October 2022: Top 10 Read Articles in Signal & Image Processing

October 2022: Top 10
Read Articles in Signal &
ImageProcessing
Signal & Image Processing: An
International Journal (SIPIJ)
***WJCI Indexed***
ISSN: 0976 – 710X [Online]; 2229 – 3922 [Print]
https://www.airccse.org/journal/sipij/index.html
Citations, h-index, i10-index
Citations 4791 h-index 32 i10-index 115

AGAUSSIAN MIXTURE MODEL BASED SPEECH RECOGNITION SYSTEM USING
MATLAB
Manan Vyas
B.E Electronics, University of Mumbai
ABSTRACT
This paper aims at development and performance analysis of a speaker dependent speech recognition
system using MATLAB®. The issues that were considered are 1) Can Matlab, be effectively used to
complete the aforementioned task, 2) Accuracy of the Gaussian Mixture Model used for parametric
modelling, 3) Performance analysis of the system, 4) Performance of the Gaussian Mixture Model as a
parametric modelling technique as compared to other modelling technique and 5) Can a Matlab® based
Speech recognition system be ported to a real world environment for recording and performing complex
voice commands. The aforementioned system is designed to recognize isolated utterances of digits 0-9.
The system is developed such that it can easily be extended to multisyllabic words as well.
KEYWORDS
Automatic Speech Recognition (ASR), Feature Extraction, Fast Fourier transform, Discrete Cosine
Transform, Linear Prediction (LPC), Mel Frequency Cepstral Co-efficient (MFCC), Gaussian Mixture
Model (GMM).
Full Text : https://aircconline.com/sipij/V4N4/4413sipij09.pdf
http://www.airccse.org/journal/sipij/vol4.html

REFERENCES
[1] X.Huang, A. Acero, and H.-W. Hon, “Spoken Language Processing: A Guide to Theory, Algorithm
and System Development”. Prentice Hall PTR May 2001
[2] Matthew Nicholas Stuttle, “A Gaussian Mixture Model Spectral Representation for Speech
Recognition”. Hughes Hall and Cambridge University Engineering Department. July 2003
[3] L. R. Rabiner, A tutorial on hidden Markov models and selected applications in speech recognition,"
Proceedings of the IEEE, vol. 77, pp. 257-286, Feb 1989.
AUTHOR
Manan Vyas received his Bachelor of Engineering in Electronics degree from
University of Mumbai in July 2012. He has also completed MITx 6.002 – a pilot
course on Circuits and Electronics by Massachusetts Institute of Technology with an
A grade. He is also a recipient of the J.R.D Tata Scholarship for excellent academics
during his engineering. His passions include playing football and trekking.

TWO NEW APPROACHES FOR SECURED IMAGE STEGANOGRAPHY USING
CRYPTOGRAPHIC TECHNIQUES AND TYPE CONVERSIONS
Sujay Narayana1
and Gaurav Prasad2
1
Department of Electronics and Communication, NITK, Surathkal, INDIA
2
Department of Information Technology, NITK, Surathkal, INDIA
ABSTRACT
The science of securing a data by encryption is Cryptography whereas the method of hiding secret
messages in other messages is Steganography, so that the secret’s very existence is concealed. The term
‘Steganography’ describes the method of hiding cognitive content in another medium to avoid detection
by the intruders. This paper introduces two new methods wherein cryptography and steganography are
combined to encrypt the data as well as to hide the encrypted data in another medium so the fact that a
message being sent is concealed. One of the methods shows how to secure the image by converting it into
cipher text by S-DES algorithm using a secret key and conceal this text in another image by
steganographic method. Another method shows a new way of hiding an image in another image by
encrypting the image directly by S-DES algorithm using a key image and the data obtained is concealed
in another image. The proposed method prevents the possibilities of steganalysis also.
KEYWORDS
Steganography, Cryptography, image hiding, least-significant bit (LSB) method
Full Text : http://aircconline.com/sipij/V1N2/1210sipij06.pdf

REFERENCES
[1] Clair, Bryan. “Steganography: How to Send a Secret Message.” 8 Nov. 2001
www.strangehorizons.com/2001/20011008/steganography.shtml
[2] R.J. Anderson and F. A. P. Petitcolas (2001) On the limits of the Stegnography, IEEE Journal Selected
Areas in Communications, 16(4), pp. 474-481.
[3] Johnson, Neil F., and SushilJajodia. “Exploring Steganography: Seeing the Unseen.” IEEE Computer
Feb. 1998: 26-34
[4] Westfeld, A., and G. Wolf, Steganography in a Video conferencing system, in proceedings of the
second international workshop on information hiding, vol. 1525 of lecture notes in computer
science,Springer, 1998. pp. 32-47.
[5] Krenn, R., “Steganography and Steganalysis”, http://www.Krenn.nl/univ/cry/steg/article.pdf
[6] E. Biham, A. Shamir. “Differential cryptanalysis of DES-like cryptosystems,” Journal of Cryptology,
vol. 4, pp. 3-72, January 1991.
[7] T. Moerland, “Steganography and Steganalysis”, Leiden Institute of Advanced Computing Science,
www.Liacs.nl/home/tmoerl/priytech.pdf
[8] A. Ker, “Improved detection of LSB steganography in grayscale images,” in Proc. Information
Hiding Workshop, vol. 3200, Springer LNCS, pp. 97–115, 2004.
[9] A. Ker, “Steganalysis of LSB matching in greyscale images,” IEEE Signal Process. Lett., Vol. 12,
No. 6, pp. 441–444, June 2005
[10]C. C. Lin, and W. H. Tsai, "Secret Image Sharing with Steganography and Authentication," Journal
of Systems and Software, 73(3):405-414, December 2004.
[11]N. F. Johnson and S. Jajodia, “Steganalysis of Images Created using Current Steganography
Software,” Lecture Notes in Computer Science, vol. 1525, pp. 32 – 47, Springer Verlag, 1998.
[12]J. Fridrich, M. Long, “Steganalysis of LSB encoding in colorimages,”Multimedia and Expo, vol. 3,
pp. 1279-1282, July 2000.
[13]KafaRabah. Steganography - The Art of Hiding Data. Information technology Journal 3 (3) - 2004.
[14]A. Westfeld, "F5-A Steganographic Algorithm: High Capacity Despite Better Steganalysis," LNCS,
Vol. 2137, pp. 289-302,April 2001.
[15] C.-C. Chang, T. D. Kieu, and Y.-C. Chou, "A High Payload Steganographic Scheme Based on (7, 4)
Hamming Code for Digital Images," Proc. of the 2008 International Symposium onElectronic
Commerce and Security, pp.16-21, August 2008.
[16] Jiri Fridrich ,Du Dui, “Secure Steganographic Method for Palette Images,” 3rd Int. Workshop on
InformationHiding, pp.47-66, 1999.
[17] R. Chandramouli, M. Kharrazi, N. Memon, “Image Steganography and Steganalysis: Concepts and
Practice “ , International Workshop on DigitalWatermarking, Seoul, October 2004.
[18] K. Kim, S. Park, and S. Lee, “Reconstruction of s2DES S–Boxes and their Immunity to
DifferentialCryptanalysis,” Proceedings of the 1993 Korea–Japan Workshop on Information Security
and Cryptography, Seoul, Korea, 24–26 Oct 1993, pp. 282–291.
[19] S. Dumitrescu, W.X.Wu and N. Memon (2002) On steganalysis of random LSB embedding in
continuous-tone images, Proc. International Conference on Image Processing, Rochester, NY, pp. 641-
644.
[20] William Stallings, Cryptography and Network Security, Principles and Practice, Third edition,
PearsonEducation, Singapore, 2003.
[21]Hide & Seek: An Introduction to Stegnography: http:niels.xtdnet.nl/papers/practical.pdf.
[22] Y. Lee and L. Chen (2000) High capacity image steganographic model, IEE Proceedings on
Vision,Image and Signal Processing, 147(3), pp. 288-294.
[23]T. Morkel, J. H. P. Eloff, M. S. Olivier, ”An Overview of Image Steganography”, Information and
Computer Security Architecture (ICSA) Research Group, Department of Computer Science, University of
Pretoria, SA.

AUTHORS
Sujay Narayana received the BE degree in Electronics and Communication from
KVG College of Engineering, Sullia, in 2009. He is currently with the
Department of Electronics and Communication, National Institute of Technology
Karnataka, Surathkal.
Gaurav Prasad received the BE degree in Information Science from P.A College of
Engineering, Nadupadavu, Mangalore in 2006 and MTech degree in Information
Security from NITK, Surathkal . He is currently with the Department of Information
Technology, National Institute of Technology Karnataka, Surathkal.

