Fingerprint Image Enhancement via Raised Cosine Filtering
|
|
- Anis Reed
- 6 years ago
- Views:
Transcription
1 Fingerprint Image Enhancement via Raised Cosine Filtering Shing Chyi Chua 1a, Eng Kiong Wong 2, Alan Wee Chiat Tan 3 1,2,3 Faculty of Engineering and Technology, Multimedia University, Melaka, Malaysia. a scchua@mmu.edu.my Abstract This paper aims to present fingerprint image enhancement by using directional circular raised cosine filters which are implemented using a radial component and an angular component. It has been compared with the common filters used for bandpass filtering, i.e. circular Gabor filters and Log-Gabor filters at the same bandwidth. The experimental results have found that the circular raised cosine filters perform at par with the Gabor filters at bandwidth of three octaves for fingerprint image enhancement. The circular raised cosine filters have the advantages of: i) the frequency tail has gradual frequency response that drops towards zero, and ii) its residual can be implemented as a lowpass filter in tandem with a narrow bandpass filter. The Log-Gabor filter did not perform as well due to its design of null DC and thus the results have also shown that the DC component is necessary for fingerprint image enhancement. Keywords: Fingerprint enhancement, raised cosine filter, Gabor filter, Log-Gabor filter Introduction Fingerprint enhancement is a process whereby the raw fingerprint images are improved so that feature extraction such as minutiae detection (for matching) can improve the overall performance. The need for such enhancement is due to quality of the fingerprint images, some regions in the fingerprint are not well-defined, or the condition of the fingerprint when it is acquired such as wet or dry (which may cause excessive blotching or blurring), or noise that may be accidentally introduced such as smearing of inkrolled fingerprint, etc. The image that is enhanced shall improve visual perception based on the human viewers (i.e. to make it clearer ) and thus is assumed to be a better image than it previously was. In fingerprint image, enhancement is aimed at increasing the contrast between the ridges and valleys; and for connecting broken ridges due to insufficient amount of ink, etc. [1]. Fingerprint image enhancement can either be carried out in the spatial domain or the frequency domain. Observation of frequency response of the image to be enhanced is crucial as it provides the cue of how filters are to be designed. Fingerprint enhancement can be done on binary ridge images or grey level images. However the binary images, which are obtained through extraction of the grey level images, suffered from lost of information of the true ridge structures. Grey level images can be modelled locally as a sinusoidal plane wave which has well defined frequency and orientation and thus can be exploited for the enhancement. However, enhancement cannot be applied to poor quality regions [2]. Localized linear filters are commonly used as feature or texture extractors especially in classification and segmentation. Such filters have characteristics of bandpass filters with certain optimal joint localized properties in the spatial and frequency domain. One such filter is the Gabor filters, motivated by the studies by Daugman (1985) [3] on visual modelling of simple cells which found out the orientation selectivity of visual cortical neurons. Daugman has shown that Gabor filters provide optimal joint resolution in space and spatial-frequency and that Gabor filters exhibit spatial responses similar to receptive field profiles in mammalian vision. Gabor filters are employed in wide range of applications such as texture segmentation, retina identification, edge detection, image representation, etc. Gabor filters represent time varying signals that are localized in spatial and frequency domain by the product of a Gaussian function and a sinusoid. However, Gabor filters suffer a drawback due to having nonzero DC component which can significantly affect low frequency bands in the frequency domain. According to Kovesi (2015) [4], the maximum bandwidth of a Gabor fiter is about one octave and it will not be optimal if one is seeking broad spectral information. An alternative is the Log-Gabor filters proposed by Field (1987) [5]. The filter can only be analytically defined in frequency domain as Gaussian functions with a null DC component at the origin due to the singularity of the log function (it is a Gaussian function when one views it in the logarithmic frequency scale). The filter is a product of a radial component and an angular component. Field suggested that natural images are better coded in the logarithmic frequency so as not to over represent the low frequency components and under represent the high frequency components which seems to align with measurements on mammalian visual system that humans have cell response that are symmetric on the logarithmic frequency scale. Various researches in fingerprint enhancement using Gabor filters includes [2], [6]; on circular Gabor filters [7], [8]; and improved or enhanced Gabor filters [9], [10], and other types of filters such as anisotropic filters [11], [12], and frequency domain filtering [13], [14]. A comparative study on most major filtering approaches to texture feature extraction can be found in [15]. In this paper, a circular raised cosine filtering is proposed and implemented for fingerprint enhancement. Comparison has been made with circular Gabor filter and Log-Gabor filter. It has been found that raised cosine filtering produces similar filtered output as that of the Gabor filtering approach while having advantages of i) the frequency end drops gradually towards zero, ii) a residue which can perform lowpass filtering while maintaining narrow bandpass filtering. The 698
2 investigation is strictly performed based on fingerprint image enhancement. Circular Raised Cosine Filtering For fingerprint images the ridge information are all clustered in a circular spectrum at a distance proportional equal to the frequency of the ridge. In a local window, this feature is even more striking as shown in Fig.1. A simple circular raised cosine filtering can be used as the spectra information is not broad. Figure 1: Frequency spectra of fingerprint in a localized window The raised cosine filter has the advantage that the frequency tail drops gradually to zero, a feature both Gabor filters and Log- Gabor filters lack of. As the frequency tail tends towards infinity, the response of these two types of filters drops towards infinitesimal value. Fig. 2 shows the frequency response plot with normalized radius of Gabor filter, Log-Gabor filter and raised cosine filter at bandwidth of one octave and wavelength of 10 pixels. Fig. 2 is a close-up view of the frequency response plot towards higher frequency end. As can be seen, the raised cosine filter response end drops gradual to zero, while Gabor filter and Log-Gabor filter tends towards an infinitesimal value which is often thresholded to cut down unnecessary computation time. The choice of the threshold value (such as below 1% of amplitude or below 1% of the power spectrum) is often arbitrary and