Signal & Image Processing: An International Journal (SIPIJ)

ISSN:  0976 – 710X (Online); 2229 – 3922 (print)

Evaluation of Texture as an Input of Spatial Context for Machine Learning Mapping of Wildland Fire Effects

Dale Hamilton, Barry Myers, and Jonathan Branham

Assistant Professor of Computer Science Northwest Nazarene University, Nampa, Idaho, USA 


A variety of machine learning algorithms have been used to map wildland fire effects, but previous attempts to map post-fire effects have been conducted using relatively low-resolution satellite imagery. Small unmanned aircraft systems (sUAS) provide opportunities to acquire imagery with much higher spatial resolution than is possible with satellites or manned aircraft. This effort investigates improvements achievable in the accuracy of post-fire effects mapping with machine learning algorithms that use hyperspatial (sub-decimeter) drone imagery. Spatial context using a variety of texture metrics were also evaluated in order to determine the inclusion of spatial context as an additional input to the analytic tools along with the three-color bands. This analysis shows that the addition of texture as an additional fourth input increases classifier accuracy when mapping post-fire effects.


Hyperspatial Imagery, Small Unmanned Aircraft Systems, Texture, Support Vector Machine, Wildland Fire.

For More Details:

Volume Link:


[1]     K. Barrett, E. Kasischke, A. McGuire, M. Turetsky, and E. Kane. 2010. Modeling fire severity in black spruce stands in the Alaskan boreal forest using spectral and non-spectral geospatial data. Remote Sensing of Environment 114, 1494-1503.

[2]     K. Brewer, C. Winne, R. Redmond, D. Opitz, and M. Mangrich. 2005. Classifying and mapping wildfire severity. Photogrammetric Engineering & Remote Sensing 71, 1311-1320.

[3]     I. Gitas, G. Mitri, and G. Ventura. 2004. Object-based image classification for burned area mapping of Creus Cape, Spain, using NOAA-AVHRR imagery. Remote Sensing of Environment 92, 409-413.

[4]     D. Hamilton, M. Bowerman, J. Colwell, G. Donahoe, and B. Myers. In Press. A Spectroscopic Analysis for Mapping Wildland Fire Effects from Remotely Sensed Imagery. Journal of Unmanned Vehicle Systems.

[5]     D. Hamilton and W. Hann. 2015. Mapping Landscape Fire Frequency for Fire Regime Condition Class. Proceedings of the large wildland fires conference; May 19-23, 2014; Missoula, MT. Proc. RMRS-P-73. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 111-119.

[6]     J. Han, M. Kamber and J. Pei. 2012. Data Mining: Concepts and Techniques.

[7]     R. Haralick and K. Shanmugam. 1973. Textural features for image classification. IEEE transactions on systems, man, and cybernetics 610-621.

[8]     Martin Herold, Dar A. Roberts, Margaret E. Gardner and Philip E. Dennison. 2004. Spectrometry for urban area remote sensing—Development and analysis of a spectral library from 350 to 2400 nm. Remote Sensing of Environment 91, 304-319.

[9]     A. Hudak, R. Ottmar, R. Vihnanek, N. Brewer, A. Smith, and P. Morgan. 2013. The relationship of post-fire white ash cover to surface fuel consumption. International Journal of Wildland Fire 22, 780- 785.

[10]   C. Kontoes, H. Poilve, G. Florsch, I. Keramitsoglou, and S. Paralikidis. 2009. A comparative analysis of a fixed thresholding vs. a classification tree approach for operational burn scar detection and mapping. International Journal of Applied Earth Observation and Geoinformation 11, 299-316.

[11]   A. Laliberte, J. Herrick, A. Rango, and C. Winters. 2010. Acquisition, orthorectification, and objectbased classification of unmanned aerial vehicle (UAV) imagery for rangeland monitoring. Photogrammetric Engineering & Remote Sensing 76, 661-672.

[12]   L. Lentile, Z. Holden, A. Smith, M. Falkowski, A. Hudak, P. Morgan, S. Lewis, P. Gessler, and N. Benson. 2006. Remote sensing techniques to assess active fire characteristics and post-fire effects. International Journal of Wildland Fire 15, 319-345.

[13]   P. Morgan, E. Heyerdahl, C. Miller, A. Wilson, and C. Gibson. 2014. Northern Rockies Pyrogeography: An Example of Fire Atlas Utility. Fire Ecology. 10, 14

[14]   National Aeronautics and Space Administration (NASA). LANDSAT 7. Retrieved April, 2017 from

[15]   National Interagency Fire Center (NIFC). Federal Firefighting Costs. Retrieved April, 2017 from

[16]   A. Puissant, J. Hirsch, and C. Weber. 2005. The utility of texture analysis to improve perpixel classification for high to very high spatial resolution imagery. International Journal of Remote Sensing 26, 733-745.

[17]   S. Russell and P. Norvig. 2010. Artificial intelligence: A modern approach. Prentice Hall, Upper Saddle River, NJ.

[18]   Salimi, Ziaii, Amiri, HosseinjaniZadeh, Karimpouli, &Moradkhani. (2017). Using a Feature Subset Selection method and Support Vector Machine to address curse of dimensionality and redundancy in Hyperion hyperspectral data classification. The Egyptian Journal of Remote Sensing and Space Sciences, The Egyptian Journal of Remote Sensing and Space Sciences.

[19]   J. Scott and E. Reinhardt. 2001. Assessing crown fire potential by linking models of surface and crown fire behavior. USDA Forest Service Research Paper 1.

[20]   A. Smith and A. Hudak. 2005. Estimating combustion of large downed woody debris from residual white ash. International Journal of Wildland Fire 14, 245-248.

[21]   E. Wood, A. Pidgeon, V. Radeloff, and N. Keuler. 2012. Image texture as a remotely sensed measure of vegetation structure. Remote Sensing of Environment 121, 516-526.

[22]   O. Zammit, X. Descombes, and JosianeZerubia. 2006. Burnt area mapping using support vector machines. Forest Ecology and Management 234, S240.


 Dale Hamilton received an M.S. in Computer Science from the University of Montana in 1997 and is currently pursuing a Ph.D in Computer Science from the University of Idaho. Dale joined the faculty at Northwest Nazarene University in 2013 where he is an Assistant Professor of Computer Science. In addition to teaching a variety of Computer Science courses, Dale is the Primary Investigator on NNU’s NASA funded Fire Monitoring and Assessment Platform (FireMAP) project, using machine learning and computer vision to map wildland fire extent and severity from imagery acquired with unmanned aircraft system.

Barry Myers received his Ph.D. in Information Science from the University of North Texas in 2003. Dr. Myers currently serves as Professor of Computer Science and the Chair of the Mathematics and Computer Science Department at Northwest Nazarene University (NNU). Prior work experience includes systems programming at AT&T Bell Laboratories and systems analysis at Shell Oil Company. His research interests include systems development, bioinformatics, and ecoinformatics.

Jonathan Branham is pursuing a B.A. in Computer Science at Northwest Nazarene University, which he anticipates finishing in May 2018.


Alessandro Massaro, Valeria Vitti, Angelo Galiano

Dyrecta Lab, IT research Laboratory, via Vescovo Simplicio,45, 70014 Conversano (BA), Italy


We propose in this work a study of an image processing engine able to detect automatically the features of electronic board weldings. The engine has been developed by using ImageJ and OpenCV libraries. Specifically the image processing segmentation has been improved by watershed approach. After a complete design of the automation processes, different test have been performed showing the engine efficiency in terms of features extraction, scale setting and thresholding calibration. The engine provides as outputs the storage of the cropped images of each single defects. The proposed engine together with the post-processing 3D imaging represent a good tool for the management of the production quality of electronic boards.


ImageJ, Image Processing, Watershed Segmentation, OpenCV, Electronic Boards Quality System, Welding Defects.

For More Details:

Volume Link:


[1]     Nayana, H. G., Deepa, P., Anitha, D. B., & Mahesh, R. (2017), A review on defect detection of SMT and Through Hole Components in assembled PCB,” International Journal of Advanced Research in Computer Science & Technology, Vol. 5, No. 2, pp34-38.