CONTENT BASED IMAGE RETRIEVAL USING COLOR AND TEXTURE
Manimala Singha and K.Hemachandran
Dept. of Computer Science, Assam University, Silchar India. Pin code 788011
ABSTRACT
The increased need of content based image retrieval technique can be found in a number of different
domains such as Data Mining, Education, Medical Imaging, Crime Prevention, Weather forecasting,
Remote Sensing and Management of Earth Resources. This paper presents the content based image
retrieval, using features like texture and color, called WBCHIR (Wavelet Based Color Histogram Image
Retrieval).The texture and color features are extracted through wavelet transformation and color
histogram and the combination of these features is robust to scaling and translation of objects in an image.
The proposed system has demonstrated a promising and faster retrieval method on a WANG image
database containing 1000 general-purpose color images. The performance has been evaluated by
comparing with the existing systems in the literature.
KEYWORDS
Image Retrieval, Color Histogram, Color Spaces, Quantization, Similarity Matching, Haar Wavelet,
Precision and Recall.

REFERENCES
1. R. Datta, D. Joshi, J. Li and J. Z. Wang, “Image retrieval: Ideas, influences, and trends of the new
age”, ACM computing Survey, vol.40, no.2, pp.1-60, 2008.
2. J. Eakins and M. Graham, “Content-Based Image Retrieval”, Technical report, JISC Technology
Applications Programme, 1999.
3. Y. Rui, T. S. Huang and S.F. Chang, “Image Retrieval: Current Techniques, Promising Directions and
Open Issues. Journal of Visual Communication and Image Representation. 10(4): pp. 39-62. 1999.
4. A. M. Smeulders, M. Worring and S. Santini, A. Gupta and R. Jain, “Content Based Image Retrieval
at the End of the Early Years”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(12):
pp. 1349-1380, 2000.
5. Y. Liu, D. Zang, G. Lu and W. Y. Ma, “A survey of content-based image retrieval with high-level
semantics”, Pattern Recognition, Vol-40, pp-262-282, 2007.
6. T. Kato, “Database architecture for content-based image retrieval”, In Proceedings of the SPIE - The
International Society for Optical Engineering, vol.1662, pp.112-113, 1992.
7. M. Flickner, H Sawhney, W. Niblack, J. Ashley, Q. Huang, B. Dom, M. Gorkani, J. Hafne, D. Lee, D.
Petkovic, D. Steele and P. Yanker, “Query by Image and Video Content The QBIC System” IEEE
Computer, pp-23-32, 1995.
8. A. Gupta and R. Jain. Visual information retrieval, Communications of the ACM 40 (5), 70–79. 1997.
9. A. Pentland, R.W. Picard and S. Scaroff, “Photobook: Content-Based Manipulation for Image
Databases”, International Journal of Computer Vision 18 (3), pp233–254. 1996.
10. J. R. Smith and S.F. Chang, “VisualSEEk: a fully automated content-based image query system”,
ACM Multimedia, 1996.
11. J. Wang, G. Wiederhold, O. Firschein and S. We, “Content-based Image Indexing and Searching
Using Daubechies’ Wavelets”, International Journal on Digital Libraries (IJODL) 1, (4). pp. 311–328,
1998.
12. C. Carson, S. Belongie, H. Greenspan and J. Malik, “Blobworld: image segmentation using
expectation-maximization and its application to image querying”, IEEE Trans. Pattern Anal. Mach. Intell.
8 (8), pp. 1026–1038, 2002.
13. J. Wang, J. LI and G. Wiederhold, “SIMPLIcity: Semantics-sensitive integrated matching for picture
libraries”, IEEE Transactions on Pattern Analysis and Machine Intelligence. 23, 9, pp. 947–963, 2001.
14. C.H. Lin, R.T. Chen and Y.K. Chan, “A smart content-based image retrieval system based on color
and texture feature”, Image and Vision Computing vol.27, pp.658–665, 2009.
15. J. Huang and S. K. Ravi, “Image Indexing Using Color Correlograms” , Proceedings of the IEEE
Conference, Computer Vision and Pattern Recognition, Puerto Rico, Jun. 1997.
16. G. Pass and R. Zabih, “Refinement Histogram for Content-Based Image Retrieval”, IEEE Workshop
on Application of Computer Vision, pp. 96-102. 1996.
17. M. Stricker and A. Dimai, “Color indexing with weak spatial constraints”, IS&T/SPIE Conf. on
Storage and Retrieval for Image and Video Databases IV, Vol. 2670, pp.29-40, 1996.
18. P. S. Suhasini, K. R Krishna and I. V. M. Krishna, “CBIR Using Color Histogram Processing”,
Journal of Theoretical and Applied Information Technology, Vol. 6, No.1, pp-116-122, 2009.
19. R. Chakarvarti and X. Meng, “A Study of Color Histogram Based Image Retrieval”, Sixth
International Conference on Information Technology: New Generations, IEEE, 2009.
20. X. Wan and C.C. Kuo, “Color Distrbution Analysis and Quantization for Image Retrieval”, In SPIE
Storage and Retrieval for Image and Video Databases IV, Vol. SPIE 2670, pp. 9–16, 1996.
21. S. Li and M. C. Lee, “Rotation and Scale Invariant Color Image Retrieval Using Fuzzy Clustering”,
Published in Computer Science Journal, Chinese university of Hong Kong, 2004.
22. F. Tang and H. Tae, “Object Tracking with Dynamic Feature Graph”, ICCCN’05. Proceeding of the
14th International Conference on Computer Communications and Networks, 2005.
Signal & Image Processing : An International Journal (SIPIJ) Vol.3, No.1, February 2012 55
23. M. Ioka, “A Method of defining the similarity of images on the basis of color information”, Technical

Report IBM Research, Tokyo Research Laboratory, 1989.
24. H. James. H, S. Harpreet, W. Equits, M. Flickner and W. Niblack, “Efficient Color Histogram
Indexing for Quadratic Form Distance Functions”, IEEE Transactions on Pattern Analysis and Machine
Intelligence, Vol. 17, No. 7, 1995.
25. J.R. Smith and S.F. Chang, “Automated Image Retrieval using Color and Texture”, Technical Report,
Columbia University, 1995.
26. V. V. Kumar, N. G. Rao, A. L. N. Rao and V. V. Krishna, “IHBM: Integrated Histogram Bin
Matching For Similarity Measures of Color Image Retrieval”, International Journal of Signal Processing,
Image Processing and Pattern Recognition Vol. 2, No.3, 2009.
27. M. Swain, D. Ballard, “Color indexing”, International Journal of Computer Vision, 7, pp-11–32,
1991.
28. A. Natsev, R. Rastogi and K. Shim, “WALRUS: A Similarity Retrieval Algorithm for Image
Databases”, In Proceeding. ACM SIGMOD Int. Conf. Management of Data, pp-395–406, 1999.
29. S. Ardizzoni, I. Bartolini, and M. Patella, “Windsurf: Region based Image Retrieval using Wavelets”,
In IWOSS’99, pp. 167–173, 1999.
30. G. V. D. Wouwer, P. Scheunders and D. V. Dyck, “Statistical texture characterization from discrete
wavelet representation”, IEEE Transactions on Image Processing, Vol.8, pp-592–598, 1999.
31. S. Livens, P. Scheunders, G. V. D. Wouwer and D. V. Dyck, “Wavelets for texture analysis, an
overview”, Proceedings of Sixth International Conference on Image Processing and Its Applications,
Vol. 2, pp-581–585, 1997.
32. R. C. Gonzalez and E.W. Richard, Digital Image Processing, Prentice Hall. 2001.
33. N. Jhanwar, S. Chaudhurib, G. Seetharamanc and B. Zavidovique, “Content based image retrieval
using motif co-occurrence matrix”, Image and Vision Computing, Vol.22, pp-1211–1220, 2004.
34. P.W. Huang and S.K. Dai, “Image retrieval by texture similarity”, Pattern Recognition, Vol. 36,
pp665–679, 2003.
35. G. Raghupathi, R.S. Anand, and M.L Dewal, “Color and Texture Features for content Based image
retrieval”, Second International conference on multimedia and content based image retrieval, July-21-
23, 2010.
36. P. S. Hiremath and J. Pujari, “Content Based Image Retrieval based on Color, Texture and Shape
features using Image and its complement”, 15th International Conference on Advance Computing and
Communications. IEEE. 2007.
37. Y. Chen and J. Z. Wang, “A Region-Based Fuzzy Feature Matching Approach to Content Based
Image Retrieval”, IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol. 24, No.9,
pp. 1252-1267, 2002.
38. J. Li, J.Z. Wang and G. Wiederhold, “IRM: Integrated Region Matching for Image Retrieval”, In
Proceeding of the 8th ACM Intermational Conference on Multimedia, pp- 147-156, Oct. 2000.
39. M. Banerjee, M. K. Kundu and P. K. Das, “Image Retrieval with Visually Prominent Features using
Fuzzy set theoretic Evaluation”, ICVGIP, 2004.
40. Y. Rubner, L. J. Guibas and C. Tomasi, “The earth mover’s distance, multidimensional scaling, and
color-based image retrieval”, Proceedings of DARPA Image understanding Workshop. Pp- 661-668,
1997.
41. M. B. Rao, B. P. Rao, and A. Govardhan, “CTDCIRS: Content based Image Retrieval System based
on Dominant Color and Texture Features”, International Journal of Computer Applications, Vol. 18–
No.6, pp-0975-8887, 2011.
42. J. M. Fuertes, M. Lucena, N. P. D. L Blanca and J. C. Martinez, “A Scheme of Color Image Retrieval
from Databases”, Pattern Recognition Vol. 22, No. 3, pp- 323-337, 2001.
43. Y. K. Chan and C. Y. Chen, “Image retrieval system based on color-complexity and color-spatial
features”, The Journal of Systems and Software, Vol. 71, pp-65-70, 2004.
44. T. Gevers, Color in image Database, Intelligent Sensory Information Systems, University of
Amsterdam, the Netherlands. 1998.
45. X. Wan and C. C. Kuo, “Color distribution analysis and quantization for image retrieval”, In SPIE