by employing raised cosine filtering with the same bandwidth, it eliminates one from thresholding arbitrarily. Fig. 3 shows the half-magnitude response plot. As observed, the raised cosine and Gabor filters exhibit an almost exact response and thus the filtered image should be very close or hard to discern from one another. Figure 2: Frequency response of circular Gabor filter, Log- Gabor filter and raised cosine filter Close-up view of the frequency tail In this paper, a six directional raised circular cosine filter is implemented. Fig. 4 shows the half-magnitude response plot of the six directional raised cosine filters at wavelengths (or ridge distances) of five, 10 and 20 pixels (the larger the wavelength, the smaller the concentric bands). The circular implementation of the various filters depends on two components, i.e. the radial component f(r) and the angular component f( ) as described by Eq. (1) below. f(r, )=f(r)f( ) (1) The angular component is given by 2 ( ) / ] f ( ) exp[ 2, (B / 2) 1/ 2log2 (2) o where o is the orientation angle and B is the angular bandwidth which is set to 2 /k, and k is the number of direction/orientation needed. The radial component for Gabor, Log-Gabor and raised cosine filters is as given in Table
3 Filter International Journal of Applied Engineering Research ISSN Volume 11, Number 1 (2016) pp Table 1: Radial component of different filters Radial component Gabor 2 f ( r) exp[ ( r f ) / 2 ] where / f 1/ 2log2[( 2 1) ( 2 1)] r Log-Gabor 2 f ( r) exp[ log( r / f ) / 2] where r BW r (BW / 2 ) log2 / 2 Raised cosine f ( r) 0. 5cos( ( r f )/ 2kf ) 0. 5 for ( 1 2k ) f r ( 1 2k) f where k ( 2 1) ( 2 1) Note: BW is the radial bandwidth BW BW r BW All design parameters assumed that the overlapping of the response occurred at half magnitude as can be seen from Fig. 3 and Fig. 4. Fig. 5 shows the parameters used to derive the raised cosine filter s radial component. The parameter k is 1/3 when the radial bandwidth BW required is one octave. Fig. 6 shows the representation of the radial component, angular component and raised cosine filter oriented at 0 o, 60 o, and 120 o at wavelength of 10 pixels. Fig. 7 shows the raised cosine filter s frequency spectrum (left) and its corresponding spatial domain. Note that both responses have been enlarged for ease of visual inspection. The spatial domain plot shows sinusoidal-like waveform with equally spaced wavelengths and thus proves that raised cosine filter can be used to enhanced fingerprint image which exhibit sinusoidal likeness of the same wavelength. Figure 4: Raised cosine response at half magnitude for six directions with bandwidth of one octave and wavelength of five, 10 and 20 pixels Figure 5: Raised cosine filter design parameters Figure 3: Filter response at four directions with one octave bandwidth and wavelength of 10 pixels (c) Figure 6: The radial component (left), angular component (middle) and filter representation (right) in the direction of 0 o 60 o (c) 120 o at ridge distance of 10 pixels 700
4 image in Fig. 8 while the quantized image in Fig. 8. As can be seen, the regions of the quantized image are in proportion to the number of directions used in the filtering process and are thus used in the selection of regions to obtain the final enhanced image. ˆ ~ I merged ( m, n) I n( m, n) where n (6) Ienhanced( m, n) H[ I merged ( m, n)] (7) (c) Figure 7: The filter s frequency spectrum (left) and its corresponding spatial domain (right) in the direction of 0 o 60 o (c) 120 o at ridge distance of 10 pixels Filtering Process The raised cosine frequency domain filtering process is as follows. First the input fingerprint image is transformed into the frequency domain via Fast Fourier Transform (FFT). That is M 1N 1 mu nv F( u, I( m, n) exp 2 i (3) M N m 0 n 0 where I(m,n) is the input image of size MxN. The radial component is designed at the same ridge distance r of the fingerprint image (the estimation of ridge distance is not discussed in this paper). The FFT image is then multiplied with each directional filter (six direction has been chosen) to obtain the directional filtered images. I ( u, F( u, f ( r, ), : 0 I ( u, F( u, f ( r, ), 1 I ( u, F( u, f ( r, ) where f(r, ) is the nth-directional filter with n=n /6, n=0,1,,5. The magnitude of inverse FFT images are then obtained M N mu nv I ˆ n ( m, n) I n ( u, exp 2 i MN M N (5) u 0 v 0 Each region is block selected based on the direction of quantized image of the orientation field ~. It is then histogram equalized H[.] to produce the enhanced fingerprint image. An example of the orientation field is as shown superimposed on an original (4) Figure 8: Orientation image Quantized orientation image Results and Discussion Fig. 9 shows an original fingerprint image and Fig. 9 the enhanced fingerprint image. Fig. 10 shows binary images of original fingerprint and enhanced fingerprint (both images have also been segmented) obtained using Otsu threshold. The binary image of the enhanced fingerprint also shows that many ridgelines are now connected and more well-defined as compared to the original image. For regions that are of poor quality, the enhancement process is unable to recover much visibility. Such poor quality regions are usually masked out and not considered. Fig. 11 shows the thinned images of the binary enhanced fingerprint images. Fig. 11 is the thinned image with short ridgelines of less than 30 pixels removed. Some postprocessing needs to be carried out to connect short 701
5 segments which are in alignment within certain tolerance and to remove false features such as the false minutiae structures of holes, spurs, spikes, and triangles if fingerprint matching is involved [16]. The enhanced fingerprint image as depicted in Fig. 9 are produced by using six directional raised cosine filters at bandwidth of three octaves which has been found optimum. Fig. 12 shows the results of each filtered image for each of the six orientations: 0 o, 30 o, 60 o, 90 o, 120 o, and 150 o. It is clearly revealed that regions that are aligned with the directions of the directional filters are more striking. The author has also discovered that, if the raised cosine function is bounded only at the high frequency tail while maintaining a residue at the low frequency end, raised cosine filter not only performs as a bandpass filter but also acts as a lowpass filter. For comparison, the frequency response plots with residue at one octave and without residue at three octaves are as depicted in Fig. 13. By inspecting the filter s response plots, one can notice a similarity and a difference. The difference is that the bandwidth is differed by three octaves and one octave. On the similarity, both the responses have a DC value at the origin. Clearly, the present of the DC value must have an influence on the filtered image. It has also been found that by using the residue as a lowpass filter, an enhanced image can be produced at bandwidth of just one octave as depicted in Fig. 14. The corresponding thinned image is shown in Fig. 14. Figure 10: Binary image of the original fingerprint enhanced fingerprint Figure 9: Original fingerprint Enhanced fingerprint image The enhanced image by raised cosine filtering