[2]     Strauss, R. (1998) “SMT Soldering Handbook”, Newnes 1998.

[3]     Mar, N. S. S., Fookes, C., & Yarlagadda, P.K.D.V. (2009), Design of automatic vision-based inspection system for solder joint segmentation,” International Journal of Advanced Research in Computer Science & Technology, Vol. 34, No. 2, pp145-151.

[4]     Massaro, A., Ekuakille, A.L., Caratelli, D., Palamara I., & Morabito, F.C. (2012), Optical performance evaluation of oil spill detection methods: thickness and extent,” IEEE Transactions on Instrumentation & Measurement, Vol. 61, No. 12, pp3332-3339.

[5]     Seal, A., Das, A., & Sen, P. (2015), “Watershed: an image segmentation approach,” International Journal of Computer Science and Information Technologies, Vol. 6, No. 3, pp2295-2297.

[6]     Belaid, L. J., &  Mourou, W., Sen, P. (2009), Image segmentation: a watershed transformation algorithm,” Image Analysis & Stereology, Vol. 28, pp93-102.

[7]     Bora, D. J., Gupta, A. K. &  Khan A. (2015), Color image segmentation using an efficient fuzzy based watershed approach,” Signal & Image Processing : An International Journal (SIPIJ), Vol. 6, No. 5, pp15-34.

[8]     Beucher, S., &  Meyer F. (1993), The morphological approach to segmentation: the watershed transformation,” Mathematical Morphology in Image Processing, Vol. 34, pp433-481.

[9]     Dilpreet, K., &  Ydwinder K. (2014), Various image segmentation techniques: a review,” International Journal of Computer Science and Mobile Computing, Vol. 3, No. 5, pp809-814.

[10]   Abràmoff, M. D., Magalhães, P., J., & Ram S. J. (2004), “Image Processing with ImageJ,” Biophotonics International,, Vol. 11, No. 7, pp36-42.

[11]   Haeri, M. & Haeri, M. (2015), ImageJ plugin for analysis of porous scaffolds used in tissue engineering,” Journal of Open Research Software, Vol. 3: e1, pp.1-4.

[12]   Rishi, Rana, N. (2015) Particle size and shape analysis using ImageJ with customized tools for segmentation of particles,” International Journal of Engineering Research & Technology (IJERT), Vol. 4, No. 11, pp.247- 250.

[13]   Cicala, G., Massaro, A., Velardi L., Senesi, G. S. & Valentini, A. (2014) Self-assembled pillar-like structures in nanodiamond layers by pulsed spray technique”, ACS Applied Materials & Interfaces, Vol. 6, No. 23, pp21101-21109.

[14]   Nidhi (2015) “Image processing and object detection”, International Journal of Applied Research, Vol. 1, No. 9, pp396-399.

[15]   Puri, N., Kaushik, S. (2011), Analysis of the variants of watershed algorithm as a segmentation technique in image processing,” Proceeding of 5th IEEE International Conference on Advanced Computing & Communication Technologies, pp441-445.

[16]   Moumena Al-Bayati & Ali El-Zaart (2013) Automatic thresholding techniques for optical images”, Signal & Image Processing : An International Journal (SIPIJ),  Vol.4, No.3, pp1-15.

[17]   Chatterjee, R. K., & Kar, A. (2017) Optimal global threshold estimation using statistical change-point detection”, Signal & Image Processing : An International Journal (SIPIJ),  Vol.8, No.4, pp15-24.

[18]   Beucher, S., Rivest, J. F., & Soille, P. (1993) Morphological gradients”, Journal of Electronic Imaging,  Vol.2, pp326-336.

[19]   “UMLet 14.2 Free UML Tool for Fast UML Diagrams” 2018. [Online]. Available:

[20]   “Eclipse Mars Release” 2018. [Online]. Available:

[21]   “imagej-1.46.jar” 2018. [Online]. Available:

[22]   “OpenCV” 2018. [Online]. Available:

[23] “Interactive 3D Surface Plot” 2018. [Online]. Available:

Corresponding Author 8

Alessandro Massaro: Research & Development Chief  of Dyrecta Lab s.r.l.



Alessandro Massaro, Valeria Vitti, Giuseppe Maurantonio, Angelo Galiano

Dyrecta Lab, IT research Laboratory, via Vescovo Simplicio, 45, 70014 Conversano (BA), Italy


The implementation of a stand-alone system developed in JAVA language for motion detection has been discussed. The open-source OpenCV library has been adopted for video surveillance image processing thus implementing Background Subtraction algorithm also known as foreground detection algorithm. Generally the region of interest of a body or object to detect  is related to a precise objects (people, cars, etc.) emphasized on a background. This technique is widely used for tracking a moving objects. In particular, the Background Subtractor MOG2 algorithm of OpenCV has been applied. This algorithm is based on Gaussian distributions and offers better adaptability to different scenes due to changes in lighting and the detection of shadows as well. The implemented webcam system relies on saving frames and creating GIF and JPGs files with previously saved frames. In particular the Background Subtraction function, find Contours, has been adopted to detect the contours. The numerical quantity of these contours has been compared with the tracking points of sensitivity obtained by setting  an user-modifiable slider able to save the frames as GIFs composed by different merged JPEGs. After a full design of the image processing prototype different motion test have been performed. The results showed the importance to consider few sensitivity points in order to obtain more frequent image storages also concerning minor movements. Sensitivity points can be modified through a slider function and are inversely proportional to the number of saved images. For small object in motion will be detected a low percentage of sensitivity points. Experimental results proves that the setting condition are mainly function of the typology of moving object rather than the light conditions. The proposed prototype system is suitable for video surveillance smart camera in industrial  systems.


Video Surveillance, OpenCV, Image Processing, OpenCV, Image Segmentation, Background Subtraction, Contour Extraction, Camera Motion Sensitivity.

For More Details:

Volume Link:


[1]     Salarpour, A., Salarpour, A., Fathi, M., & Dezfoulian, M. (2011), Vehicle tracking using Kalman filter and features,” Signal & Image Processing : An International Journal (SIPIJ), Vol. 2, No. 2, pp1-8.

[2]     Raad, A. H., Ghazali, S., & Loay, E. G. (2014), Vehicle detection and tracking techniques: a concise review,” Signal & Image Processing : An International Journal (SIPIJ), Vol. 5, No. 1, pp1-12.

[3]     Kishore, D. R., Mohan, M., & Rao, A. A. (2016), A novel probabilistic based image segmentation model for real time human activity detection,” Signal & Image Processing : An International Journal (SIPIJ), Vol. 7, No. 6, pp11-27.

[4]     Sujatha, G. S., & Kumari, V. V. (2016), An innovative moving object detection and tracking system by using modified region growing algorithm,” Signal & Image Processing : An International Journal (SIPIJ), Vol. 7, No. 2, pp39-55.

[5]     El-Taher, H. E., Al-hity, K. M., & Ahmed M. M. (2014) “Design and imnplementation of video tracking system based on camera field of view,” Signal & Image Processing : An International Journal (SIPIJ), Vol. 5, No. 2, pp119-129.

[6]     Massaro, A., Vitti, V., & Galiano, A. (2018) Automatic image processing engine oriented on quality control of electronic boards,” Signal & Image Processing: An International Journal (SIPIJ), Vol. 9, No. 2, pp1-14.

[7]     Bouwmans, T., El Baf, F., & Vachon, B. (2008) Background modeling using mixture of gaussians for foreground detection-a survey,” Recent Patents on Computer Science, Vol. 1, No. 3, pp219-237.

[8]     Sen-Ching S., & Kamath, C. (2005)  Robust background subtraction with foreground validation for urban traffic video,” Eurasip Journal on applied signal processing, Vol. 14, pp1-11.

[9]     Toyama, K., Krumm, J., Brumitt B., & Meyers, B. (1999) Wallflower: principles and practice of background maintenance,” The Proceedings of the Seventh IEEE International Conference on Computer Vision, Vol. 1, pp255-261.