Storage and Retrieval for Image and Video Databases IV, Vol. SPIE 2670, pp- 9–16. 1996.
46. M. W. Ying and Z. HongJiang, “Benchmarking of image feature for content-based retrieval”, IEEE.
Pp-253-257, 1998.
47. Z. Zhenhua, L. Wenhui and L. Bo, “An Improving Technique of Color Histogram in
Segmentationbased Image Retrieval”, 2009 Fifth International Conference on Information Assurance and
Security,
IEEE, pp-381-384, 2009.
Signal & Image Processing : An International Journal (SIPIJ) Vol.3, No.1, February 2012
56
48. E. Mathias, “Comparing the influence of color spaces and metrics in content-based image retrieval”,
IEEE, pp- 371-378, 1998.
49. S. Manimala and K. Hemachandran, “Performance analysis of Color Spaces in Image Retrieval”,
Assam University Journal of science & Technology, Vol. 7 Number II 94-104, 2011.
50. S. Sural, G. Qian and S. Pramanik, “Segmentation and Histogram Generation using the HSV Color
Space for Image Retrieval”, IEEE- ICIP, 2002.
51. R. C. Gonzalez and R. E. Woods, Digital Image Processing, third ed., Prentice Hall. 2007.
52. W. H. Tsang and P. W. M. Tsang, “Edge gradient method on object color”, IEEE,. TENCON-Digital
Signal Processing Application, pp- 310–349, 1996.
53. X. Wan and C. C. J. Kuo, “A new approach to image retrieval with hierarchical color clustering”,
IEEE transactions on circuits and systems for video technology, Vol. 8, no. 5, 1998.
54. X. Wan and C. C. Kuo, “Image retrieval with multiresolution color space quantization”, In Electron
Imaging and Multimedia System, 1996.
55. J. R. Smith and S. F. Chang, “Tools and techniques for color image retrieval”, in: IST/SPIE-Storage
and Retrieval for Image and Video Databases IV, San Jose, CA, 2670, 426-437, 1996.
56. IEEE. IEEE standard glossary of image processing and pattern recognition terminology. IEEE
Standard. 610.4-1990. 1990.
57. J.R. Smith and S. Chang, “Transform Features for Texture Classification and Discrimination in Large
Image Databases. Proceeding”, IEEE International Conference on Image Processing, Vol. 3, pp-407-
411, 1994.
58. B. Manjunath, P. Wu, S. Newsam and H. Shin, “A texture descriptor for browsing and similarity
retrieval”, Journal of Signal Processing: Image Communication, vol. 16, pp- 33-43, 2000.
59. R. Haralick, “Statistical and structural approaches to texture”, Proceedings of the IEEE, Vol. 67, pp.
786–804, 1979.
60. H. Tamura, S. Mori and T. Yamawaki, “Textural features corresponding to visual perception”, IEEE
Transactions. On Systems, Man and Cybern., Vol. 8, pp- 460-472, 1978.
61. R. C. Gonzalez, R. E. Woods and S. L, Eddins. Digital Image Processing Using MALAB, By Pearson
Education, 2008.
62. A. Haar. Zur Theorier der Orthogonalen Funktionensystem. Math. Annal. Vol. 69, pp-331-371, 1910.
63. C. E. Jacobas, A. Finkelstein and D. H. Salesin, “Fast Multiresolution image querying”, In Proc. Of
SiGGRaPH 95, Annual Conference Series, pp-277-286, 1995.
64. S. Manimala and K. Hemachandran, “Image Retrieval-Based on Color Histogram and performance
Evaluation of similarity Measurement”, Assam University Journal of science & Technology, Vol. 8
Number II 94-104, 2011.
65. H. A. Moghadam, T. Taghizadeh, A.H. Rouhi and M.T. Saadatmand, “Wavelet correlogram: a new
approach for image indexing and retrieval”, J. Elsevier Pattern Recognition, Vol. 38 pp-2006-2518,
2008.
66. http://wang.ist.psu.edu/docs/related/.

AUTHORS
Ms. Manimala Singha received her B.Sc. and M.Sc. degrees in Computer Science
from Assam University, Silchar in 2005 and 2007 respectively. Presently she is
working, for her Ph.D., as a Research Scholar and her area of interest includes
image segmentation, feature extraction, and image searching in large databases
Prof. K. Hemachandran is associated with the Dept. of Computer Science, Assam
University, Silchar, since 1998. He obtained his M.Sc. Degree from Sri Venkateswara
University, Tirupati and M.Tech. and Ph.D. Degrees from Indian School of Mines,
Dhanbad. His areas of research interest are Image Processing, Software Engineering
and Distributed Computing.

ADVANCES IN AUTOMATIC TUBERCULOSIS DETECTION IN CHEST
X-RAY IMAGES
Wai Yan Nyein Naing, Zaw Z. Htike
Department of Mechatronics Engineering Faculty of Engineering, IIUM, Kuala Lumpur, Malaysia
ABSTRACT
Tuberculosis (TB) is very dangerous and rapidly spread disease in the world. In the investigating cases for
suspected tuberculosis (TB), chest radiography is not only the key techniques of diagnosis based on the
medical imaging but also the diagnostic radiology. So, Computer aided diagnosis (CAD) has been
popular and many researchers are interested in this research areas and different approaches have been
proposed for the TB detection and lung decease classification. In this paper, the medical background
history of TB decease in chest X-rays and a survey of the various approaches in TB detection and
classification are presented. The literature in the related methods is surveyed papers in this research area
until now 2014.
KEYWORDS
CAD, Tuberculosis, Image processing, Radiographs

REFERENCES
[1] Stefan Jaeger et. al. “Automatic Tuberculosis Screening Using Chest Radiographs”, 2013 IEEE.
[2] KIM LE,“Automated Detection of Early Lung Cancer and Tuberculosis Based on X-Ray Image
Analysis”, Proceedings of the 6th WSEAS International Conference on Signal, Speech and Image
Processing, Lisbon, Portugal, September 22-24, 2006.
[3] Masayu Norman et. al, “Statistical Approach in Determination of Tuberculosis Spatial Pattern”,
Proceeding of the 2011 IEEE International Conference on Space Science and Communication
(IconSpace) 12-13 July 2011, Penang, Malaysia
[4] NorlizaMohd. Noor et. al. “A Statistical Interpretation of the Chest Radiograph for the Detection of
Pulmonary Tuberculosis”, 2010 IEEE EMBS Conference on Biomedical Engineering & Sciences
(IECBES 2010), Kuala Lumpur, Malaysia, 30th November.
[5] ShafeenaBasheeret. al, “Computer Assisted X-Ray Analysis System for De-tection of Onset of
Tuberculosis”, International Journal of Scientific & Engineering Research, Volume 4, Issue 9, September-
2013
[6] Stefan Jaeger et. al. “Detecting Tuberculosis in Radiographs Using Combined Lung Masks”, 34th
Annual International Conference of the IEEE EMBS San Diego, California USA, 28 August – 1
September, 2012.
[7] A. D. Orjuela-Cañón, Fuzzy-ART Neural Networks for Triage in Pleural Tuberculosis, 2013 PAN
AMERICAN HEALTH CARE EXCHANGES (PAHCE). CONFERENCE.
[8] RuiShenet. al. “A Hybrid Knowledge-Guided Detection Technique for Screening of Infectious
Pulmonary Tuberculosis From Chest Radiographs”, IEEE TRANSACTIONS ON BIOMEDICAL
ENGINEERING, VOL. 57, NO. 11, NOVEMBER 2010.
[9] RatnasariNurRohmahLung, “Tuberculosis Identification Based on Statistical Feature of Thoracic
Xray”, IEEE, 2013.
[10] PATIL S.A. “Texture Analysis of TB X-ray Images Using Image Processing Techniques”, Journal of
Biomedical and Bioengineering, Volume 3, Issue 1, 2012,
[11] Narayan PENDSE, MD , “Chest Radiography in the field”.
[12] KusworoAdi ET. AL “TUBERCULOSIS (TB) IDENTIFICATION IN THE ZIEHL-NEELSEN
SPUTUM SAMPLE IN NTSC CHANNEL AND SUPPORT VECTOR MACHINE (SVM)
CLASSIFICATION, International Journal of Innovative Research in Science, Engineering and
Technology, 2013.
[13] Mrs. J. ShyamalaDev ,”A Study on Improving the Conspicuity of Lung Nodules by use of Virtual
Dual-Energy" Radiography,” 4th National Conference on Advanced Computing, Applications &
Technologies, May 2014
[14] WHO (2013)Global Tuberculosis Report 2013, World Health Organization
[15] Smear-Negative Multidrug-Resustant Tuberculosis a Significance Hidden Problem for MDR-TB
Control: An Analysis of Real World Data”,Journal of Tuberculosis Research, 2014.
[16] Md. Rafiqul Islam et. al. “Medical Image Classification Using an Efficient Data Mining Technique”
[17] ALI EL-SOLH et. al, “Validity of a Decision Tree for Predicting Active Pulmonary Tuberculosis”,
American Journal of respiratory and Critical Care Medicine, 1997.
[18] Tan JH, Acharya UR, Tan C, et al. “Computer-assisted diagnosis of tuberculosis: a first order
statistical approach to chest radiograph.” J Med Syst 2012;36:2751-9.
[19] Tao Xu, Irene Cheng, Richard Long and MrinalMandal, Novel coarse-to-fine dual scale technique
for tuberculosis cavity detection in chest radiographs, Xu et al. EURASIP Journal on Image and Video
Processing 2013, 2013:3
[20] V.Sampath Kumar et. al,Lung Nodules Detection by Computer Aided Diagnosis (CAD) Using
Image Processing, International Journal of Advance Research in Computer Science and Management
Studies, 2014.
[21] Fan Zhang et. al, “Lung Nodule Classification with Multi-Level Patch-based Context Analysis, IEEE
TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2013.