is as good as those produced by with Gabor filter at the same bandwidth. This is as shown in Fig. 15. On the other hand, the Log- Gabor filter did not produce the expected enhancement. This is as depicted in Fig. 15. A careful comparison of raised cosine, Gabor and Log-Gabor filters response plots at bandwidth of three octaves as illustrated in Fig. 16 revealed that both raised cosine and Gabor filters have DC components while Log-Gabor is locked at null DC due to its design. As all filters are tested using the same bandwidth of three octaves, performance should be at par with each other. Due to the existence of the DC components for raised cosine and Gabor filters, the authors have again performed another experiment on Log-Gabor filter to see if the fingerprint image is rightly enhanced. Now, a DC component of 0.5 is experimentally added at the original of zero frequency of Log-Gabor filters. This is to confirm whether the DC component plays a role in fingerprint enhancement. Alas, Fig. 17 shows the enhanced fingerprint image of the Log- Gabor filters, which clearly shows that the fingerprint image is now enhanced. On the computation requirement, by averaging the time taken to perform the enhancement of 100 images of the NIST 702
6 Special Database 4 [17], the time is 4.16s, 4.09s and 4.07s for Log-Gabor, Gabor, and raised cosine filters. The test is carried out on Windows Vista, 32-bit OS using Intel Core Dual 1.5GHz and 2038 MB of RAM. All the source codes are written and tested using Matlab R2007a. while having advantage of i) the high frequency end drops gradually towards zero and thus eliminates one from thresholding, ii) a residue which can perform lowpass filtering while maintaining small bandpass filtering which is often more desirable than wide bandpass filtering. (c) (d) Figure 11: Thinned enhanced image enhanced image with short ridgelines removed Conclusion A fingerprint enhancement approach using raised circular cosine filter has been proposed. The frequency response characteristic is similar to that of the Gabor filters. The filter s response towards the higher frequency tail drops gradual to zero and thus eliminate the need for thresholding, which is usually carried out for Gabor filters and Log-Gabor filters as their response towards higher frequency tail tend towards infinitesimal value. It has been found that the raised cosine filtering performed at par with Gabor filtering at bandwidth of three octaves for the image investigated while the raised cosine filtering with residue at one octave bandwidth has also been found to produce the enhanced fingerprint image. Investigation has also shown that the raised DC component in the bandpass design is necessary to obtain the desired enhanced fingerprint image. This is clearly demonstrated by comparing Log-Gabor filter and the Log-Gabor filter with added DC value. On the timing requirement, it is found that raised cosine filtering is faster. In conclusion, the raised cosine filtering produces similar output to that of the Gabor filtering approach (e) Figure 12: Filtered images at 0 o 30 o (c) 60 o (d) 90 o (e) 120 o (f) 150 o (f) 703
7 Figure 13: Response plot Half magnitude plot Figure 15: Enhanced fingerprint image by Gabor filter Log-Gabor filter Figure 14: Enhanced image Thinned enhanced image of one octave raised cosine filtering with residue Figure 16: Response plots of raised cosine, Gabor, and Log- Gabor filters at three octaves of bandwidth 704
8 Figure 17: Enhanced fingerprint using Log-Gabor filters at three octaves with added DC value References [1] R. Rajkumar, and K. Hemachandran, A Review on Image Enhancement Using Directional Filters, Physical Sciences and Technology, vol. 7, no. 11, pp , [2] L. Hong, Y. Wan, and A. Jain, Fingerprint Image Enhancement: Algorithm and Performance Evaluation, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 20, no. 8, pp , [3] J.G. Daugman, Uncertainty Relation for Resolution in Space, Spatial Frequency, and Orientation Optimized by Two-Dimensional Visual Cortical Filters, Journal of the Optical Society of America A, vol. 2, no. 7, pp , [4] P. Kovesi, Log-Gabor filters, Online: Docs/convexpl.html [Accessed: June 2015] [5] D.J., Field, Relations Between the Statistics of Natural Images and the Response Properties of Cortical Cells, Journal of the Optical Society of America A, vol. 4, no. 12, pp , [6] A.K. Jain, S. Prabhakar, L. Hong, and S. Pankanti, Filterbank-based Fingerprint Matching, IEEE Transactions on Image Processing, vol. 9, no. 5, pp , [7] J. Zhang, T. Tan, and L. Ma, Invariant Texture Segmentation via Circular Gabor Filters, Proceedings of the 16th International Conference on Pattern Recognition, vol. 2, pp , [8] Z. Zhu, M. Tang, and H. Lu, A New Robust Circular Gabor Object Matching by Using Weighted Hausdorff Distance, Pattern Recogntion Letters, vol. 25, pp , [9] J. Yang, L. Liu, T. Jiang, and Y. Fan, A Modified Gabor Filter Design Method for Fingerprint Image Enhancement, Pattern Recognition Letters, vol. 24, pp , [10] H. Ke, H. Wang, and D. Kong, An Improved Gabor Filtering for Fingerprint Image Enhancement Technology, 2nd International Conference on Electronic & Mechanical Engineering and Information technology, pp , [11] S. Greenberg, M. Aladjem, and D. Kogan, Fingerprint Image Enhancement Using Filtering Techniques, Real-Time Imaging, vol. 8, pp , [12] R. Hastings, Ridge Enhancement in Fingerprint Images Using Oriented Diffusion, 9th Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications, pp , [13] B.G. Sherlock, D.M. Monro, and K. Millard, Fingerprint Enhancement by Directional Fourier Filtering, IEE Proc. Vis. Image Signal Process, vol. 141, no. 2, pp , [14] T. Ko., Fingerprint Enhancement by Spectral Analysis Technique, IEEE Applied Imagery Pattern Recognition Workshop, pp , [15] T. Randen, and J.H. Husoy, Filtering for Texture Classification: A Comparative Study, IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 21, no. 4, pp , [16] Q. Xiao, and H. Raafat, H. Fingerprint Image Postprocessing: A Combined Statistical and Structural Approach, Pattern Recognition, vol. 24, pp , [17] C.I. Watson, and C.L. Wilson, NIST Special Database 4: Fingerprint Database, National Institute of Standards and Technology,
Adaptive Fingerprint Binarization by Frequency Domain Analysis
Adaptive Fingerprint Binarization by Frequency Domain Analysis Josef Ström Bartůněk, Mikael Nilsson, Jörgen Nordberg, Ingvar Claesson Department of Signal Processing, School of Engineering, Blekinge Institute
More informationAlgorithm for Detection and Elimination of False Minutiae in Fingerprint Images
Algorithm for Detection and Elimination of False Minutiae in Fingerprint Images Seonjoo Kim, Dongjae Lee, and Jaihie Kim Department of Electrical and Electronics Engineering,Yonsei University, Seoul, Korea
More informationFingerprint Segmentation using the Phase of Multiscale Gabor Wavelets
CCV: The 5 th sian Conference on Computer Vision, 3-5 January, Melbourne, ustralia Fingerprint Segmentation using the Phase of Multiscale Gabor Wavelets Sylvain Bernard,, Nozha Boujemaa, David Vitale,
More informationAbstract Terminologies. Ridges: Ridges are the lines that show a pattern on a fingerprint image.