[10]   Carranza, J., Theobalt, C., Magnor, M. A. & Seidel, H. P. (2003) Free-viewpoint video of human actors,” ACM transactions on graphics (TOG), Vol. 22, No. 3, pp569-577.

[11]   Mikić, I., Trivedi, M., Hunter, E., & Cosman, P. (2003) Human body model acquisition and tracking using voxel data,” International Journal of Computer Vision, Vol. 53, No. 3, pp199-223.

[12]   Warren, J. (2003) Unencumbered full body interaction in video games,” in Master’s Thesis, Parsons School of Design, 2003.

[13]   Elhabian, S. Y., El-Sayed K. M., & Ahmed, S. H. (2008) Moving object detection in spatial domain using background removal techniques-state-of-art,” Recent Patents on Computer Science, Vol. 1, No. 1, pp 32-54.

[14]   McFarlane  N. J., & Schofield, C. P. (1995) “Segmentation and tracking of piglets in images,” Machine Vision and Applications, Vol. 8, No. 3, pp187-193.

[15]   Zheng, J., Wang, Y., Nihan N., & Hallenbeck, M. (2006) Extracting roadway background image: Mode-based approach,” Transportation Research Record: Journal of the Transportation Research Board, Vol. 1944, pp82-88.

[16]   Elgammal, A., Harwood, D., & Davis, L. (2000) Non-parametric model for background subtraction,”  Proceeding of European Conference on Computer VisionBerlin, Springer, pp751-767.

[17]   Sigari, M. H., Mozayani, N., & Pourreza, H. (2008) Fuzzy running average and fuzzy background subtraction: concepts and application,” International Journal of Computer Science and Network Security, Vol. 8, No. 2, pp138-143.

[18]   Chang, R., Gandhi, T. & Trivedi, M. M. (2004) Vision modules for a multi-sensory bridge monitoring approach,” Proceeding of The 7th International IEEE Conference on Intelligent Transportation Systems, pp971-976.

[19]   Wang H. & Suter, D. (2006) A novel robust statistical method for background initialization and visual surveillance, Proceeding of Asian Conference on Computer Vision, Berlin, Springer, pp 328-337.

[20]   Porikli F., & Tuzel, O. (2003) Human body tracking by adaptive background models and mean-shift analysis,” IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, pp1-9.

[21]   Porikli F., & Tuzel, O. (2005) Bayesian background modeling for foreground detection,” Proceedings of the third ACM international workshop on Video surveillance & sensor networks, pp55-58.

[22]   “cv::BackgroundSubtractorMOG2 Class Reference” 2018. [Online]. Available:

[23]   Wren, C. R., Azarbayejani, A., Darrell, T. & Pentland, A. P. (1997) “Pfinder: Real-time tracking of the human body,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 19, No. 7, pp780-785.

[24]   Stauffer C., & Grimson, W. E. L. (1999) Adaptive background mixture models for real-time tracking,” Proceeding of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 2, pp246-252.

[25]   Eveland, C., Konolige K., & Bolles, R. C., «Background modeling for segmentation of video-rate stereo sequences,» IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 266-271, 1998.

[26]   Gordon, G., Darrell, T., Harville, M., & Woodfill, J. (1999) Background estimation and removal based on range and color,” Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 2, pp459-464.

[27]   “UMLet 14.2 Free UML Tool for Fast UML Diagrams” 2018. [Online]. Available:

Corresponding Author 

Alessandro Massaro: Research & Development Chief  of Dyrecta Lab s.r.l.


 Jayasree M1, N K Narayanan2,  Kabeer V3 and Arun C R4

1Dept. of Computer Science & Engineering, Govt. Engineering College, Thrissur, India 2College of Engineering, Vadakara, India 3Department of Computer Science, Farook College, Kozhikode, India 4Dept. of Electronics & Comm. Engineering, Model Engineering College, Kochi, India


Edge detection is a crucial step in various image processing systems like computer vision , pattern recognition and feature extraction. The Canny edge detection algorithm even though exhibits high accuracy, is computationally more complex compared to other edge detection techniques. A block based distributed edge detection technique is presented in this paper, which adaptively finds the thresholds for edge detection depending on block type and the distribution of gradients in each block. A novel method of computation of high threshold has been proposed in this paper.  Block-based hysteresis thresholds are computed using a non uniform gradient magnitude histogram.  The algorithm exhibits remarkably high edge detection accuracy, scalability and significantly reduced computational time. Pratt’s Figure of Merit quantifies the accuracy of the edge detector, which showed better values than that of original Canny and distributed Canny edge detector for benchmark dataset. The method detected all visually prominent edges for diverse block size.


Canny edge detection, edge detection, gradient magnitude histogram, hysteresis threshold.

For More Details:

 Volume Link:


[1]     Pratt, W. K . Digital Image Processing, 3rd ed.; Publisher: Wiley Interscience New York, USA, 2001; pp. 459–498

[2]     Ziou, Djemel.; Salvatore, Tabbone. Edge detection techniques-an overview. Pattern Recognition and Image Analysis C/C of Raspoznavaniye Obrazov I Analiz Izobrazhenii 8, 1998, pp 537-559

[3]     Gardiner, B., Coleman, S.A. and Scotney, B.W., 2016. Multiscale Edge Detection Using a Finite Element Framework for Hexagonal Pixel-Based ImagesIEEE Transactions on Image Processing, 25(4), pp.1849-1861.

[4]     Dollár, Piotr, and C. Lawrence Zitnick. “Fast edge detection using structured forests.” IEEE transactions on pattern analysis and machine intelligence 37.8 (2015): 1558-1570.

[5]     Melin, P., Gonzalez, C.I., Castro, J.R., Mendoza, O. and Castillo, O., 2014. Edge-detection method for image processing based on generalized type-2 fuzzy logic. IEEE Transactions on Fuzzy Systems, 22(6), pp.1515-1525.

[6]     Swaminathan, A. and Ramapackiyam, S.S.K., 2014. Edge detection for illumination varying images using wavelet similarity. IET Image Processing, 8(5), pp.261-268.

[7]     Sun, Q., Qiao, Y., Wu, H. and Wang, J., 2016. An Edge Detection Method Based on Adjacent Dispersion. International Journal of Pattern Recognition and Artificial Intelligence, 30 (10), pp 1-17

[8]     Wang, Jian, Zhen-Qiang Yao, Quan-Zhang An, Yao-Jie Zhu, Xue-Ping Zhang, Wei-Bin Gu, Xin-Guang Liang et al. RIDED-2D: a rule-based instantaneous denoising and edge detection method for 2D range scan line.” International Journal of Pattern Recognition and Artificial Intelligence 25, no. 06 (2011): 807-833.

[9]     Akinlar, C. and Topal, C., 2012. EDPF: a real-time parameter-free edge segment detector with a false detection control. International Journal of Pattern Recognition and Artificial Intelligence, 26(01), p.1255002.

[10]   Luo, Y., Duraiswami, R. Canny edge detection on NVIDIA CUDA, Proceedings of the  IEEE CVPRW, Alaska, USA ,Jun. 23-28, 2008, pp. 1–8.

[11]   Canny, J. F. A computation approach to edge detection.  IEEE Trans. Pattern Anal. Mach. Intell. 1986, volume. 8, no. 6, pp. 679–698, DOI: 10.1109/TPAMI.1986.4767851

[12]   R, Deriche. Using canny criteria to derive a recursively implemented optimal edge detector. Int. J. Comput. Vis., 1987,volume. 1, no. 2, pp. 167–187

[13]   L. Torres.; M, Robert, E, Bourennane.; M, Paindavoine. Implementation of a recursive real time edge detector using retiming technique, Proc. Asia South Pacific IFIP Int. Conf. Very Large Scale Integr., pp. 811–816

[14]   F. G, Lorca.; L, Kessal.; D, Demigny. Efficient ASIC and FPGA implementation of IIR filters for real time edge detection, in Proc. IEEE ICIP, 1997, vol. 2, pp. 406–409

[15]   D. V, Rao.; M, VenkatesanAn efficient reconfigurable architecture and implementation of edge detection algorithm using handle-C, in Proc. IEEE Conf. ITCC, 2004, vol. 2, pp. 843–847, DOI

[16]   H,  Neoh.; A, Hazanchuck. Adaptive edge detection for real-time video processing using FPGAs, Altera Corp., San Jose, CA, USA, Application Note, 2005. Available online: (Accessed on 24th Nov 2016)

[17]   C, Gentsos.; C, Sotiropoulou.; S, Nikolaidis.; N, Vassiliadis.; Real time canny edge detection parallel implementation for FPGAs, in Proc.IEEE ICECS, 2010, pp. 499–502

[18]   W, He.; K, Yuan.; An improved canny edge detector and its realization on FPGA, in Proc. IEEE 7th WCICA, 2008, pp. 6561–6564.

