefficient of α ij where i and j represent the row and column, respectively. The length of the octagon pixel is mm while the length of the square pixel is mm. Based on the Beer-Lambert Law of absorption, the horizontal equations for projection 1 for each sensor are: ( 0.014) (0.006) (0.014) (0.006) (0.014) (0.006) (0.014 M ) ) 11(0.014) 12(0.006) 13(0.014) 14(0.006) 15(0.014) 16(0.006) ( M and so forth until M 7. Similarly for the vertical equations of projection 2 for each sensor: ( 0.014) (0.006) (0.014) (0.006) (0.014) (0.006) (0.014 M ) ) 11(0.014) 21(0.006) 31(0.014) 41(0.006) 51(0.014) 61(0.006) ( M and continue until M 14. For Projection 3: (0.014) M (0.014) 15(0.014) 26(0.014) M and continue until M 21. Projection 4: (0.014) M (0.014) 55(0.014) 46(0.014) M 23 and the same process is repeated until M 28. Now, the above expressions are expressed in matrix form, S xr M (3) where S is the sensitivity matrix [7x49 matrix], R is the matrix of attenuation coefficients [49x1 matrix], and M is the measurement values [28x1 matrix] Inverse problem In order to reconstruct the tomographic image, equation (3) needs to be re-arranged 1 R S xm (4) The main problem associated with the inverse problem is that the matrix S is not square and hence there is no direct inverse. This is the main limitation of the inverse problem where it is quite impossible to have the number of projections to be equal to the number of pixels involved. Even if the matrix S is square, the inversion is still not possible because the matrix is too sparse. Therefore, transpose and pseudo inverse are used in order to obtain the inverse matrix of S. transpose T R S x M S x M (5) pseudo inverse R S xm pinvs xm The pseudo inverse matrix is equivalent to inversion of a matrix compared to transpose in the case of simple matrix calculations. In the case where matrix-inversion is not possible because the matrix is (6) Organized by 277

6 not square or too sparse, it is better to use pseudo inverse to represent the inverse matrix. However, the transpose matrix is widely used in the tomographic image reconstruction process. 3. METHODOLOGY 3.1. Simulation Research Methodology This project will use an experimental rig which is based on four-projection CCD linear image sensor surrounding a measurement section. The shadow of an object will be casted on the CCD linear image sensor, where the output voltage will be acquired by the Keithley DAS. 3.2 Data Sources The data sources for this project is captured using CCD based optical tomography system as can be seen from figure 4 LS4 FILT4 pipe OBJL4 SPHL4 45 CCD1 CCD2 LS3 FILT3 OBJL3 SPHL3 CCD3 CCD4 ray-box SPHL2 SPHL1 OBJL2 FILT2 OBJL1 SPHL: spherical lens LS2 FILT1 OBJL: objective lens FILT: colour filter LS: laser diode LS1 Figure 4 : CCD Based Optical Tomography System The designed optical tomography system comprised of four projections system where each projection consists of the ray-box which has the laser diode, the objective lens, the filters and the spherical lens in it and the CCD linear image sensor at the other side of the pipe as shown in Figure 5. The CCD linear image sensor used in the system is Sony ILX551A with a built-in timing generator and clock-drivers ensuring direct drive at 5 volts logic for easy use. It has 2048 effective pixels with a pixel size of 14 micron by 14 micron. The size of the pipe used in the system is 40mm diameter. Organized by 278

7 Figure 5: Mechanism used for determination of particle shape. In the experiment, water is used as the medium and several projections are taken at different positions, as the particle is drop downward (Figure 6). The output results are processed using the Matlab software to generate slices of images. 3.3 Instrumentation Figure6 : Experimental setup for the CCD-based optical tomography. Organized by 279

8 4.0 Progress And Preliminary Results 4.1 Project Progress Project progress are shown as the gantt chart below: 4.2 Preliminary Result Sensitivity Matrix Source Code in C Language has been designed. Forward Problem and Inverse Problem Source Code in Matlab Source Code has been designed. 5.0 Conclusion And Future Works An image reconstruction program/software which is capable to reconstruct an image of small objects detected by the four projections CCD linear image sensor. Future works include; graphical user interface to collect students information such as name and Id.number, build a fingerprints reader and testing process. Organized by 280

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