An Approach To Extract Minutiae Points From Enhanced Fingerprint Image Annu Saini Apaji Institute of Mathematics & Applied Computer Technology Department of computer Science and Electronics, Banasthali
More informationAn Algorithm for Fingerprint Image Postprocessing
An Algorithm for Fingerprint Image Postprocessing Marius Tico, Pauli Kuosmanen Tampere University of Technology Digital Media Institute EO.BOX 553, FIN-33101, Tampere, FINLAND tico@cs.tut.fi Abstract Most
More informationTouchless Fingerprint Recognization System
e-issn 2455 1392 Volume 2 Issue 4, April 2016 pp. 501-505 Scientific Journal Impact Factor : 3.468 http://www.ijcter.com Touchless Fingerprint Recognization System Biju V. G 1., Anu S Nair 2, Albin Joseph
More informationEffective and Efficient Fingerprint Image Postprocessing
Effective and Efficient Fingerprint Image Postprocessing Haiping Lu, Xudong Jiang and Wei-Yun Yau Laboratories for Information Technology 21 Heng Mui Keng Terrace, Singapore 119613 Email: hplu@lit.org.sg
More informationFingerprint Recognition using Minutiae Extraction
Fingerprint Recognition using Minutiae Extraction Krishna Kumar 1, Basant Kumar 2, Dharmendra Kumar 3 and Rachna Shah 4 1 M.Tech (Student), Motilal Nehru NIT Allahabad, India, krishnanitald@gmail.com 2
More informationTDI2131 Digital Image Processing
TDI2131 Digital Image Processing Image Enhancement in Spatial Domain Lecture 3 John See Faculty of Information Technology Multimedia University Some portions of content adapted from Zhu Liu, AT&T Labs.
More informationIntegrated Digital System for Yarn Surface Quality Evaluation using Computer Vision and Artificial Intelligence
Integrated Digital System for Yarn Surface Quality Evaluation using Computer Vision and Artificial Intelligence Sheng Yan LI, Jie FENG, Bin Gang XU, and Xiao Ming TAO Institute of Textiles and Clothing,
More informationFast identification of individuals based on iris characteristics for biometric systems
Fast identification of individuals based on iris characteristics for biometric systems J.G. Rogeri, M.A. Pontes, A.S. Pereira and N. Marranghello Department of Computer Science and Statistic, IBILCE, Sao
More informationAdaptive Optimum Notch Filter for Periodic Noise Reduction in Digital Images
Adaptive Optimum Notch Filter for Periodic Noise Reduction in Digital Images Payman Moallem i * and Majid Behnampour ii ABSTRACT Periodic noises are unwished and spurious signals that create repetitive
More informationIris Segmentation & Recognition in Unconstrained Environment
www.ijecs.in International Journal Of Engineering And Computer Science ISSN:2319-7242 Volume - 3 Issue -8 August, 2014 Page No. 7514-7518 Iris Segmentation & Recognition in Unconstrained Environment ABSTRACT
More informationOn-Line, Low-Cost and Pc-Based Fingerprint Verification System Based on Solid- State Capacitance Sensor
On-Line, Low-Cost and Pc-Based Fingerprint Verification System Based on Solid- State Capacitance Sensor Mohamed. K. Shahin *, Ahmed. M. Badawi **, and Mohamed. S. Kamel ** *B.Sc. Design Engineer at International
More information8.2 IMAGE PROCESSING VERSUS IMAGE ANALYSIS Image processing: The collection of routines and
8.1 INTRODUCTION In this chapter, we will study and discuss some fundamental techniques for image processing and image analysis, with a few examples of routines developed for certain purposes. 8.2 IMAGE
More informationContent Based Image Retrieval Using Color Histogram
Content Based Image Retrieval Using Color Histogram Nitin Jain Assistant Professor, Lokmanya Tilak College of Engineering, Navi Mumbai, India. Dr. S. S. Salankar Professor, G.H. Raisoni College of Engineering,
More informationLicense Plate Localisation based on Morphological Operations
License Plate Localisation based on Morphological Operations Xiaojun Zhai, Faycal Benssali and Soodamani Ramalingam School of Engineering & Technology University of Hertfordshire, UH Hatfield, UK Abstract
More information1.Discuss the frequency domain techniques of image enhancement in detail.