[22] Bram van Ginneken, “Segmentation of anatomical structures in chest radiographs using supervised
methods: a comparative study on a public database”, Elsevier, 2005.
[23] Cootes, T.F., Taylor, C.J., Cooper, D., Graham, J., 1995, “Active shape models- their training and
application”, Computer Vision and Image Understanding 61 (1), 38-59.
[24] B. Van Ginneken, B. terHaarRomeny, and M. Viergever, “Computer aided diagnosis in chest
radiography: a survey,” Medical Imaging, IEEE Transactions on, vol. 20, no. 12, pp. 1228–1241,
2001.
[25] D. Field et al., “Relations between the statistics of natural images and the response properties of
cortical cells,” J. Opt. Soc. Am. A, vol. 4, no. 12, pp. 2379–2394, 1987.
[26] D. Comaniciu and P. Meer, “Mean shift: A robust approach toward feature space analysis,” IEEE
Trans. Pattern Anal. Mach. Intell., vol. 24, no. 5, pp. 603–619, May 2002.
[27] G. Dong, N. Ray, and S. Acton, “Intravital leukocyte detection using the gradient inverse coefficient
of variation,” IEEE Trans.Med. Imag., vol. 24, no. 7, pp. 910–924, Jul. 2005.
[28] C. Di Ruberto and A. Dempster, “Circularity measures based on mathematical morphology,”
Electron. Lett., vol. 36, no. 20, pp. 1691–1693, Sep. 2000.
[29] S. Candemir, S. Jaeger, K. Palaniappan, S. Antani, and G. Thoma, “Graph-cut based automatic lung
boundary detection in chest radiographs,” in IEEE Healthcare Technology Conference: Translational
Engineering in Health & Medicine, 2012, pp. 31–34.
[30] J. Shiraishi, S. Katsuragawa, J. Ikezoe, T. Matsumoto, T. Kobayashi, K. Komatsu, M. Matsui, H.
Fujita, Y. Kodera, and K. Doi, “Development of a digital image database for chest radiographs with
and without a lung nodule,” American Journal of Roentgenology, vol. 174, no. 1, pp. 71–74, 2000.
[31] B. Van Ginneken, M. Stegmann, and M. Loog, “Segmentation of anatomical structures in chest
radiographs using supervised methods: a comparative study on a public database,” Medical Image
Analysis, vol. 10, no. 1, pp. 19–40, 2006.
[32] Y. Boykov and G. Funka-Lea, “Graph cuts and efficient n-d image segmentation,” Int. J. Computer
Vision, vol. 70, pp. 109–131, 2006.
[33] Daniela Stan Raicu, Image Feature Extraction.
[34] Watman, C. and Le, K., “Gravity Segmentation of Human Lungs from X-ray Images for Sickness
Classification”, Fourth International Conference on Intelligent Technologies (InTech'03), 2003, pp. 428-
434.
[35] Noor NM, Rijal OM, Fah CY. Wavelet a as features for tuberculosis (MTB) using standard x-ray
film images.
[36] SUSANTA KUMAR SAHU, Image Segmentation for Text Extraction .
[37] http://en.wikipedia.org/wiki.
[38] Shen R, Cheng I, Basu A. A Hybrid Knowledge Guided Detection Technique for Screening of
Infectious Pulmonary Tuberculosis from Chest Radiographs.
[39] Carpenter G. A., Grossberg S., Rosen D., “A neural network realization of Fuzzy-ART”, Technical
report CAS/CNS-91-021, August 1991.
[40] Faussete L., Fundamentals of Neural networks: architectures, algorithms, and applications.Third
Edition, Pearsons Education Publishers.
[41] Baptista de Oiveira e Souza Filho J., Silva Antunes P. H., Seixas J., Maidantchik C.,
“RedesNeuraisAplicadasaoDiangnóstico da TuberculosePulmonarPaucibacilar” In Portuguese,
AutomaticBrazilianCongress, 2010.
[42] Tony Lindeberg, KTH Royal Institute of Technology, Stockholm, Sweden ,”Scale Invariant Feature
Transform”
[43] Feature Based Image Classification by using Principal Component Analysismore by Imran
SarwarBajwa.
[44] Imran S. Bajwa1, M. ShahidNaweed, M. Nadim Asif, S. IrfanHy-der,Feature Based Image
Classification by using Principal Component Analysis.
[45] V. Vapnik, The nature of statistical learning theory. Springer Verlag, 2000.
[46] B. Sch¨olkopf, C. Burges, and A. Smola, Advances in kernel methods: support vector learning. The

MIT press, 1999.
[47] K. Suzuki, J. Shiraishi ,H. Abe, H. Mac Mahon, and K. Doi, “False-positive reduction in
computeraided diagnostic scheme for detecting nodules in chest radiographs by means of
[48] Noor N M, Rijal OM, Yunus A, et. al A statistical Interpretation of the Chest radiograph for the
detection of pulmonary tuberculosis. In Biomedical Engineering and Sciences (IECBES), 2010 IEEE
EM BS Conference.
[49] Xu T, Cheng I, Long R, et. al, “Novel Coarse-to-fine dual scale technique for tuberculosis cavity
detection in chest radiographs”, EURASIP Journal on Image and Video Processing, 2013.
[50] Song YL. “Localization algorithm and implementation for focal of pulmonary tuberculosis chest
image”. In: Machine Vision and Human-Machine Interface (MVHI), 2010 International Conference
on, IEEE 2010:361-4.
[51] Maduskar P, Hogeweg L, Philipsen R, et al. Improved texture analysis for automatic detection of
tuberculosis (TB) on chest radiographs with bone suppression images. In: SPIE Medical Imaging, pages:
86700H-86700H. International Society for Optics and Photonics, 2013.
[52] Rijal M, Ebrahimian H, Noor NM. Determining features for discriminating PTB and normal lungs
using phase congruency model. In: Biomedical and Health Informatics (BHI), 2012 IEEE-EMBS
International Conference on, IEEE 2012:341-4.
[53] Leibstein JM, Nel AL. Detecting tuberculosis in chest radiographs using image processing
techniques. University of Johannesburg, 2006.
[54] Koeslag A, de Jager G. Computer aided diagnosis of miliary tuberculosis. Proceedings of the Pattern
Recognition Association of South Africa, 2001.
[55] Sarkar S, Chaudhuri S. Automated detection of infiltration and cavitation in digital chest radiographs
of chronic pulmonary tuberculosis. In: Engineering in Medicine and Biology Society, 1996. Bridging
Disciplines for Biomedicine. Proceedings of the 18th Annual International Conference of the IEEE,
IEEE 1997;3:1185-6.
[56] “Implementing the WHO Stop TB Strategy”, A Handbook for National TB Control Programmes,
2008.
[57] “WHO Policy on Collaborative TB/HIV activities”, Guidelines for national programmes and other
stakeholders, Update Version of 2004.
[58] http://en.wikipedia.org/wiki/Mycobacterium_tuberculosis
[59] http://en.wikipedia.org/wiki/Tuberculosis