[19]   R, Palomar, J. M, Palomares.; J. M, Castillo.; J, Olivares.; J, Gómez-Luna. Parallelizing and optimizing lip-canny using NVIDIA CUDA, in Proc. IEA/AIE, Berlin, Germany, 2010, pp. 389–398.

[20]   L. H. A, Lourenco. Efficient implementation of canny edge detection filter for ITK using CUDA, in Proc. 13th Symp. Comput. Syst., 2012, pp. 33–40.

[21]   Qian, Xu.; Srenivas, Varadarajan.; Chaitali,Chakrabarti.; Lina J, Karam.; A Distributed Canny Edge Detector: Algorithm and FPGA Implementation, IEEE transactions on image processing, 2014, vol. 23, no. 7, pp. 2944 – 2960

[22]   Rafael C, Gonzalez.; Richard E, Woods.; Digital Image Processing , 3rd ed.; Publisher: Prentice Hall New Jersey, United States, 2006, pp. 689-763

[23]   S, Nercessian.; A new class of edge detection algorithms with performance measure, M.S. thesis, Dept. Electr. Eng., Tufts Univ., Medford, MA, USA, 2009.

[24]   J. K, Su.; R. M, Mersereau.; Post-processing for artifact reduction in JPEG-compressed images, in Proc. IEEE ICASSP, 1995, vol. 4, pp. 2363-2366

[25]   Q, Xu.; C, Chakrabarti.; L J, Karam. A distributed Canny edge detector and its implementation on FPGA,  in Proc. DSP/SPE, 2011, pp. 500–505.

[26]   Berkeley Segmentation Dataset. Available online:  (Accessed on 21 Nov 2016)

[27]   Kodak Dataset. Available online: (Accessed on 21 Nov 2016)


 Majdi Al-qdah

Department of Computer Engineering, University of Tabuk, Tabuk, KSA


This paper presents a hybrid watermarking technique for medical images.  The method uses a combination of three transforms: Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), and singular value decomposition (SVD).  Then, the paper discusses the results of applying the combined method on different medical images from eight patients.  The images were watermarked with a small watermark image representing the patients’ medical data.  The visual quality of the watermarked images (before and after attacks) was analyzed using five quality metrics: PSNR, WSNR, PSNR-HVS-M, PSNR-HVS, and MSSIM. The first four metrics’ average values of the watermarked medical images before attacks were approximately 32 db, 35 db, 42 db, and 40 db respectively; while the MSSM index indicated a similarity between the original and watermarked images of more than 97%. However, the metric values decreased significantly after attacking the images with various operations even though the watermark image could be retrieved after almost all attacks. In brief, the initial results indicate that watermarking medical images with patients’ data does not significantly affect their visual quality and they can still be used by medical staff.


Transforms, Watermarking, medical images, visual metrics

For More Details:

 Volume Link:


 [1]     Lee, S.hyun. & Kim Mi Na, (2008) “This is my paper”, ABC Transactions on ECE, Vol. 10, No. 5, pp120-122.

[2]     Gizem, Aksahya & Ayese, Ozcan  (2009)  Coomunications & Networks,  Network Books,  ABC    Publishers.

[3]     Ashourian (2006), A new mixed spatial domain watermarking of three dimensional triangle mesh, proceeding of the 4th international conference on computer graphics and interactive techniques in Australia and Southeast Asia

[4]     Ahmed (2008), Intelligent watermark recovery using spatial domain extension, International conference on intelligent information hiding and multimedia signal processing, IIHMSP’ 08

[5]     Lai, C.C., Tsai, C.C. (2010): Digital Image Watermarking Using Discrete Wavelet Transform and  Singular Value Decomposition. IEEE Transactions on Instrumentation and Measurement 59(11), 3060-3063

[6]     Soliman MM, Hassanien AE, Ghali NI, Onsi HM, (2012) “An Adaptive Watermarking Approach for Medical Imaging using Swarm Intelligence, Int Journal Smart Home 6:37-50

[7]     Zain J, Clarke M, (2011) Security in Telemedicine: Issue in Watermarking Medical Images, International Conference: Science of Electronic, Technologies of Information and Telecommunications

[8]     Lin W-H, Horng S-J, Kao T-W, Chen R-J, Chen Y-H, Lee C-L, Terano T (2009) Image copyright protection with forward error correction. Expert Syst Appl 36(9):11888–11894

 [9]     Rosiyadi D, Horng S-J, Fan P, Wang X (2012) Copyright protection for e-government document images. IEEE MultiMedia 19(3):62–73

[10]   Shi-Jinn H, Rosiyadi D, Fan P, Wang X, Khan MK (2014) An adaptive watermarking scheme for e-government document images. Multimed Tools Appl 72(3):3085

[11]   Singh AK, Dave M, Mohan A (2014) Hybrid technique for robust and imperceptible image watermarking in DWT- DCT-SVD domain. Natl Acad Sci Lett 37(4):351–358

[12]   Giakoumaki A, Pavlopoulos S, KoutsourisD (2006) Secure and efficient health data management through multiple watermarking on medical images. Med Biol Eng Comput 44:619–631

[13]   Lin W-H, Wang Y-R, Horng S-J, Kao T-W, Pan Y (2009) A blind watermarking method using maximum wavelet coefficient quantization. Expert Syst Appl 36(9):11509–11516

[14]   Liu, R., Tan, T. (2002): An SVD-based watermarking scheme for protecting rightful ownership, IEEE Transactions on Multimedia 4(1), 121-128

[15]   N. Ponomarenko, V. Lukin, M. Zriakhov, K. Egiazarian, and J. Astola (2006), Estimation of accessible quality in noise image compression, in Proceedings of European Signal Processing Conference (EUSIPCO ’06), pp. 1–4, Florence, Italy.

[16]   S. G. Chang, B. Yu, and M. Vetterli, (2000) Adaptive wavelet thresholding for image denoising and compression, IEEE Transactions on Image Processing, vol. 9, no. 9, pp. 1532–1546.

[17]   Shaick (2000), A hybrid transform method for image denoising.  10th European. Signal Processing Conference,

[18]   Z. Wang and A. C. Bovik (2006). Modern Image Quality Assessment. Morgan and Claypool Publishing Company, New York

[19]   Z. Wang, A. C. Bovik, H. R. Sheikh (2004), and E. P. Simoncelli, Image quality assessment: from error visibility to structural similarity,” IEEE Transactions on Image Processing, vol. 13, no. 4, pp. 600–612

[20]   Z. Wang and A. C. Bovik (2009), Mean squared error: love it or leave it? A new look at signal fidelity measures, IEEE Signal Processing Magazine, vol. 26, no. 1, pp. 98–117.

[21]   N. Ponomarenko, F. Battisti, K. Egiazarian, J. Astola, and V. Lukin (2009), Metrics performance comparison for color image database, in Proceedings of the 4th International Workshop on Video Processing and Quality Metrics, pp. 1–6, Scottsdale, Ariz, USA, CD-ROM.

[22]   Zhou Wang1, Eero P. Simoncelli1 and Alan C (2003). Bovik multi-scale structural similarity for image quality assessment.  Proceeding of the 37th IEEE Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, Nov. 9-12, 2003.