1.Discuss the frequency domain techniques of image enhancement in detail. Enhancement In Frequency Domain: The frequency domain methods of image enhancement are based on convolution theorem. This is represented
More informationFingerprint Feature Extraction Dileep Sharma (Assistant Professor) Electronics and communication Eternal University Baru Sahib, HP India
Volume 4, Issue 7, July 2014 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Shaifali Dogra
More informationImproved SIFT Matching for Image Pairs with a Scale Difference
Improved SIFT Matching for Image Pairs with a Scale Difference Y. Bastanlar, A. Temizel and Y. Yardımcı Informatics Institute, Middle East Technical University, Ankara, 06531, Turkey Published in IET Electronics,
More informationIris Recognition using Histogram Analysis
Iris Recognition using Histogram Analysis Robert W. Ives, Anthony J. Guidry and Delores M. Etter Electrical Engineering Department, U.S. Naval Academy Annapolis, MD 21402-5025 Abstract- Iris recognition
More informationTarget detection in side-scan sonar images: expert fusion reduces false alarms
Target detection in side-scan sonar images: expert fusion reduces false alarms Nicola Neretti, Nathan Intrator and Quyen Huynh Abstract We integrate several key components of a pattern recognition system
More informationInternational Journal of Engineering and Emerging Technology, Vol. 2, No. 1, January June 2017
Measurement of Face Detection Accuracy Using Intensity Normalization Method and Homomorphic Filtering I Nyoman Gede Arya Astawa [1]*, I Ketut Gede Darma Putra [2], I Made Sudarma [3], and Rukmi Sari Hartati
More informationANALYSIS OF PARTIAL IRIS RECOGNITION
ANALYSIS OF PARTIAL IRIS RECOGNITION Yingzi Du, Robert Ives, Bradford Bonney, Delores Etter Electrical Engineering Department, U.S. Naval Academy, Annapolis, MD, USA 21402 ABSTRACT In this paper, we investigate
More informationAn Adaptive Kernel-Growing Median Filter for High Noise Images. Jacob Laurel. Birmingham, AL, USA. Birmingham, AL, USA
An Adaptive Kernel-Growing Median Filter for High Noise Images Jacob Laurel Department of Electrical and Computer Engineering, University of Alabama at Birmingham, Birmingham, AL, USA Electrical and Computer
More informationVEHICLE LICENSE PLATE DETECTION ALGORITHM BASED ON STATISTICAL CHARACTERISTICS IN HSI COLOR MODEL
VEHICLE LICENSE PLATE DETECTION ALGORITHM BASED ON STATISTICAL CHARACTERISTICS IN HSI COLOR MODEL Instructor : Dr. K. R. Rao Presented by: Prasanna Venkatesh Palani (1000660520) prasannaven.palani@mavs.uta.edu
More informationImage Processing Lecture 4
Image Enhancement Image enhancement aims to process an image so that the output image is more suitable than the original. It is used to solve some computer imaging problems, or to improve image quality.
More informationA New Fake Iris Detection Method
A New Fake Iris Detection Method Xiaofu He 1, Yue Lu 1, and Pengfei Shi 2 1 Department of Computer Science and Technology, East China Normal University, Shanghai 200241, China {xfhe,ylu}@cs.ecnu.edu.cn
More informationSegmentation of Fingerprint Images
Segmentation of Fingerprint Images Asker M. Bazen and Sabih H. Gerez University of Twente, Department of Electrical Engineering, Laboratory of Signals and Systems, P.O. box 217-75 AE Enschede - The Netherlands
More informationNOVEL APPROACH OF ACCURATE IRIS LOCALISATION FORM HIGH RESOLUTION EYE IMAGES SUITABLE FOR FAKE IRIS DETECTION
International Journal of Information Technology and Knowledge Management July-December 2010, Volume 3, No. 2, pp. 685-690 NOVEL APPROACH OF ACCURATE IRIS LOCALISATION FORM HIGH RESOLUTION EYE IMAGES SUITABLE
More informationData Embedding Using Phase Dispersion. Chris Honsinger and Majid Rabbani Imaging Science Division Eastman Kodak Company Rochester, NY USA
Data Embedding Using Phase Dispersion Chris Honsinger and Majid Rabbani Imaging Science Division Eastman Kodak Company Rochester, NY USA Abstract A method of data embedding based on the convolution of
More informationFrequency Domain Median-like Filter for Periodic and Quasi-Periodic Noise Removal
Header for SPIE use Frequency Domain Median-like Filter for Periodic and Quasi-Periodic Noise Removal Igor Aizenberg and Constantine Butakoff Neural Networks Technologies Ltd. (Israel) ABSTRACT Removal
More informationStochastic Screens Robust to Mis- Registration in Multi-Pass Printing
Published as: G. Sharma, S. Wang, and Z. Fan, "Stochastic Screens robust to misregistration in multi-pass printing," Proc. SPIE: Color Imaging: Processing, Hard Copy, and Applications IX, vol. 5293, San
More informationCOMBINING FINGERPRINTS FOR SECURITY PURPOSE: ENROLLMENT PROCESS MISS.RATHOD LEENA ANIL
COMBINING FINGERPRINTS FOR SECURITY PURPOSE: ENROLLMENT PROCESS MISS.RATHOD LEENA ANIL Department of Electronics and Telecommunication, V.V.P. Institute of Engg & Technology,Solapur University Solapur,
More informationA Generative Model for Fingerprint Minutiae
A Generative Model for Fingerprint Minutiae Qijun Zhao, Yi Zhang Sichuan University {qjzhao, yi.zhang}@scu.edu.cn Anil K. Jain Michigan State University jain@cse.msu.edu Nicholas G. Paulter Jr., Melissa
More informationCLASSIFICATION OF MULTIPLE SIGNALS USING 2D MATCHING OF MAGNITUDE-FREQUENCY DENSITY FEATURES
Proceedings of the SDR 11 Technical Conference and Product Exposition, Copyright 2011 Wireless Innovation Forum All Rights Reserved CLASSIFICATION OF MULTIPLE SIGNALS USING 2D MATCHING OF MAGNITUDE-FREQUENCY
More informationDEFOCUS BLUR PARAMETER ESTIMATION TECHNIQUE
International Journal of Electronics and Communication Engineering and Technology (IJECET) Volume 7, Issue 4, July-August 2016, pp. 85 90, Article ID: IJECET_07_04_010 Available online at http://www.iaeme.com/ijecet/issues.asp?jtype=ijecet&vtype=7&itype=4
More informationSpectro-Temporal Methods in Primary Auditory Cortex David Klein Didier Depireux Jonathan Simon Shihab Shamma
Spectro-Temporal Methods in Primary Auditory Cortex David Klein Didier Depireux Jonathan Simon Shihab Shamma & Department of Electrical Engineering Supported in part by a MURI grant from the Office of
More informationAn Evaluation of Automatic License Plate Recognition Vikas Kotagyale, Prof.S.D.Joshi
An Evaluation of Automatic License Plate Recognition Vikas Kotagyale, Prof.S.D.Joshi Department of E&TC Engineering,PVPIT,Bavdhan,Pune ABSTRACT: In the last decades vehicle license plate recognition systems
More informationLinear Gaussian Method to Detect Blurry Digital Images using SIFT
IJCAES ISSN: 2231-4946 Volume III, Special Issue, November 2013 International Journal of Computer Applications in Engineering Sciences Special Issue on Emerging Research Areas in Computing(ERAC) www.caesjournals.org
More informationRESEARCH PAPER FOR ARBITRARY ORIENTED TEAM TEXT DETECTION IN VIDEO IMAGES USING CONNECTED COMPONENT ANALYSIS
International Journal of Latest Trends in Engineering and Technology Vol.(7)Issue(4), pp.137-141 DOI: http://dx.doi.org/10.21172/1.74.018 e-issn:2278-621x RESEARCH PAPER FOR ARBITRARY ORIENTED TEAM TEXT
More informationFourier Transform. Any signal can be expressed as a linear combination of a bunch of sine gratings of different frequency Amplitude Phase
Fourier Transform Fourier Transform Any signal can be expressed as a linear combination of a bunch of sine gratings of different frequency Amplitude Phase 2 1 3 3 3 1 sin 3 3 1 3 sin 3 1 sin 5 5 1 3 sin
More informationIntroduction of Audio and Music
1 Introduction of Audio and Music Wei-Ta Chu 2009/12/3 Outline 2 Introduction of Audio Signals Introduction of Music 3 Introduction of Audio Signals Wei-Ta Chu 2009/12/3 Li and Drew, Fundamentals of Multimedia,
More informationKeyword: Morphological operation, template matching, license plate localization, character recognition.