COMPARATIVE ANALYSIS OF VOWELS,DIPHTHONGS AND GLIDES OF SINDHI
Ayaz Keerio1
, Lachhman Das Dhomeja2
, Asad Ali Shaikh2
, Yasir Arfat Malkani1
1
Institue of Mathematics and Computer Science, University of Sindh, Jamshoro, Pakistan
2
Institute of Information and Communication Technology, University of Sindh, Jamshoro, Pakistan
ABSTRACT
Sindhi language is primarily spoken in the Sindh province of Pakistan, and in some parts of India.
Languages phonemic inventory include vowels, consonants and diphthongs. This paper presents acoustic
analysis and properties of the glide consonants of Sindhi. Glides are considered having stable and
predictable formant structure and associated acoustic properties like vowels and diphthongs.
Understanding the corresponding acoustic similarities, differences and relationship between three types of
these sounds is the subject of discussion of this paper.
KEYWORDS
Consonants, Formant frequencies, Glides, Phonemic inventory, Sindhi

REFERENCES
[1] Olive, J. P., Alice, G., & John. C. (1993). Acoustics of American English Speech: a dynamic
approach. New York: Springer-Verlag.
[2] Jennifer, S. C. (2006). The Sindhi language. In In K. Brown (ed.) Encyclopedia of Language and.
Linguistics (2 ed., Vol. 11, pp. 384-386). Oxford: Elsevier.
[3] Ladefoged, P. (1993). A course in phonetics (3 ed.). Harcourt College Publishers, New York.
[4] MAXWELL, O. and FLETCHER, J. (2010), The acoustic characteristics of diphthongs in Indian
English. World Englishes, 29: 27–44.
[5] Kent, R. D., & Charles, R. (2002). The acoustic analysis of speech (2 ed.). Singular Publishing Group.
[6] Raphael, L. J., Gloria, J. B., & Katherine, S. H. (2006). Speech science primer: physiology, acoustics,
and perception of speech (5 ed.). Lippincott Williams & Wilkins.
[7] Kehoe, M., G. Hilaire-Debove, K. Demuth & C. Lleó (2008) The structure of branching onsets and
rising diphthongs: Evidence from the acquisition of French and Spanish. Language Acquisition 15: 5-
57
[8] Jones, D. (1969). An outline of English phonetics (9 ed.). England: W. Heffer & Sons Ltd.,
Cambridge.
[9] Ioana, C. (2002). A perception-production study of Romanian diphthongs and glide-vowel sequences.
Journal of the International Phonetic Association , Vol. 32, pp. 203-222.
[10] Martínez Celdrán, E. (2004). Problems in the classification of approximants. Journal of the
International Phonetic Association, Vol. 34, pp. 201-210.
[11] Padgett, J. (2008). Glides, vowels, and features. Lingua , Vol. 118 (12), pp. 1937-1955.
[12] Aguilar, L. (1999). Hiatus and diphthong: Acoustic cues and speech situation differences. Speech
Communication , Vol. 28 (1), pp. 57-74.
[13] Gay, T. (1968). Effects of speaking rate on diphthong formant movements. Journal of the Acoustics
Society of America ,Vol. 44, pp. 1570-1573.
[14] Aiza, S., Sana, A., & Aymen, A. (2004). Diphthongs in Urdu Language and Analysis of their
Acoustic Properties. Center for Research in Urdu Language Processing (CRULP), pp. 9-15.
[15] Borzone de Manrique, A.M (1979). Acoustic analysis of the Spanish diphthongs. Phonetica, Vol. 36,
pp. 194–206.
[16] Jha, S. K. (1985). Acoustic analysis of the Maithili diphthongs. Journal of Phonetics, Vol. 13, pp.
107-115.
[17] Keerio, A., Patoli, M. Z., Mitra, B. K., Chatwin, C., Young, R., & Birch, P. (2010). Acoustic
Analysis of Diphthongs in Sindhi. GRASSROOTS , 41 (1), pp. 1-18.
[18] Hongyan, W. (2007). Mutual intelligibility of Chinese, Dutch and American speakers of English.
Ph.D dissertation, Graduate School of Linguistics: Netherlands.
[19] Lobanov, B. M. (1971). Classification of Russian vowels spoken by different speakers. Journal of the
Acoustical Society of America , Vol. 49 (2B), pp. 606-608.
AUTHORS
Dr. Ayaz Keerio is an assistant Professor at the Institute of Mathematics and
Computer Science (IMCS), University of Sindh, Jamshoro, Pakistan. He got his
Master’s degree in Computer Science from University of Sindh, Jamshoro (Pakistan)
and PhD from University of Sussex, UK in 2011. His main area of research is Speech
Recognition and Synthesis systems. He is also interested in digital signal processing,
Data communication & networks and mobile & distributed computing systems.

Dr. Lachhman Das Dhomeja is an Assistant Professor at the Institute of Information
& Communication Technology (IICT), University of Sindh, Jamshoro, Pakistan. He
got his Master’s degree in Computer Technology from University of Sindh, Jamshoro
(Pakistan) in 1991 and PhD from University of Sussex, UK in 2011. His main
research area is Pervasive Computing in general and policy-based context-awareness
in particular. His other research interests include secure device pairing in ubiquitous
environments, Data communication & networks, software architectures and
Distributed Computing.
Dr. Asad Ali Shaikh is an Associate Professor and director of the Institute of
Information and Communication Technology (IICT), University of Sindh, Jamshoro,
Pakistan. He did his Masters degree in Computers Engineering from Clarkson
University, USA in 1991 and PhD degree in Information Technology from University
of Sindh, Pakistan in 2006. His current research focus is on the protocol design,
security issues in computer networks and software development. He is also interested
in digital signal processing and Data communication & networks.
Dr. Yasir Arfat Malkani is a Lecturer at the Institute of Mathematics and
Computer Science (IMCS), University of Sindh, Jamshoro, Pakistan. He got his
Master’s degree in Computer Science from University of Sindh, Jamshoro
(Pakistan) in 2003 and PhD from University of Sussex, Brighton, UK in 2011. His
main area of research is Pervasive Computing. His research is focused on secure
device/service discovery and access control mechanisms using policies and
location/proximity data/information. He is also interested in sensor networks,
wireless networks (including WiFi, Bluetooth, WiMAX, etc), and solutions to
various issues in distributed and pervasive computing systems through the integration of tools and
techniques from distinct disciplines/areas. He is also interested in the design and/or development of
various tools and techniques that might be useful in giving world-wide recognition to various national
languages, such as SINDH and URDU.

APAPER ON AUTOMATIC FABRICS FAULT PROCESSING USING IMAGE
PROCESSING TECHNIQUE IN MATLAB
R.Thilepa
Department of EEE Adhiyamaan Educational & Research Institute, Hosur-Tamil nadu 635 109
M.Thanikachalam
Department of Civil Engineering, Velammal Engineering College, Chennai-600 066
ABSTRACT
The main objective of this paper is to elaborate how defective fabric parts can be processed using Matlab
with image processing techniques. In developing countries like India especially in Tamilnadu, Tirupur the
Knitwear capital of the country in three decades yields a major income for the country. The city also
employs either directly or indirectly more than 3 lakhs of people and earns almost an income of 12, 000
crores per annum for the country in past three decades [2]. To upgrade this process the fabrics when
processed in textiles the fault present on the fabrics can be identified using Matlab with Image processing
techniques. This image processing technique is done using Matlab 7.3 and for the taken image, Noise
Filtering, Histogram and Thresholding techniques are applied for the image and the output is obtained in
this paper. This research thus implements a textile defect detector with system vision methodology in
image processing.
Keywords:
Image processing, Matlab 7.3, Gray image, Histogram, Thresholding.