[23]   N. Nill, (1985) A visual model weighted cosine transform for image compression and quality assessment, IEEE Transactions on Communications COM-33, pp. 551-557.

[24]   R. F. Zampolo, R. Seara, (2003) A Measure for Perceptual Image Quality Assessment”, in Proc. of Int. Conf. on Image Proc., Barcelona, Spain, pp: 433-436, Sept.


Dr. Majdi is currently an assistant professor in department of computer engineering at the University of Tabuk Saudi Arabia. His research interests include data hiding, cryptography, medical imaging and other various other current engineering topics.


Rohit Kamal Chatterjee1 and Avijit Kar2

1Department of Computer Science & Engineering, Birla Institute of Technology, Mesra, Ranchi, India.

2Department of Computer Science & Engineering, Jadavpur University, Kolkata, India.


Aim of this paper is reformulation of global image thresholding problem as a well-founded statistical method known as change-point detection (CPD) problem. Our proposed CPD thresholding algorithm does not assume any prior statistical distribution of background and object grey levels. Further, this method is less influenced by an outlier due to our judicious derivation of a robust criterion function depending on Kullback-Leibler (KL) divergence measure. Experimental result shows efficacy of proposed method compared to other popular methods available for global image thresholding. In this paper we also propose a performance criterion for comparison of thresholding algorithms. This performance criteria does not depend on any ground truth image. We have used this performance criterion to compare the results of proposed thresholding algorithm with most cited global thresholding algorithms in the literature.


Global image thresholding, Change-point detection, Kullback-Leibler divergence, robust statistical measure, thresholding performance criteria.

For More Details:

Volume Link:


[1]     M. Sezgin and B. Sankur (2004) Survey over image thresholding techniques and quantitative performance evaluation”, J. of Electronic Imaging, Vol. 13, No. 1, pp.146–165.

[2]     A. Rosenfeld and P. De la Torre, (1983) Histogram concavity analysis as an aid in threshold selection”, IEEE Trans. Syst. Man Cybernetics. SMC-13, pp. 231–235.

[3]     M. I. Sezan, (1985) A peak detection algorithm and its application to histogram-based image data reduction”, Graph. Models Image Process. Vol. 29, pp.47–59.

[4]     D. M. Tsai, (1995) A fast thresholding selection procedure for multimodal and unimodal histograms”, Pattern Recogn. Lett. Vol. 16, pp. 653–666.

[5]     A. Pikaz and A. Averbuch, (1996) Digital image thresholding based on topological stable state”, Pattern Recogn. Vol. 29, pp.829–843.

[6]     N. R. Pal and S. K. Pal, (1993) A review on image segmentation techniques,” Pattern Recog., vol. 26, no. 9, pp. 1277–1294.

[7]     P. K. Sahoo, S. Soltani, A. K. C. Wong, and Y. C. Chen, (1988) A survey of thresholding techniques,” Computer Vision, Graphics, and Image Process., vol. 41, no. 2, pp.233–260.

[8]     C. V. Jawahar, P. K. Biswas, and A. K. Ray, (1997) ‘‘Investigations on fuzzy thresholding based on fuzzy clustering,’’ Pattern Recogn., vol. 30, no. 10, pp. 1605–1613.

[9]     H. V. Poor, O. Hadjiliadis, Quickest Detection, Cambridge University Press, New York, 2009.

[10]   J. Chen, A. K. Gupta, (2012) Parametric statistical change point analysis, with applications to genetics, medicine and finance, 2nd Ed., Birkhäuser, Boston.

[11]   Y. Wang, (2011) “Generalized Information Theory: A Review and Outlook”, J. of Inform. Tech., Vol. 10, No. 3, pp. 461-469.

[12]   L. Pardo, (2006) Statistical Inference Based on Divergence Measures, Chapman & Hall/CRC, pp. 233.

[13]   S. Choi, S. Cha, C. C. Tappert, (2010) A Survey of Binary Similarity and Distance Measures,” Systemics, Cybernetics and Informatics Vol. 8, No. 1, pp. 43-48, 2010.

[14]   J. Kittler and J. Illingworth (1986) “Minimum error thresholding”, Pattern Recognition, Vol. 19, pp. 41–47.

[15]   N. Otsu, (1979) A threshold selection method from gray level histograms”, IEEE Trans. Syst. Man Cybern. SMC-9, pp. 62–66.

[16]   T. Kurita, N. Otsu, and N. Abdelmalek, (1992) Maximum likelihood thresholding based on population mixture models”. Pattern Recognition, Vol. 25, pp. 1231-1240.

[17]   P. Sahoo, C. Wilkins, and J. Yeager, (1997) “Threshold selection using Renyi’s entropy”, Pattern Recogn. Vol. 30, pp. 71–84.

[18]   P.K.Sahoo, G.Arora, Image thresholding using two-dimensional Tsallis–Havrda–Charvat entropy”, Pattern Recognition Letters, Vol. 27, pp. 520–528, 2006.

[19]   J. Xue and D. M. Titterington (2011) t-tests, F-tests and Otsu’s Methods for Image Thresholding,” IEEE Trans. Image Processing, vol. 20, no. 8, pp. 2392-2396.

[20]   H. Tizhoosh, (2005) “Image thresholding using type II fuzzy sets”, Pattern Recognition, vol. 38, pp. 2363 – 2372.


Amir Mohammad Esmaieeli Sikaroudi1, Sasan Ghaffari2, Ali Yousefi3, Hassan Sadeghi Naeini4

1Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran 2,3,4Department of Industrial Design, Iran University of Science and Technology, Tehran, Iran


This paper introduces a device, algorithm and graphical user interface to obtain anthropometric measurements of foot. Presented device facilitates obtaining scale of image and image processing by taking one image from side foot and underfoot simultaneously. Introduced image processing algorithm minimizes a noise criterion, which is suitable for object detection in single object images and outperforms famous image thresholding methods when lighting condition is poor. Performance of image-based method is compared to manual method. Image-based measurements of underfoot in average was 4mm less than actual measures. Mean absolute error of underfoot length was 1.6mm, however length obtained from side foot had 4.4mm mean absolute error. Furthermore, based on t-test and f-test results, no significant difference between manual and image-based anthropometry observed. In order to maintain anthropometry process performance in different situations user interface designed for handling changes in light conditions and altering speed of the algorithm.


Foot anthropometry, Image processing, single object image thresholding

For More Details:

Volume Link:


[1]     Bennett, K.A. and R.H. Osborne,(1986) “Interobserver measurement reliability in anthropometry”, Human Biology, Vol. 58 No. 5, pp 751-759.

[2]     Kemper, H. and J. Pieters,(1974) Comparative study of anthropometric measurements of the same subjects in two different institutes“, American journal of physical anthropology, Vol. 40 No. 3, pp 341-343.

[3]     Jamison, P.L. and S.L. Zegura,(1974) A univariate and multivariate examination of measurement error in anthropometry“, American Journal of Physical Anthropology, Vol. 40 No. 2, pp 197-203.

[4]     Hung, P.C.-Y., C.P. Witana, and R.S. Goonetilleke,(2004) Anthropometric measurements from photographic images, Computing Systems, Vol. 29, pp 764-769.

[5]     Meunier, P. and S. Yin,(2000) Performance of a 2D image-based anthropometric measurement and clothing sizing system“, Applied Ergonomics, Vol. 31 No. 5, pp 445-451.

[6]     BenAbdelkader, C. and Y. Yacoob,(2008) Statistical estimation of human anthropometry from a single uncalibrated image“, Computational Forensics, pp 200-20.

[7]     Gittoes, M.J., I.N. Bezodis, and C. Wilson,(2009) An image-based approach to obtaining anthropometric measurements for inertia modeling“, Journal of applied biomechanics, Vol. 25 No. 3, pp 265-270.

[8]     Yeadon, M.R.,(1990) The simulation of aerial movement—II. A mathematical inertia model of the human body“, Journal of biomechanics, Vol. 23 No. 1, pp 67-74.

[9]     Li, Z., et al. Anthropometric body measurements based on multi-view stereo image reconstruction. in Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE. 2013. IEEE.