Volume 4, Issue 11, November 2014 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Automatic
More informationIDENTIFICATION OF FISSION GAS VOIDS. Ryan Collette
IDENTIFICATION OF FISSION GAS VOIDS Ryan Collette Introduction The Reduced Enrichment of Research and Test Reactor (RERTR) program aims to convert fuels from high to low enrichment in order to meet non-proliferation
More informationGaussian and Fast Fourier Transform for Automatic Retinal Optic Disc Detection
Gaussian and Fast Fourier Transform for Automatic Retinal Optic Disc Detection Arif Muntasa 1, Indah Agustien Siradjuddin 2, and Moch Kautsar Sophan 3 Informatics Department, University of Trunojoyo Madura,
More informationImage Enhancement using Histogram Equalization and Spatial Filtering
Image Enhancement using Histogram Equalization and Spatial Filtering Fari Muhammad Abubakar 1 1 Department of Electronics Engineering Tianjin University of Technology and Education (TUTE) Tianjin, P.R.
More informationGuided Image Filtering for Image Enhancement
International Journal of Research Studies in Science, Engineering and Technology Volume 1, Issue 9, December 2014, PP 134-138 ISSN 2349-4751 (Print) & ISSN 2349-476X (Online) Guided Image Filtering for
More informationPreprocessing of Digitalized Engineering Drawings
Modern Applied Science; Vol. 9, No. 13; 2015 ISSN 1913-1844 E-ISSN 1913-1852 Published by Canadian Center of Science and Education Preprocessing of Digitalized Engineering Drawings Matúš Gramblička 1 &
More informationAudio Fingerprinting using Fractional Fourier Transform
Audio Fingerprinting using Fractional Fourier Transform Swati V. Sutar 1, D. G. Bhalke 2 1 (Department of Electronics & Telecommunication, JSPM s RSCOE college of Engineering Pune, India) 2 (Department,
More informationPublished by: PIONEER RESEARCH & DEVELOPMENT GROUP (www.prdg.org) 1
IJREAT International Journal of Research in Engineering & Advanced Technology, Volume 2, Issue 2, Apr- Generating an Iris Code Using Iris Recognition for Biometric Application S.Banurekha 1, V.Manisha
More informationAn Efficient Color Image Segmentation using Edge Detection and Thresholding Methods
19 An Efficient Color Image Segmentation using Edge Detection and Thresholding Methods T.Arunachalam* Post Graduate Student, P.G. Dept. of Computer Science, Govt Arts College, Melur - 625 106 Email-Arunac682@gmail.com
More informationColor Image Segmentation Using K-Means Clustering and Otsu s Adaptive Thresholding
Color Image Segmentation Using K-Means Clustering and Otsu s Adaptive Thresholding Vijay Jumb, Mandar Sohani, Avinash Shrivas Abstract In this paper, an approach for color image segmentation is presented.
More informationCLASSIFICATION OF MULTIPLE SIGNALS USING 2D MATCHING OF MAGNITUDE-FREQUENCY DENSITY FEATURES
CLASSIFICATION OF MULTIPLE SIGNALS USING 2D MATCHING OF MAGNITUDE-FREQUENCY DENSITY FEATURES Aaron Roof (Vanteon Corporation, Fairport, NY; aroof@vanteon.com); Adly Fam (Dept. of Electrical Engineering,
More informationIntroduction to Video Forgery Detection: Part I
Introduction to Video Forgery Detection: Part I Detecting Forgery From Static-Scene Video Based on Inconsistency in Noise Level Functions IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 5,
More informationA new seal verification for Chinese color seal
Edith Cowan University Research Online ECU Publications 2011 2011 A new seal verification for Chinese color seal Zhihu Huang Jinsong Leng Edith Cowan University 10.4028/www.scientific.net/AMM.58-60.2558
More informationIris Recognition using Hamming Distance and Fragile Bit Distance
IJSRD - International Journal for Scientific Research & Development Vol. 3, Issue 06, 2015 ISSN (online): 2321-0613 Iris Recognition using Hamming Distance and Fragile Bit Distance Mr. Vivek B. Mandlik
More informationExamples of image processing
Examples of image processing Example 1: We would like to automatically detect and count rings in the image 3 Detection by correlation Correlation = degree of similarity Correlation between f(x, y) and
More informationAn Improved Bernsen Algorithm Approaches For License Plate Recognition
IOSR Journal of Electronics and Communication Engineering (IOSR-JECE) ISSN: 78-834, ISBN: 78-8735. Volume 3, Issue 4 (Sep-Oct. 01), PP 01-05 An Improved Bernsen Algorithm Approaches For License Plate Recognition
More informationImage Smoothening and Sharpening using Frequency Domain Filtering Technique
Volume 5, Issue 4, April (17) Image Smoothening and Sharpening using Frequency Domain Filtering Technique Swati Dewangan M.Tech. Scholar, Computer Networks, Bhilai Institute of Technology, Durg, India.