REFERENCES:
1. R. C. Gonzalez, R. E. Woods, S. L. Eddins, “Digital Image Processing using MATLAB”, ISBN 81-
297-0515-X, 2005, pp. 76-104,142-166
2. http:// en.wikipedia.org/wiki/Tirupur
3. Kenneth R. Castelman, Digital image processing, Tsinghua Univ Press, 2003.
4. I.Pitas, Digital Image Processing Algorithm and Applications. John Wiley &Sons, Inc.2002.
5. ENGN 4528 Computer Vision, Semester 1, 2003 Lab 1: Introduction to Image Processing in Matlab
&Binary Image Analysis
6. newsgroups.derkeiler.com > Archive > Comp > comp.soft-sys.matlab > 2007-09.
7. Thresholding (image processing) - Wikipedia, the free encyclopedia.mht
8. Thresholding A Pixel-Level Image Processing Methodology Preprocessing Technique for an OCR
System for the Brahmi Script Devi Ancient Asia.mht
9. Histogram plot - MATLAB.mht
10. Color histogram - Wikipedia, the free encyclopedia.mht
11. Textile Views - Textile news, Apparel news, fabric, yarns,Tirupur exporters , Tirupur Ready made
garments , apparel news, Tirupur yarn market , CMT cost.mht
12. http://www.scribd.com/doc/7015798/Tirupur-case-study
13. Ahmed Ridwanul Islam, Farjana Zebin Eishita,Jesmine Ara Bubly, “Implementation of a RealTime
Automated Fabric Defect DetectionSystem” 2007.
14. B. G. Batchelor and P. F. Whelan, “Selected Papers on Industrial Machine Vision . Systems,”SPIE
Milestone Series, 1994.
15. T. S. Newman and A. K. Jain, “A Survey of Automated Visual Inspection,” Computer Vision and
Image Understanding, vol. 61, 1995, pp. 231–262.
16. Kang T.J. et al. “Automatic Recognition of Fabric Weave Patterns by Digital Image Analysis”,
Textile Res. J. 69(2), 77-83 (1999
17. Kang T.J. et al. “Automatic Structure Analysis and Objective Evaluation of Woven Fabric Using
Analysis”, Textile Res. J. 69(2), 77-83 (1999)

AN ACTIVE CONTOUR FOR RANGE IMAGE SEGMENTATION
Khaldi Amine1
and Merouani Hayet Farida2
1
Department of computer sciences, Badji Mokhtar University, Laboratory of LRI BP12.Sidi Amar, 23000
Annaba, Algeria
2
Department of computer sciences, Badji Mokhtar University, Laboratory of LRI BP12.Sidi Amar, 23000
Annaba, Algeria
ABSTRACT
In this paper a new classification of range image segmentation method is proposed according to the
criterion of homogeneity which obeys the segmentation, then, a deformable model-type active contour
“Snake” is applied to segment range images.
KEYWORDS
Image segmentation, Active contour, Snake, Range image, Classification, Criterion of homogeneity.

REFERENCES
[1] Emerico Natonek, Fast Range Image Segmentation for Servicing Robots, International Conference on
Robotics and Automation - ICRA , vol. 1, pp. 406-411, 1998
[2] Thoma Chaperon, segmentation of point cloud 3d modeling for automatic industrial environments
digitized, PhD thesis, école des mines de Paris, 2002
[3] Jean-Philippe Tarel, Recalage géométrique avec plusieurs prototypes, institut national de recherche en
informatique et en automatique des Yvelines, 1996
[4] Laurent Chevalier, Fabrice Jaillet, AtillaBaskurt, Segmentation and Superquadrics Modeling of 3D
Objects, The 11-th International Conference in Central Europe on Computer Graphics, Visualization
and Computer Vision, Plzen-Bory, Czech Republic, February 2003
[5] Jorge Hernández, Beatriz Marcotegui. Point Cloud Segmentation towards Urban Ground Modeling,
5th GRSS/ISPRS Joint workshop on remote sensing and data fusion over urban areas, Shangai, China.
May 2009.
[6] Yonghuai Liu, Replicator Dynamics in the Iterative Process for Accurate Range Image Matching,
International Journal of Computer Vision, Volume 83, Number 1, Pages 30-56, 2009
[7] Christophe Simon, Frédérique Bicking, Thierry Simon , Influence of mathematic models used on the
quality of estimation of the depth in images, Proceedings of 20th IEEE Instrumentation and Measurement
Technology conference, IEEE/IMTC2003, Vail, Colorado, USA, 2003
[8] Edouard Thomas, Frederic Nicolier, and Gilles Millon, Low-cost system for ancient stamps range
image acquisition, Proceedings of SPIE 5679, pp 288, Machine Vision Applications in Industrial
Inspection XIII, San Jose, CA, USA, 17 January 2005
[9] Ahmed Kirmani, Andrea Colaço, Franco N. C. Wong, and Vivek K. Goyal, Exploiting sparsity in
time-of-flight range acquisition using a single time-resolved sensor, Optics Express, Vol. 19, Issue 22,
pp. 21485-21507, 2011
[10] P. J. Besl, R. C. Jain, Segmentation through variable-order surface fitting,IEEE Trans. Pattern Anal.
Machine Intell vol. PAMI-10, no. 2, pp. 167-192, March 1988.
[11] N. Yokoya, M. D. Levine, Range image segmentation based on differential geometry: a hybrid
approach, IEEE Trans. Patt. Anal. Mach. Intell, vol. PAMI-11, no.6, pp.643-649, June 1989.
[12] T. Kasvand, The k1k2 space in range image analysis, Proc.9th Int. Conference on Pattern
Recognition pp.923-926, Italy, 1988.
[13] G. Maître, H. Hügli, F. Tièche & J.P. Amann, Range image segmentation based on function
approximation, Close-Range Photogrammetry Meets Machine Vision, SPIE Vol 1395, pp. 275-282,
1990
[14] B. Parvin, G. Medioni, Segmentation of range images into planar surfaces by split and merge,
Computer Vision Pattern Recognition pp. 415-417, 1986.
[15] R. W. Taylor, M. Savini, A. P. Reeves, Fast segmentation of range imagery into planar regions,
Computer Vision Graphics and Image Processing vol. 45, pp. 42-60, 1989.
[16] M. Dalai and R. Leonardi. Segmentation based image coding with l-infinity norm error control.
Proceedings of the Picture Coding Symposium PCS'04, USA, 2004
[17] A. Gupta, R. R. Bajcsy, Integrated approach for surface and volumetric segmentation of range
images using biquadrics and superquadrics, Applications of Artificial Intelligence X: Machine Vision and
Robotics K. W. Bowyer, Editor Proc.SPIE 1708, pp.210-227, 1992.
[18] X. Y. Jiang, H. Bunke, Fast segmentation of range images into planar regions by scan line grouping,
1994.
[19] A. Davignon, Contribution of edges and regions to range image segmentation, Applications of
Artificial Intelligence X: Machine Vision and Robotics K. W. Bowyer, Editor Proc.SPIE 1708,
pp.228-239, 1992.
[20] Paul Besl, ActiveOptical Range Imaging Sensors, General Motors Research Laboratories, Michigan
USA, 1988
[21] F. Ade, A. Ylä-Jääski, Segmentation and symbolic description of range images, German association

for pattern recognition Symposium, Vol. 254Springer (1990), p. 292-298.
[22] HANZI WANG, DAVID SUTER, MDPE: A Very Robust Estimator for Model Fitting and Range
Image Segmentation, Department of Electrical and Computer Systems Engineering, Monash University,
Australie, 2004
[23] Paulo Fabiano Urnau Gotardo, Olga Regina Pereira Bellon, Kim Boyer, Luciano Silva, Range Image
Segmentation Into Planar and Quadric Surfaces Using an improved Robust estimator and genetic
algorithm, ieee transactions on systems, man, and cybernetics, vol. 34, no. 6, december 2004

FUSION OF FINGERPRINT AND AGE BIOMETRIC FOR GENDER CLASSIFICATION
USING FREQUENCY AND TEXTURE ANALYSIS
S. S. Gornale* and Kruthi R#
*School of Mathematics and Computer Science, Department of Computer Science, Rani Channamma
University, Belagavi-Karnataka-INDIA.
#
Research Student, Department of Computer Science and Engineering, Jain University, Bangalore-
Karnataka-INDIA
ABSTRACT
Classification of gender from fingerprints is one of the important steps in forensic anthropology. This
forensic anthropology is used to identify the gender of a criminal in order to minimize the suspects list of
search. A very few researcher have worked on gender classification using fingerprints and have gain the
competitive results. In this work we are trying to fuse the fingerprint and age biometrics for gender
classification. The real fingerprints were collected from different age groups such as 15-20 years and 20-
60 years of the rural and urban people. According to this experimental observation soft biometric
information can be used significantly to improve the recognition performance of biometric system. The
overall performance of the proposed method is found to be satisfactory and more competitive.
KEYWORDS
Gender classification, frequency domain, texture analysis, soft biometrics and hard biometrics traits.