[10]   Xiong, S., et al.,(2008) Modelling foot height and foot shape-related dimensions“, Ergonomics, Vol. 51 No. 8, pp 1272-1289.

[11]   Witana, C.P., et al.,(2006) Foot measurements from three-dimensional scans: A comparison and evaluation of different methods“, International Journal of Industrial Ergonomics, Vol. 36 No. 9, pp 789-807.

[12]   Agić, A., V. Nikolić, and B. Mijović,(2006) Foot anthropometry and morphology phenomena“, Collegium antropologicum, Vol. 30 No. 4, pp 815-821.

[13]   Krauss, I., et al.,(2011) Sex-related differences in foot shape of adult Caucasians–a follow-up study focusing on long and short feet“, Ergonomics, Vol. 54 No. 3, pp 294-300.

[14]   Lee, Y.-C. and M.-J. Wang,(2015) Taiwanese adult foot shape classification using 3D scanning data“, Ergonomics, Vol. 58 No. 3, pp 513-523.

[15]   Sezgin, M. and B.l. Sankur,(2004) Survey over image thresholding techniques and quantitative performance evaluation“, Journal of Electronic imaging, Vol. 13 No. 1, pp 146-168.

[16]   Huang, L.-K. and M.-J.J. Wang,(1995) Image thresholding by minimizing the measures of fuzziness“, Pattern recognition, Vol. 28 No. 1, pp 41-51.

[17]   Prewitt, J.M. and M.L. Mendelsohn,(1966) The analysis of cell images“, Ann. NY Acad. Sci, Vol. 128 No. 3, pp 1035-1053.

[18]   Ridler, T. and S. Calvard,(1978) Picture thresholding using an iterative selection method“, IEEE transactions on Systems, Man and Cybernetics, Vol. 8 No. 8, pp 630-632.

[19]   Li, C. and P.K.-S. Tam,(1998) An iterative algorithm for minimum cross entropy thresholding“, Pattern Recognition Letters, Vol. 19 No. 8, pp 771-776.

[20]   Kapur, J.N., P.K. Sahoo, and A.K. Wong,(1985) A new method for gray-level picture thresholding using the entropy of the histogram, Computer vision, graphics, and image processing, Vol. 29 No. 3, pp 273-285.

[21]   Glasbey, C.A.,(1993) An analysis of histogram-based thresholding algorithms“, CVGIP: Graphical models and image processing, Vol. 55 No. 6, pp 532-537.

[22]   Kittler, J. and J. Illingworth,(1986) “Minimum error thresholding”, Pattern recognition, Vol. 19 No. 1, pp 41-47.

[23]   Tsai, W.-H. Moment-preserving thresholding: A new approach. in Document image analysis. 1995. IEEE Computer Society Press.

[24]   Otsu, N.,(1979) “A threshold selection method from gray-level histograms“, IEEE Transactions on systems, man, and cybernetics, Vol. 9 No. 1, pp 62-66.

[25]   Doyle, W.,(1962) Operations useful for similarity-invariant pattern recognition“, Journal of the ACM (JACM), Vol. 9 No. 2, pp 259-267.

[26]   Shanbhag, A.G.,(1994) Utilization of information measure as a means of image thresholding“, CVGIP: Graphical Models and Image Processing, Vol. 56 No. 5, pp 414-419.

[27]   Zack, G., W. Rogers, and S. Latt,(1977) Automatic measurement of sister chromatid exchange frequency, Journal of Histochemistry & Cytochemistry, Vol. 25 No. 7, pp 741-753.


SK.Umar Faruq1, Dr.K.V.Ramanaiah2, Dr.K.Soundararajan

1Department of Electronics & Comminications, QCET Nellore, A.P, India 2Department of Electronics & Comminications, Y.S.R College, Proddutur , A.P, India 3Department of Electronics & Comminications , TKR Engineering College, Hyderabad , T.S, India


This paper addresses image enhancement system consisting of image denoising technique based on Dual Tree Complex Wavelet Transform (DT-CWT) . The proposed algorithm at the outset models the noisy remote sensing image (NRSI) statistically by aptly amalgamating the structural features and textures from it. This statistical model is decomposed using DTCWT with Tap-10 or length-10 filter banks based on Farras wavelet implementation and sub band coefficients are suitably modeled to denoise with a method which is efficiently organized by combining the clustering techniques with soft thresholding – soft-clustering technique. The clustering techniques classify the noisy and image pixels based on the neighborhood connected component analysis(CCA), connected pixel analysis and inter-pixel intensity variance (IPIV) and calculate an appropriate threshold value for noise removal. This threshold value is used with soft thresholding technique to denoise the image .Experimental results shows that that the proposed technique outperforms the conventional and state-of-the-art techniques .It is also evaluated that the denoised images using DTCWT (Dual Tree Complex Wavelet Transform) is better balance between smoothness and accuracy than the DWT.. We used the PSNR (Peak Signal to Noise Ratio) along with RMSE to assess the quality of denoised images.


Image Denoising, DTCWT, Tap-10 Filter banks, Soft-Clustering, PSNR

 For More Details:

 Volume Link:


[1]     Mukesh C. Motwani, Mukesh C. Gadiya, Rakhi C. Motwani, Frederick C. Harris, Jr, 2004. Survey of Image DenoisingTechniques. Proc. of GSPx 2004, Santa Clara Convention Center, Santa Clara, CA, pp. 27-30.

[2]     Ming Zhang and Bahadir K. Gunturk, 2008. Multiresolution Bilateral Filtering for Image Denoising. IEEE Transactions on Image Processing, Vol. 17, No. 12.

[3]     Guo.H, J. E. Odegard, M. Lang, R. A. Gopinath, I.W. Selesnick and C. S. Burrus, 1994.Wavelet based Speckle Reduction  with Application to SAR based ATD/R. First International Conference on Image Processing, 1, pp. 75-79.

[4]     Canny.J, 1986 .A computational approach to edge detection. IEEE Trans.Pattern Anal. Machine Intell., vol. PAMI-8, pp. 679–697, Nov.

[5]     Ming Zhang and Bahadir Gunturk, 2008.A New Image Denoising Method based on the Bilateral Filter.ICASSP, IEEE,pp. 929-932.

[6]     Donoho.D.L 1995.De-noising by Soft Thresholding.IEEE Trans. on Inform, Theory, 41, No. 3, pp. 613- 627.

[7]     Donoho.D..L and I. M. Johnstone, 1995.Adapting to Unknown Smoothness via Wavelet Shrinkage. Journal of the American Statistical Association, 90, No. 432, pp. 1200– 1224.

 [8]     Donoho.D..L and, I.M. Johnstone, 1994 Ideal Spatial Adaptation by Wavelet Shrinkage,” Biometrika, 81, No. 3, pp. 425–455

[9]     Chang.S.G. et al. 2000.Adaptive Wavelet Thresholding for Image Denoising and Compression. IEEE Transactions on Image Processing, 9, pp. 1532 –1546.

[10]   Chang,.S..G B. Yu, and M. Vetterli, 1998. Spatially Adaptive Wavelet Thresholding with Context Modeling for Image Denoising. Proc. ICIP, pp. 535-539.

[11]   Kingsbury..N.G ,2001.Complex wavelets for shift invariant analysis and filtering of signals. Appl. Comput. Harmon. Anal., vol. 10, no. 3, pp.234(20)–253(20), May 2001.

[12]   Van Spaendonck.R , F. Fernandes, M. Coates, and C. Burrus. 2000.Non-redundant, directionally selective, complex wavelets. In Proc. Int. Conf. Image Process. volume 2, pages 379{382, Istanbul, Turkey, Sep. 2000.

[13]   Vandergheynst.P and J.-F. Gobbers. 2002.Directional dyadic wavelet transforms: design and algorithms. IEEE Trans. Image Process., 11(4):363{372, Apr. 2002.

[14]   Selesnick. I.W 2001.Hilbert transform pairs of wavelet bases. Signal Process. Lett., 8(6):170{173,Jun. 2001

[15]   Herley.C and M. Vetterli. Wavelets and recursive filter banks.IEEE Trans. Signal Process., 41(8):2536.2556, 1993.