More informationPARAMETER IDENTIFICATION IN RADIO FREQUENCY COMMUNICATIONS
Review of the Air Force Academy No 3 (27) 2014 PARAMETER IDENTIFICATION IN RADIO FREQUENCY COMMUNICATIONS Marius-Alin BELU Military Technical Academy, Bucharest Abstract: Modulation detection is an essential
More informationStamp detection in scanned documents
Annales UMCS Informatica AI X, 1 (2010) 61-68 DOI: 10.2478/v10065-010-0036-6 Stamp detection in scanned documents Paweł Forczmański Chair of Multimedia Systems, West Pomeranian University of Technology,
More informationA Proficient Matching For Iris Segmentation and Recognition Using Filtering Technique
A Proficient Matching For Iris Segmentation and Recognition Using Filtering Technique Ms. Priti V. Dable 1, Prof. P.R. Lakhe 2, Mr. S.S. Kemekar 3 Ms. Priti V. Dable 1 (PG Scholar) Comm (Electronics) S.D.C.E.
More informationInternational Journal of Advanced Research in Computer Science and Software Engineering
Volume 3, Issue 4, April 2013 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com A Novel Approach
More informationLOCAL MULTISCALE FREQUENCY AND BANDWIDTH ESTIMATION. Hans Knutsson Carl-Fredrik Westin Gösta Granlund
LOCAL MULTISCALE FREQUENCY AND BANDWIDTH ESTIMATION Hans Knutsson Carl-Fredri Westin Gösta Granlund Department of Electrical Engineering, Computer Vision Laboratory Linöping University, S-58 83 Linöping,
More informationExperiments with An Improved Iris Segmentation Algorithm
Experiments with An Improved Iris Segmentation Algorithm Xiaomei Liu, Kevin W. Bowyer, Patrick J. Flynn Department of Computer Science and Engineering University of Notre Dame Notre Dame, IN 46556, U.S.A.
More informationImplementation of Barcode Localization Technique using Morphological Operations
Implementation of Barcode Localization Technique using Morphological Operations Savreet Kaur Student, Master of Technology, Department of Computer Engineering, ABSTRACT Barcode Localization is an extremely
More informationImplementation of Band Pass Filter for Homomorphic Filtering Technique
INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND MOBILE APPLICATIONS Implementation of Band Pass Filter for Homomorphic Filtering Technique Pin Yang Tan 1, Haidi Ibrahim 2 1 School of Electrical & Electronic
More informationOrthonormal bases and tilings of the time-frequency plane for music processing Juan M. Vuletich *
Orthonormal bases and tilings of the time-frequency plane for music processing Juan M. Vuletich * Dept. of Computer Science, University of Buenos Aires, Argentina ABSTRACT Conventional techniques for signal
More informationFeature Extraction of Human Lip Prints
Journal of Current Computer Science and Technology Vol. 2 Issue 1 [2012] 01-08 Corresponding Author: Samir Kumar Bandyopadhyay, Department of Computer Science, Calcutta University, India. Email: skb1@vsnl.com
More informationSupplementary Materials for
advances.sciencemag.org/cgi/content/full/1/11/e1501057/dc1 Supplementary Materials for Earthquake detection through computationally efficient similarity search The PDF file includes: Clara E. Yoon, Ossian
More informationQuantitative Assessment of the Individuality of Friction Ridge Patterns
Quantitative Assessment of the Individuality of Friction Ridge Patterns Sargur N. Srihari with H. Srinivasan, G. Fang, P. Phatak, V. Krishnaswamy Department of Computer Science and Engineering University
More informationDesign of Various Image Enhancement Techniques - A Critical Review
Design of Various Image Enhancement Techniques - A Critical Review Moole Sasidhar M.Tech Department of Electronics and Communication Engineering, Global College of Engineering and Technology(GCET), Kadapa,
More informationMethod for Real Time Text Extraction of Digital Manga Comic
Method for Real Time Text Extraction of Digital Manga Comic Kohei Arai Information Science Department Saga University Saga, 840-0027, Japan Herman Tolle Software Engineering Department Brawijaya University
More information771 Series LASER SPECTRUM ANALYZER. The Power of Precision in Spectral Analysis. It's Our Business to be Exact! bristol-inst.com
771 Series LASER SPECTRUM ANALYZER The Power of Precision in Spectral Analysis It's Our Business to be Exact! bristol-inst.com The 771 Series Laser Spectrum Analyzer combines proven Michelson interferometer
More informationAn Efficient Approach for Iris Recognition by Improving Iris Segmentation and Iris Image Compression
An Efficient Approach for Iris Recognition by Improving Iris Segmentation and Iris Image Compression K. N. Jariwala, SVNIT, Surat, India U. D. Dalal, SVNIT, Surat, India Abstract The biometric person authentication
More informationFrequency Domain Based MSRCR Method for Color Image Enhancement
Frequency Domain Based MSRCR Method for Color Image Enhancement Siddesha K, Kavitha Narayan B M Assistant Professor, ECE Dept., Dr.AIT, Bangalore, India, Assistant Professor, TCE Dept., Dr.AIT, Bangalore,
More informationPreprocessing and postprocessing for skeleton-based fingerprint minutiae extraction
Pattern Recognition 40 (2007) 1270 1281 www.elsevier.com/locate/pr Preprocessing and postprocessing for skeleton-based fingerprint minutiae extraction Feng Zhao, Xiaoou Tang Department of Information Engineering,
More informationColor Constancy Using Standard Deviation of Color Channels
2010 International Conference on Pattern Recognition Color Constancy Using Standard Deviation of Color Channels Anustup Choudhury and Gérard Medioni Department of Computer Science University of Southern
More informationhttp://www.diva-portal.org This is the published version of a paper presented at SAI Annual Conference on Areas of Intelligent Systems and Artificial Intelligence and their Applications to the Real World