REFERENCES:
[1] Ujwala “A Survey on Soft biometrics” International Journal of Innovative and Applied Research
(IJIAR) , Sept, 2013,Vol.2. Issue 8: ISSN 2278-7844, PP: 460-465,2013.
[2] Anil k Jain et.al, “Biometrics of Next Generation: an overview” Springer, 2010
(http://biometrics.cse.msu.edu/Publications/GeneralBiometrics/JainKumarNextGenBiometrics_Book
Chap10.pdf)
[3] Gnanasivam .P, and Dr. Muttan S, “Fingerprint Gender Classification Using Wavelet Transform and
Singular Value Decomposition”. International Journal of Computer Science Issues, Vol. 9, Issue 2, No 3,
March 2012
[4] Gnanasivam .P, and Dr. Muttan S, “Gender Identification Using Fingerprint through Frequency
Domain analysis”. European Journal of Scientific Research ISSN 1450-216X Vol.59 No.2 (2011).
[5] Bai-Ling Zhang, Haiphong Zhang, and Shuzhi Sam Ge, “Face Recognition by Applying Wavelet Sub
band Representation and Kernel Associative Memory”, IEEE Transactions on Neural Networks, vol.
15, no. 1, 2004, pp.166-177.
[6] Ahmed Badawi, Mohamed Mahfouz, Rimon Tadross, Richard Jantz, “Fingerprint-Based Gender
Classification.” Proceedings of the International conference on Image Processing Computer Vision and
Pattern Recognition (IPCV’06), June-2006, PP:41-46.
[7] Acree.M, “Is there a gender difference in fingerprint ridge density?” Forensic Science International,
vol. 102, no.1, 1999, pp.35-44.
[8] Anil K. Jain, Karthik Nandakumar, Xiaoguang Lu,and Unsang park.”Integrating Faces, Fingerprints,
and Soft Biometric Traits for user Recognition.” Proceedings of Biometric Authentation Workshop
,LNCS 3087, PP.259-269, PRAGUE,(MAY 2004).
[9] Shimon K Modi ,Prof. Stephen J, Elliott ,Jeff .”Impact of Age Groups on Finger printing Recognition
Performance”.1-4244 -1300-2/o7/ 2007 IEEE.
[10] Manish Verma and Suneeta Agarwal, "Fingerprint Based Male-Female Classification.’’ In
Proceedings of the international workshop on computational intelligence in security for information
systems ,Genoa, Italy, 2008, pp.251-257
[11] Gholamerza Amayel, George Babis,Mircea Nicolescu. “Gender Classification from Hand
shape”.978-1-4244-2340-8/08/$25.00 2008 IEEE.
[12] Jen feng wang, et al, “Gender Determination using Fingertip Features”. Internet Journal of Medical
Update 2008 Jul-Dec;3(2):22-8.
[13] Angela Bell, “Loop ridge count differences between genders”. Nebraska Wesleyan University.(
http://www.neiai.org/)
[14] Dr. Prateek Rastogi, Ms. Keerthi R Pillai “A study of fingerprints in relation to gender and blood
group” Journal Indian Academy Forensic Medicine, 32(1), pp-11-14 ISSN 0971-0973.
[15] Shrikant Tiwari, Aruni Singh, Sanjay Kumar Singh. “Fusion of Ear and Soft-Biometrics for
Recognition fo Newborn”. Signal & image processing: an international Journal (SIPIJ) vol.3 No.3, June
2012.
[16] Ritu Kaur and Susmita Ghosh Mazumdar, “Fingerprint Based Gender Identification using Frequency
Domain Analysis”. International Journal of Advances in Engineering & Technology, March
2012.©IJAET ISSN: 2231-1963.
[17] T. Arulkumaran, Dr.P.E.Sankaranarayanan, Dr.G.Sundari.”Fingerprint Based Age Estimation Using
2D Discrete Wavelet Transforms and Principal Component Analysies”. International Journal of advanced
research in Electrical and Instrumentation Engineering vol.2 issue 3, March 2013.
[18] Rijo Jackson Tom, T. Arulkumaran , “Fingerprint Based Gender Classification Using 2D Discrete
Wavelet Transforms and Principal Component Analysis”. International Journal of Engineering Trends
and Technology, Volume 4 Issue 2,2013
[19] S.S.Gornale ,Geetha D, Kruthi R “Analysis of fingerprint image for gender classification using
spatial and frequency domain analysis”, American International Journal of Research in Science,

Technology, Engineering and Mathematics”, ISSN (Print): 2328-3491, ISSN (Online): 2328-3580, ISSN
(CDROM): 2328-3629, PP: 46-50, 2013
[20] Ritu Kaur and Susmita Ghosh Mazumdar, Mr. Devanand Bhonsle, “A Study On Various Methods of
Gender Identification Based on Fingerprints”. International Journal of Emerging Technology and
Advanced Engineering, ISSN 2250-2459, Volume 2,Issue 4, April 2012
[21] Sajid Alikhan, Maqsood Ahmad, Muhamamud Nazir and Naveed Riaz.”A comparative Analysis of
Gender classification Techniques”. International Journal of Bio-science and Biotechnology,Vol.5No.4,
August, 2013.
[22] Anil K. Jain, Karthik Nandakumar, Xiaoguang Lu,and Unsang park. “Integrating Faces, Fingerprints,
and Soft Biometric Traits for user Recognition.” Proceedings of Biometric Authentication Workshop,
LNCS 3087, PP.259-269, PRAGUE, 2004.
[23] A. Ross, A. Jain, “Information fusion in biometrics”, Pattern Recognition Letters 24 (2003) 2115–
21252003 Elsevier Science B.V. All rights reserved. doi:10.1016/S0167-8655(03)00079-5, Pattern
Recognition Letters 24 (2003) 2115–2125 www.elsevier.com/locate/pattern recognition, vol. 24, no.
13, pp. 2115-2125, 2003.
[24] Min-Gu Kim, Hae-MinMoon, Yongwha Chung, and Sung BumPan, “A Survey and Proposed
Framework on the Soft Biometrics Technique for Human Identification in Intelligent Video Surveillance
System”, Journal of Biomedicine and Biotechnology, Volume 2012, Article ID 614146, 7 pages,
doi:10.1155/2012/614146.
[25] Seema Verma, Sonu Agrawal, “A Study on “A Soft Biometric Approach: Face
Recognition””International Journal of Advanced Research in Computer Science and Software
Engineering, Volume 3, Issue 3, March 2013 ISSN: 2277 128X.
[26] Vikas Humbe, S S Gornale , K V Kale, R R Manza’, “Mathematical Morphology Approach for
Genuine Fingerprint Feature Extraction”, International Journal of Computer Science and Security, ISSN:
1985-1533 Volume No. 1 issue 2 PP: 53-59-2007.

TEST DATA COMPRESSION BASED ON GOLOMB CODING AND TWO-VALUE
GOLOMB CODING
Priyanka Kalode1
and Mrs. Richa Khandelwal2
1
Department of Electronics Engineering, Ramdeobaba college of Engg and Mgt, Nagpur
2
Department of Electronics Engineering, Ramdeobaba college of Engg and Mgt, Nagpur
ABSTRACT:
In this paper, we discuss test data compression and decompression method based on variable length
Golomb codes and 2-V Golomb Codes for test data. The method is targeted to minimize the amount of
test data, which reduces the size of memory required in ATE for test data and also time required to
transfer test data to specific device on SOC. We completed MATLAB coding for both methods and
applied test vectors of some standard ISCAS benchmark circuits and compared results for same.
Experimental results on ISCAS benchmark circuits show that the compressed data produced by 2-V
Golomb coding is better than Golomb Coding method.
KEYWORDS:
Automatic test equipment (ATE), precomputed test sets, variable-to-variable-length codes, Golomb
coding, RLE, SOC, Golomb Coding, 2-V Golomb Coding.