[16]   Kingsbury. N..G .“The dual-tree complex wavelet transform: A new technique for shift invariance and directional filters,” in Proc. IEEE DSP Workshop, Aug. 1998, Bryce, Canyon, paper no. 86.

[17]   Fernandes, F.C.A ,R. L. C. van Spaendonck, and C. S. Burrus. A new framework for complex wavelet transforms. IEEE Trans. Signal Process., 51(7):1825{1837, Jul. 2003.

[18]   Tinku Acharya and Ping-Sing Tsai,2007 .Computational Foundations of Image Interpolation Algorithms, ACM Ubiquity, Vol. 8, 2007.

[19]   Gagnon, L. 1999. Wavelet Filtering of Speckle Noise- some Numerical Results, Proceedings of the Conference Vision Interface, Trois-Reveres.


Shaik. Umar Faruq is currently working as an Associate Professor & Head in QUBA College of engineering and Technology, Nellore. He received B.E degree from Osmania University and M.Tech from JNTU in 2005 .since 2010 he has been a Ph.D student in the department of Electronics and Communications, JNTUA, Anantapur. He has 15 years of teaching experience both at UG and PG level and his research interests include Reconfigurable Architectures, Image and Video Processing

K.V. Ramanaiah is currently working as an Associate Professor & Head in Yogi Vemana  University, Kadapa. He received M.Tech degree from Jawaharlal Nehru Technological University, Hyderabad in 1998 and Ph.D degree from JNTUH in 2009. He has vast experience as academician and published number of papers in international Journals and conferences .His research interests include VLSI Architectures, Signal & Image Processing.

K Soundara Rajan received the B.Tech in Electronics & Communication Engineering from Sri Venkateswara  University. M.Tech (Instrumentation & Control) from Jawaharlal Nehru Technological University in 1972. Ph.D degree from University of Roorkee, U.P. He has published number of papers in international journals and conferences. He is a member of professional bodies like NAFEN, ISTE, IAENG etc,. He has vast experience as academician, administrator and philanthropist. He is reviewer for number of journals. His research interests include Fault Tolerant Design, Embedded Systems and signal processing.


Kimia rezaei1 and Hamed agahi2

1Corresponding author: Department of Electrical Engineering, Fars science and research branch, Islamic Azad University, Iran 2Associate professor, Department of Electrical Engineering, Shiraz branch, Islamic Azad University, Fars, Iran


In this article a method is proposed for segmentation and classification of benign and malignant tumor slices in brain Computed Tomography (CT) images. In this study image noises are removed using median and wiener filter and brain tumors are segmented using Support Vector Machine (SVM). Then a two-level discrete wavelet decomposition of tumor image is performed and the approximation at the second level is obtained to replace the original image to be used for texture analysis. Here, 17 features are extracted that 6 of them are selected using Student’s t-test. Dominant gray level run length and gray level co-occurrence texture features are used for SVM training. Malignant and benign tumors are classified using SVM with kernel width and Weighted kernel width (WSVM) and k-Nearest Neighbors (k-NN) classifier. Classification accuracy of classifiers are evaluated using 10 fold cross validation method. The segmentation results are also compared with the experienced radiologist ground truth. The experimental results show that the proposed WSVM classifier is able to achieve high classification accuracy effectiveness as measured by sensitivity and specificity.


Brain tumor, Computed tomography, Segmentation, Classification, Support vector machine.

For More Details:

 Volume Link:


[1]     Chen X.,  Nguyen B.P.,  Chui Ch., Ong S., 2016, Automated brain tumor segmentation using kernel dictionary learning and superpixel-level features, 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Budapest, 002547 – 002552

[2]     Nandpuru, H.B., Salankar, S.S., Bora, V.R., 2014,MRI Brain Cancer Classification Using Support Vector MachineIEEE. Conf.  Electrical  Electronics and Computer Science,1–6

[3]     Khaled Abd-Ellah M.,  Ismail Awad A.,  Khalaf A. M., HamedF. A.,2016, Design and implementation of a computer-aided diagnosis system for brain tumor classification, 2016 28th International Conference on Microelectronics (ICM), Cairo, Egypt, 73 – 76

[4]     Lang R., Zhao L.,  Jia K., 2016, Brain tumor image segmentation based on convolution neural network, 2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Datong, China, 1402 – 1406

[5]     Jahanavi M. S., Kurup S., 2016, A novel approach to detect brain tumor in MRI images using hybrid technique with SVM classifiers2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), Bangalore, India, 546 – 549

[6]     Kaur K., Kaur G.,  Kaur J., 2016, Detection of brain tumor using NNE approach, 2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), Bangalore, India,1864 – 1868

[7]     Kaur, T., Saini, B. S., & Gupta, S. (2016). Optimized Multi Threshold Brain Tumor Image Segmentation Using Two Dimensional Minimum Cross Entropy Based on Co-occurrence Matrix. In Medical Imaging in Clinical Applications (pp. 461-486). Springer International Publishing.

[8]     Kaur, T., Saini, B. S., & Gupta, S. (2016) A joint intensity and edge magnitude-based multilevel thresholding algorithm for the automatic segmentation of pathological MR brain images. Neural Computing and Applications, 1-24.

[9]     Verma, A. K., & Saini, B. S. ALEXANDER FRACTIONAL INTEGRAL FILTERING OF WAVELET COEFFICIENTS FOR IMAGE DENOISING. Signal & Image Processing : An International Journal (SIPIJ) Vol.6, No.3, June 2015

[10]   Chun-yu ,N.,2009, Research on removing noise in medical image based on median filter method, IT in Medicine & Education, ITIME ’09. IEEE International Symposium, Jinan,384 – 388

[11]   Benesty, J.; Jingdong Chen; Huang, Y.A.,2010, Study of the widely linear Wiener filter for noise reduction, Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference, Dallas, TX, 205- 208

[12]   El-Naqa, I., Yang, Y., Wernick, M.N., Galatsanos, N.P., Nishikawa, R.M,2002, A support vector machine approach for detection of microcalcifications, IEEE Trans. Med. Imag., 21, (12), 1552–1563

[13]   Zhengliang Huan; Yingkun Hou,2008, An Segmentation Algorithm of Texture Image Based on DWT, Natural Computation, 2008. ICNC ’08. Fourth International Conference, Jinan, 5, 433- 436

[14]   Tang, X.,1998, Texture information in run length matrices, IEEE Trans. Image Process.,7, (11), 234–243

 [15]   Khuzi, M., Besar, R., Zaki WMD, W., Ahmad, N.N.,2009, Identification of masses in digital mammogram using gray level co-occurrence matrices, Biomed. Imag. Interv. J.,5, (3), 109–119

[16]   Haralick, R.M., Shanmugam, K., Dinstein, I.,1973, Texture features for Image classification, IEEE Trans. Syst. Man Cybern.3, (6), 610–621

[17]   Levner, I., Bulitko, V., Lin, G., 2006, Feature extraction for classification of proteomic mass spectra: a comparative study, Springer-Verlag Berlin Heidelberg, Stud Fuzz, 207, 607–624

[18]   Soper D.S.: ‘P-value calculator for a student t-test (Online Software)’, 2011,

[19]   F. Latifoglu, K. Polat, S. Kara, S. Gunes,2008,  Medical diagnosis of atherosclerosis from carotid artery Doppler signals using principal component analysis (PCA), k-NN based weighting pre-processing and Artificial Immune Recognition System (AIRS), J. Biomed. Inform.41, 15–23.

[20]   Yuvaraj N., Vivekanandan P.,2013, An Efficient SVM based Tumor Classification with Symmetry Non-Negative Matrix Factorization Using Gene Expression Data , Information Communication and Embedded Systems (ICICES), 2013 International Conference, Chennai,761– 768

[21]   Liao Y.-Y., Tsui, P.-H., Yeh, C.-K.,2009, Classification of benign and malignant breast tumors by ultrasound B-scan and nakagami-based images, J. Med. Biol. Eng.30, (5), 307–312


Dr. Hamed Agahi, has obtained his doctoral degree from Tehran University, Iran. He has10 4 years of teaching experience. He is currently working as an Assistant Professor and the head of researchers and elite club in Shiraz Azad University, Iran. He has published many papers in scientific journals and conference proceedings. His research interests include pattern recognition, image processing, signal processing and machine vision and applications.