More informationKeywords: Image segmentation, pixels, threshold, histograms, MATLAB
Volume 6, Issue 3, March 2016 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Analysis of Various
More informationNON UNIFORM BACKGROUND REMOVAL FOR PARTICLE ANALYSIS BASED ON MORPHOLOGICAL STRUCTURING ELEMENT:
IJCE January-June 2012, Volume 4, Number 1 pp. 59 67 NON UNIFORM BACKGROUND REMOVAL FOR PARTICLE ANALYSIS BASED ON MORPHOLOGICAL STRUCTURING ELEMENT: A COMPARATIVE STUDY Prabhdeep Singh1 & A. K. Garg2
More informationWide-Band Enhancement of TV Images for the Visually Impaired
Wide-Band Enhancement of TV Images for the Visually Impaired E. Peli, R.B. Goldstein, R.L. Woods, J.H. Kim, Y.Yitzhaky Schepens Eye Research Institute, Harvard Medical School, Boston, MA Association for
More informationA Proficient Roi Segmentation with Denoising and Resolution Enhancement
ISSN 2278 0211 (Online) A Proficient Roi Segmentation with Denoising and Resolution Enhancement Mitna Murali T. M. Tech. Student, Applied Electronics and Communication System, NCERC, Pampady, Kerala, India
More informationQuality Measure of Multicamera Image for Geometric Distortion
Quality Measure of Multicamera for Geometric Distortion Mahesh G. Chinchole 1, Prof. Sanjeev.N.Jain 2 M.E. II nd Year student 1, Professor 2, Department of Electronics Engineering, SSVPSBSD College of
More informationOptical transfer function shaping and depth of focus by using a phase only filter
Optical transfer function shaping and depth of focus by using a phase only filter Dina Elkind, Zeev Zalevsky, Uriel Levy, and David Mendlovic The design of a desired optical transfer function OTF is a
More informationROBOT VISION. Dr.M.Madhavi, MED, MVSREC
ROBOT VISION Dr.M.Madhavi, MED, MVSREC Robotic vision may be defined as the process of acquiring and extracting information from images of 3-D world. Robotic vision is primarily targeted at manipulation
More informationInternational Journal of Advance Research in Computer Science and Management Studies
Volume 3, Issue 2, February 2015 ISSN: 2321 7782 (Online) International Journal of Advance Research in Computer Science and Management Studies Research Article / Survey Paper / Case Study Available online
More informationDifferent Approaches of Spectral Subtraction Method for Speech Enhancement
ISSN 2249 5460 Available online at www.internationalejournals.com International ejournals International Journal of Mathematical Sciences, Technology and Humanities 95 (2013 1056 1062 Different Approaches
More informationMassachusetts Institute of Technology Dept. of Electrical Engineering and Computer Science Fall Semester, Introduction to EECS 2
Massachusetts Institute of Technology Dept. of Electrical Engineering and Computer Science Fall Semester, 2006 6.082 Introduction to EECS 2 Modulation and Demodulation Introduction A communication system
More informationTransforms and Frequency Filtering
Transforms and Frequency Filtering Khalid Niazi Centre for Image Analysis Swedish University of Agricultural Sciences Uppsala University 2 Reading Instructions Chapter 4: Image Enhancement in the Frequency
More informationAcoustics, signals & systems for audiology. Week 4. Signals through Systems
Acoustics, signals & systems for audiology Week 4 Signals through Systems Crucial ideas Any signal can be constructed as a sum of sine waves In a linear time-invariant (LTI) system, the response to a sinusoid
More informationAn Audio Fingerprint Algorithm Based on Statistical Characteristics of db4 Wavelet
Journal of Information & Computational Science 8: 14 (2011) 3027 3034 Available at http://www.joics.com An Audio Fingerprint Algorithm Based on Statistical Characteristics of db4 Wavelet Jianguo JIANG
More informationClassification in Image processing: A Survey
Classification in Image processing: A Survey Rashmi R V, Sheela Sridhar Department of computer science and Engineering, B.N.M.I.T, Bangalore-560070 Department of computer science and Engineering, B.N.M.I.T,
More informationA Novel Approach of Compressing Images and Assessment on Quality with Scaling Factor
A Novel Approach of Compressing Images and Assessment on Quality with Scaling Factor Umesh 1,Mr. Suraj Rana 2 1 M.Tech Student, 2 Associate Professor (ECE) Department of Electronic and Communication Engineering
More informationFPGA implementation of DWT for Audio Watermarking Application
FPGA implementation of DWT for Audio Watermarking Application Naveen.S.Hampannavar 1, Sajeevan Joseph 2, C.B.Bidhul 3, Arunachalam V 4 1, 2, 3 M.Tech VLSI Students, 4 Assistant Professor Selection Grade
More informationLecture 3 Digital image processing.
Lecture 3 Digital image processing. MI_L3 1 Analog image digital image 2D image matrix of pixels scanner reflection mode analog-to-digital converter (ADC) digital image MI_L3 2 The process of converting
More informationContrast adaptive binarization of low quality document images
Contrast adaptive binarization of low quality document images Meng-Ling Feng a) and Yap-Peng Tan b) School of Electrical and Electronic Engineering, Nanyang Technological University, Nanyang Avenue, Singapore
More informationFeature Extraction Technique Based On Circular Strip for Palmprint Recognition
Feature Extraction Technique Based On Circular Strip for Palmprint Recognition Dr.S.Valarmathy 1, R.Karthiprakash 2, C.Poonkuzhali 3 1, 2, 3 ECE Department, Bannari Amman Institute of Technology, Sathyamangalam
More informationDESIGN & DEVELOPMENT OF COLOR MATCHING ALGORITHM FOR IMAGE RETRIEVAL USING HISTOGRAM AND SEGMENTATION TECHNIQUES
International Journal of Information Technology and Knowledge Management July-December 2011, Volume 4, No. 2, pp. 585-589 DESIGN & DEVELOPMENT OF COLOR MATCHING ALGORITHM FOR IMAGE RETRIEVAL USING HISTOGRAM
More information