REFERENCES
[1] Y. Zorian, E. J. Marinissen, and S. Dey, “Testing embedded-core based system chips,” in Proc. Int.
Test Conf., 1998, pp. 130–143.
[2] V. Iyengar, K. Chakrabarty, and B. T. Murray, “Deterministic built-in pattern generation for
sequential circuits,” J. Electron. Testing: Theory and Applications (JETTA), vol. 15, pp. 97–115,
Aug./Oct. 1999.
[3] A. Jas, J. Ghosh-Dastidar, and N. A. Touba, “Scan vector compression/decompression using statistical
coding,” In Proc. IEEE VLSI Test Symp., 1999, pp. 114–120.
[4] Sybille Hellebrand, Armin Würtenberger, “Alternating Run-Length Coding -A Technique for
Improved Test“,Handouts 3rd IEEE International Workshop on Test Resource Partitioning, Baltimore,
MD, USA, October 10 –11,2002Data Compression
[5] I. Hamzaoglu and J. H. Patel, “Test set compaction algorithms for combinational circuits,” in Proc.
Int. est Conf., 1998, pp. 283–289.
[6] S. Kajihara, I. Pomeranz, K. Kinoshita, and S. M. Reddy, “On compacting test sets by addition and
removal of vectors,” in Proc. VLSI Test Symp., 1994, pp. 202–207.
[7] I. Hamzaoglu and J. H. Patel, “Reducing test application time for full scan embedded cores,” in Proc.
Int. Symp. Fault-Tolerant Computing, 1999, pp. 260–267.
[8] H. Kobayashi and L. R. Bahl, “Image data compression by predictive coding, Part I: Prediction
algorithm,” IBM J. Res. Devel., vol. 18, p. 164, 1974.
[9] Anshuman Chandra and Krishnendu Chakrabarty, “Test Data Compression for System-on-a-Chip
Using Golomb Codes1”, IEEE Trans. Computer-Aided Design, 2000.
[10] Y. Zorian, S. Dey, and M. Rodgers, “Test of future system-on-chips,” in Proceedings of International
Conference Computer-Aided Design, 2000, pp. 392-398.
[11] PO-CHANG TSAI, SYING-JYAN WANG, CHING-HUNG LIN AND TUNG-HUA YEH, “Test
Data Compression for Minimum Test Application Time,” JOURNAL OF INFORMATION SCIENCE
AND ENGINEERING 23, 1901-1909 (2007)
[12] A. Chandra and K. Chakrabarty, “A unified approach to reduce SoC test data vol ume, scan power,
and testing time,” IEEE Transactions on Computer-Aided Design, Vol. 22, 2003, pp. 352-363.
[13] Chandra, A.; Chakrabarty, K., “Test Data Compression and Decompression Based on Internal Scan
Chains and Golomb Coding”, IEEE Trans. Computer-Aided Design, Volume: 21 , Publication Year:
2002 , Page(s): 715 - 722
[14] A. Jas and N. A. Touba, “Test vector decompression via cyclical scan chains and its application to
testing core- based design,” in Proc. Int. Test Conf., 1998, pp. 458–464.
[15] S. J. Wang and S. N. Chiou, “Generating efficient tests for continuous scan,” in Proceedings of
Design Automation Conference, 2001, pp. 162-165.
[16] A. Chandra and K. Chakrabarty, “System-on-a-chip test data compression and decompression
architectures based on Golomb codes,” IEEE Trans. Computer-Aided Design, vol. 20, pp. 355–368,
Mar.2001.
[17] S. Hellebrand, H.-G. Liang, and H.-J. Wunderlich, “A mixed-mode BIST scheme based on reseeding
of folding counters,” in Proc. Int. Test Conf., 2000, pp. 778–784.
[18] A. Chandra and K. Chakrabarty, “System-on-a-chip test data compression and decompression
architectures based on Golomb codes,” IEEE Trans. Computer-Aided Design, vol. 20, pp. 355–368,
Sep. 2000.
[19] Huizhuo Niu, Yuanyuan Shang, Xinhua Yang, Dawei Xu, Baoyuan Han, Chuan Chen, “Design and
Research on the JPEG-LS Image Compression Algorithm”, 2010 Second International Conference on
Communication Systems, Networks and Applications
[20] Tsung-Han Tsai, Member, IEEE, and Yu-Hsuan Lee, “A 6.4 Gbit/s Embedded Compression Codec
for Memory-Efficient Applications on Advanced-HD Specification”, IEEE TRANSACTIONS ON
CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 20, NO. 10, OCTOBER 2010 1277

AREVIEW PAPER:NOISE MODELS IN DIGITAL IMAGE PROCESSING
Ajay Kumar Boyat1
and Brijendra Kumar Joshi2
1
Research Scholar, Department of Electronics Telecomm and Computer Engineering, Military College of
Tele Communication Engineering, Military Head Quartar of War (MHOW), Ministry of Defence, Govt.
of India, India
2
Professor, Department of Electronics Telecomm and Computer Engineering, Military College of Tele
Communication Engineering, Military Head Quartar of War (MHOW), Ministry of Defence, Govt. of
India, India
ABSTRACT
Noise is always presents in digital images during image acquisition, coding, transmission, and processing
steps. Noise is very difficult to remove it from the digital images without the prior knowledge of noise
model. That is why, review of noise models are essential in the study of image denoising techniques. In
this paper, we express a brief overview of various noise models. These noise models can be selected by
analysis of their origin. In this way, we present a complete and quantitative analysis of noise models
available in digital images.
KEYWORDS
Noise model, Probability density function, Power spectral density (PDF), Digital images.

REFERENCES
[1] Gonzalez R. C., & Woods R. E. (2002) “Digital Image Processing,” second ed., Prentice Hall,
Englewood, Cliffs, NJ.
[2] Bovick A. (2000) “Handbook of Image and Video processing,” Acedemic press, New York.
[3] Patil, J. & Jadhav S. (2013) “A Comparative Study of Image Denoising Techniques,” International
Journal of Innovative Research in Science, Engineering and Technology, Vol. 2, No. 3.
[4] Dougherty G. (2010) “Digital Image Processing for Medical Applications,” second ed., Cambridge
university press.
[5] Boyat, A. and Joshi, B. K. (2013) “Image Denoising using Wavelet Transform and Median Filtering’,
IEEE Nirma University International Conference on Engineering,” Ahemdabad.
[6] Astola J. & Kuosmanen P. (1997) “Fundamentals of nonlinear digital filtering,” CRC Press, Boca
Raton.
[7] Mallet S. (1998) “A Wavelet Tour of Signal Processing,” Academic Press, New York.
[8] Catipovic M. A., Tyler P. M., Trapani J. G., & Carter A. R., (2013) “Improving the quantification of
Brownian motion,” American Journal of Physics, Vol. 81 No. 7 pp. 485-491.
[9] Bhattacharya J. K., Chakraborty D., & Samanta H. S., (2005) “Brownian Motion - Past and Present,”
Cornall university library. arXiv:cond-mat/0511389
[10] Radenovic A., “Brownian motion and single particle tracking,” Advanced Bioengineering methods
laboratory, Ecole polyteachenique federal de Lausanne.
[11] Peidle J., Stokes C., Hart R., Franklin M., Newburgh R., Pahk J., Rueckner W. & Samuel AD,
(2009) “Inexpensive microscopy for introductory laboratory courses,” American Journal of Physics Vol.
77 pp. 931-938.
[12] Nakroshis P., Amoroso M., Legere J. & Smith C., (2003) “Measuring Boltzmann’s constant using
video microscopy of Brownian motion,” American Journal of Physics, Vol. 71, No. 6, pp. 568-573.
[13] Chabay R. W., & Sherwood B. A., (2009) “Matter and Interactions,” 3rd edition, John Wiley and
Sons.
[14] Joshi, A., Boyat, A. and Joshi, B. K. (2014) “Impact of Wavelet Transform and Median Filtering on
removal of Salt and Pepper noise in Digital Images,” IEEE International Conference on Issues and
Challenges in Intelligant Computing Teachniques, Gaziabad.
[15] Hosseini H. & Marvasti F., (2013) “Fast restoration of natural images corrupted by high-density
impulse noise,” EURASIP Journal on Image and Video Processing. doi:10.1186/1687-5281-2013-15
[16] Koli M. & Balaji S., (2013) “Literature survey on impulse noise reduction,” Signal & Image
Processing : An International Journal (SIPIJ) Vol.4, No.5.
[17] Benzarti F. & Amiri H., (2013) “Speckle Noise Reduction in Medical Ultrasound Images,” Signal,
Image and Pattern Recognition Laboratory, Engineering School of Tunis (ENIT).
[18] Kaur T., Sandhu M. & Goel P. “Performance Comparison of Transform Domain for Speckle
Reduction in Ultrasound Image” International Journal of Engineering Research and Application, Vol.
2, Issue 1, pp.184-188.
[19] Salivahanan S., Vallavaraj A. & Gnanapriya C. (2008) “Digital Signal Processing,” Tata
McgrawHill, Vol. 23, NewDelhi.
[20] Zhang L., Dong W., Zhang D. & Shi G. (2010) “Two stage denoising by principal component
analysis with local pixel grouping,” Elsevier Pattern Recognition, Vol. 43, Issue 4, pp. 1531-1549.
[21] Boyat, A. and Joshi, B. K. (2014) ‘Image Denoising using Wavelet Transform and Wiener Filter
based on Log Energy Distribution over Poisson-Gaussian Noise Model’, In Press, IEEE International
Conference on Computational Intelligence and Computing Research, Coimbatore.
[22] Luisier, F., Blu, T. and Unser, M. (2011) ‘Image denoising in mixed Poisson-Gaussian noise’, IEEE
Trans. Image Process., Vol. 20, No. 3, pp. 696–708.
[23] Makitalo, M. and Foi, A. (2013) “Optimal inversion of the genralized Anscombe transformation for
Poisson-Gaussian noise,” IEEE Trans. Image Process., vol. 22, no. 1, pp. 91-103.

[24] Behrens R. T. (1990) “Subspace signal processing in structured noise,” Thesis, Faculty of the
Graduate School of the University of Colorado, the degree of Doctor of Philosophy, Department of
Electrical and Computer Engineering.
[25] Schowengerdt R. A. (1983) “Techniques for Image Processing and classifications in Remote
Sensing,” First Edition Academic Press.
[26] Kamboj P. & Rani V., (2013) “A Brief study of various noise models and filtering techniques,”
Journal of Global Research in Computer Science, Vol. 4, No. 4.
[27] T. Chhabra, G. Dua and T. Malhotra (2013) “Comparative Analysis of Denoising Methods in CT
Images” International Journal of Emerging Trends in Electrical and Electronics, Vol. 3, Issue 2.

October 2022: Top 10 Read Articles in Signal & Image Processing

More Related Content

Similar to October 2022: Top 10 Read Articles in Signal & Image Processing (20)

Recently uploaded (20)

October 2022: Top 10 Read Articles in Signal & Image Processing