Kimia Rezaei received her Bachelor degree from Fasa Azad University,Iran, and the Master degree in telecommunications engineering from Shiraz Azad University,Ira11n. She has published one paper in national conference in Iran. She is currently working as Telecommunicatons Engineer in Sahand Telecommunication company in Iran. Her research interest is focused on pattern recognition and Image processing related research programs targeted for practical applications.


Asmaa AIT MOULAY and Aouatif AMINE

BOSS Team, Systems engineering laboratory, National school of applied sciences, University Compus, Kenitra, MOROCCO


Object tracking can be defined as the process of detecting an object of interest from a video scene and keeping track of its motion, orientation, occlusion etc. in order to extract useful information. It is indeed a challenging problem and it’s an important task. Many researchers are getting attracted in the field of computer vision, specifically the field of object tracking in video surveillance. The main purpose of this paper is to give to the reader information of the present state of the art object tracking, together with presenting steps involved in Background Subtraction and their techniques. In related literature we found three main methods of object tracking: the first method is the optical flow; the second is related to the background subtraction, which is divided into two types presented in this paper, then the temporal differencing and the SIFT method and the last one is the mean shift method. We present a novel approach to background subtraction that compare a current frame with the background model that we have set before, so we can classified each pixel of the image as a foreground or a background element, then comes the tracking step to present our object of interest, which is a person, by his centroid. The tracking step is divided into two different methods, the surface method and the K-NN method, both are explained in the paper. Our proposed method is implemented and evaluated using CAVIAR database.


Video Surveillance, Object Tracking, Feature Extraction, Background Subtraction.

For More Details:

Volume Link:


[1]     Shipra Ojha and Sachin Sakhare, Image Processing Techniques for Object Tracking in Video Surveillance- A Survey, International Conference on Pervasive Computing, Department of Computer Engineering Vishwakarma Institute of Information Technology, india, 2015.

[2]     R.Venkatesan and A.Balaji Ganesh, Real Time Implementation On Moving Object Tracking And Recognisation Using Matlab, Dept of TIFAC CORE Velammal engineering college Chennai, india, 2012.

[3]     Junzo Watada, Zalili Musa, Lakhmi C. Jain, and John Fulcher, Human Tracking: A State-of-Art Survey, Waseda University: Japan,University Malaysia Pahang: Malaysia, University of South Australia: Australia, University of Wollongong: Australia, 2010.

[4]     Patrick Dickinson and Andrew Hunter, Scene Modelling Using An Adaptive Mixture of Gaussians in Colour and Space, Department of Computing and Informatics, University of Lincoln, UK, 2005.

[5]     Alper Yilmaz, Omar Javed and Mubarak Shah, Object Tracking: A Survey, Ohio State University, University of Central Florida, USA, 2006.

[6]     Harsha Varwani, Heena Choithwani,Kajal Sahatiya, Shruti Gangan, Tina Gyanchandani and Dashrath Mane. Understanding various Techniques for Background Subtraction and Implementation of Shadow Detection., VES Institute of Technology, Chembur, 2013.

[7]     Sebastian Brutzer, Benjamin Hoferlin and Gunther Heidemann, Evaluation of Background Subtraction Techniques for Video Surveillance, Intelligent Systems Group, Universitat Stuttgart, germany, 2014.

[8]     C.Wren, A. Azabayejani, T. Darrel, and A. Pentland, P finder: Real-time tracking of the human body, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.19, pp.780-785. July 1997.

[9]     R. Cutler and L. Davis, view-based detection and analysis of periodic motion. Fourteenth International Conference on Pattern Recognition, vol.1, pp. 495-500, Brisbane, Australia, Aug 1998.

[10]   A. Elgammal, D. Harwood, and L. Davis, Non-parametric model for background subtraction in Proceedings of IEEE ICCV’99 Frame-rate workshop, Sept 1999.

[11]   C. Stauffer and W. Grimson, Learning patterns of activity using real-time trackingin IEEE Trans on Pattern Analysis and Machine Intelligence, vol. 22, pp. 747-57, Aug 2000.

[12]   P. KaewTraKulPong and R. Bowden, An improved adaptive background mixture model for real-time tracking with shadow detection in Proceedings of the 2nd European Workshop on Advanced VideoBased Surveillance Systems, Sept. 2001.

[13]   M. Harville,A framework for high-level feedback to adaptive, per-pixel, mixture-of-Gaussian background models, , in Proceedings of the Seventh European Conference on Computer Vision, Part III, pp. 543-60:Copenhagen, Denmark, May 2002.

[14]   D. R. Magee, Tracking multiple vehicles using foreground, background, and motion models,in Proceedings of the Statistical Methods in Video Processing Workshop, pp. 7-12: Copenhagen, Denmark, june 2002.

[15]   R. Cucchiara, M. Piccardi, and A. Prati Detecting moving objects, ghosts, and shadows in video streams, IEEE Transactions on Pattern Analysis and Machine Intelligence. vol.25, pp. 1337-1342, oct 2003.

[16]   T. Bouwmans, F. El Baf, and B. Vachon,Background modeling using mixture of gaussians for foreground detection-a survey, Recent Patents on Computer Science.Vol.1(Num. 3), pp. 219- 237,2008.

[17]   Sen-Ching S. Cheung and Chandrika Kamath, Robust techniques for background subtraction in urban trafic video, Center for Applied Scientific Computing: Lawrence Livermore National Laboratory, 2004.

[18]   C. Qin, H. R. Ren, C. C. Chang, Q. K. Chen, Novel Occlusion Object Removal with Inter-frame Editing and Texture Synthesis, Journal of Information Hiding and Multimedia Signal Processing, vol.7, no. 2, pp. 386-398, 2016.

[19]   Jean-Philippe Jodoin, Guillaume-Alexandre Bilodeau, Nicolas Saunier, Background subtraction based on Local Shape, Ecole Polytechnique de Montreal P.O. Box 6079, Station Centre-ville, Montreal, (Quebec), Canada, H3C 3A7, 2012

[20]   Fukunaga, Hostetler, The Estimation of the Gradient of a Density Function, with Applications in Pattern Recognition, IEEE Transactions on Information Theory vol. 21 , pp. 32-40 ,1975

[21]   Ives Rey-Otero, Mauricio Delbracio, Anatomy of the SIFT Method, Image Processing On Line, CMLA, ENS Cachan, Duke University, France, 2014

[22]   Robert T. Collins, Mean-shift Blob Tracking through Scale Space, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Carnegie Mellon University, 2003.



Ait Moulay Asmaa is a Phd student of computer engineering at ENSA of Kenitra, University of Ibn Tofail. She received her Master’s degree in information sy13stems security from the national school of applied sciences, and she is currently completing her researches on field of detecting and tracking individuals from multiple camera with the BOSS team in the LGS Laboratory, ENSA, Kenitra, Morocco.

Amine Aouatif received her Master’s degree (DESA) in Computer Sciences and 14Telecommunications engineering from the faculty of sciences, Mohammed V-Agdal University, Rabat, Morocco, in 2004. She remained at Computer Sciences and earned a PhD degree in 2009 for a dissertation titled “Feature Extraction and Selection for Dimensionality Reduction in Pattern Recognition and their Application in Face Recognition”. Her research interests include but are not limited to dimensionality reduction, feature selection applied to face detection and recognition and driver hypo-vigilance. She joined the ENSA, Kenitra, Morocco, in 2010, as an assistant professor. Since November 2010, Aouatif Amine has been a Vice-Chair of IEEE Signal Processing Society Morocco Chapter. Since April 2015, Aouatif AMINE is the BOSS team header in the LGS Laboratory, ENSA, Kenitra, Morocco.7