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  1. 1. Rashmi A Jain, Mrunalini P Moon / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue5, September- October 2012, pp.1888-1893 Image Processing And Pattern Generation Rashmi A Jain, Mrunalini P Moon G.H.Raisoni Institute Engineering & Technology for Women Nagpur (M.S.)ABSTRACT Image processing usually refers to digital technology for medical applications was inductedimage processing, but optical and analog image into the Space Foundation Space Technology Hallprocessing also are possible. This article is about of Fame in 1994.general techniques that apply to all of them. Theacquisition of images (producing the input image INTRODUCTIONin the first place) is referred to as imaging. In recent years there has been much interestClosely related to image processing are computer in using large scale homogeneous cellular array ofgraphics and computer vision. In computer simple circuits to perform image processing tasksgraphics, images are manually made from and to demonstrate inserting pattern formingphysical models of objects, environments, and phenomena .The cellular neural network (CNN) firstlighting, instead of being acquired (via imaging introduced.devices such as cameras) from natural scenes, as 1. As an implementable alternative to fullyin most animated movies. computer vision, on the connected neural networks has evolved intoother hand, is often considered high-level image a paradigm for these types of arrays.processing out of which a 2. The motivation for studying the usefulnessmachine/computer/software intends to decipher of such arrays has included the biology ofthe physical contents of an image or a sequence the retina, the system of morphogenesisof images (e.g., videos or 3d full-body magnetic and cellular automata. Example: Siliconresonance scans).in modern sciences and retinatechnologies, images also gain much broader 3. The general resistive grids, which havescopes due to the ever growing importance of been able to produce many effects of linearscientific visualization (of often large-scale and non linear spatial filtering and motioncomplex scientific/experimental data). Examples sensitive filtering.include microarray data in genetic research, or 4. The system motivated by the Reactionreal-time multi-asset portfolio trading in finance. Diffusion Equation can produce effects in finger print enhancement, textile faultHISTORY OF IMAGE PROCESSING: detection, pattern formation and synergetic. Many of the techniques of digital image 5. The cellular automata based methodsprocessing, or digital picture processing as it often which can be used for mathematicalwas called, were developed in the 1960s at the Jet morphology.Propulsion Laboratory, Massachusetts Institute of 6. Modeling physical system such as ISMGTechnology, Bell Laboratories, University of spin glass.Maryland, and a few other research facilities, withapplication to satellite imagery, wire-photostandards conversion, medical imaging, videophone,character recognition, and photograph enhancement.The cost of processing was fairly high, however,with the computing equipment of that era. Thatchanged in the 1970s, when digital imageprocessing proliferated as cheaper computers anddedicated hardware became available. Images thencould be processed in real time, for some dedicatedproblems such as television standards conversion.As general-purpose computers became faster, theystarted to take over the role of dedicated hardwarefor all but the most specialized and computer- The simplest CNN cell has a singleintensive operations. With the fast computers and capacitor, giving it first order dynamic and it issignal processors available in the 2000s, digital coupled in neighboring cells through non linearimage processing has become the most common controlled sources. The dynamic of the CNN isform of image processing and generally, is used described bybecause it is not only the most versatile method, butalso the cheapest. Digital image processing 1888 | P a g e
  2. 2. Rashmi A Jain, Mrunalini P Moon / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue5, September- October 2012, pp.1888-1893 has a long history in morphogenesis literature.With the output nonlinearityAs shown in fig.1. The emphasis on a template can be easilyunderstood by writing (1) in block diagram form, asshown in fig.2. We will make use of discrete space Fourier Note that the correlation sums in the transform (DSFT), which gives the representation ofequation can be written as convolutions (denoted by sequence I the basis of complex exponentials.an asterisk) by template reflection. From thediagram it can be seen that the B template forms asimple feed forward finite impulse response(FIR)filtered version of the input which itself can beconsidered as static input to the rest of the system.On the other hand , A template’s operating in afeedback loop along with a nonlinearity a featurethat gives interesting behavior.THE ESSENTIAL BACKGROUND – THECNN CENTRAL LINEAR SYSTEM Because the output nonlinearly applied tothe state (see fig.1.) is piecewise linear the statespace can be considered to be the union of regionwhere the dynamics are exactly linear? The mostimportant region for this purpose is called centralstate space where all cells have states IN this section we will see use ofspatial frequency domain to write the time solvingof the network.SPATIAL CONVOLUTION TIME SOLUTION:FORMULATION: The discussion on boundary conditions In particular, the state evolution is written showed that we actually only need to study onein terms of the convolution of two dimensional differential equation to understand the behavior ofinfinite spatial sequences. any of these systems, The time solution can be written down for each w1,w2 from elementarySPATIALFREQUENCY FOMULATOIN: linear system theory as follows: Because of the spatial mixing introducedby the coupling, writing the time solution of aboveequation would not be very intuitive. However, it isstraight forward to transform the system by a changeof basis into one that is uncoupled, an approach that 1889 | P a g e
  3. 3. Rashmi A Jain, Mrunalini P Moon / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue5, September- October 2012, pp.1888-1893 2. Time varying spatial filtering 3. Design Issue 4. Saturated Behavior UNSTABLE SATURATED BEHAVIOR: 3.1. Autonomous behavior 3.1.1. Linear Pre filtering 3.1.2. Morphological constraint 3.2. Non Autonomous Behavior Methods of Image Processing There are two methods available in Image( Processing. Analog Image ProcessingSTABLE CENTRAL LINEAR SYSTEM Analog Image Processing refers to the alteration ofTo help develop an intuitive notion for behavior of image through electrical means. The most common(7), we will make separate studies of stable and example is the television image.unstable operation. It will be shown that this The television signal is a voltage level which variesdivision leads to qualitatively different image in amplitude to represent brightness through theprocessing and pattern formation behavior. image. By electrically varying the signal, the 1. Equilibrium linear spatial Filtering displayed image appearance is altered. The Example (i). Low pass filter design. brightness and contrast controls on a TV set serve 1890 | P a g e
  4. 4. to adjust the amplitude and reference of the videosignal, resulting in the brightening, darkening andalteration of the brightness range of the displayedimage.Digital Image Processing In this case, digital computers are used toprocess the image. The image will be converted todigital form using a scanner – digitizer [6] (asshown in Figure 1) and then process it. It is definedas the subjecting numerical representations ofobjects to a series of operations in order to obtain adesired result. It starts with one image andproduces a modified version of the same. It istherefore a process that takes an image intoanother. The term digital image processinggenerally refers to processing of a two-dimensionalpicture by a digital computer [7,11]. In a broadercontext, it implies digital processing of any two-dimensional data. A digital image is an array ofreal numbers represented by a finite number of bits.Image Enhancement Techniques Sometimes images obtained from satellitesand conventional and digital cameras lack incontrast and brightness because of the limitations 2. X-Ray Imagingof imaging sub systems and illumination conditions X-rays are among the oldest sources ofwhile capturing image. Images may have different EM radiation used for imaging. The best knowntypes of noise. In image enhancement, the goal is to use of X-rays is medical diagnostics, but they alsoaccentuate certain image features for subsequent are used extensively in industry and other areas,analysis or for image display [1, 2]. Examples like astronomy. X-rays for medical and industrialinclude contrast and edge enhancement, pseudo- imaging are generated using an X-ray tube, whichcoloring, noise filtering, sharpening, and is a vacuum tube with a cathode and anode. Themagnifying. Image enhancement is useful in cathode is heated, causing free electrons to befeature extraction, image analysis and an image released. These electrons flow at high speed to thedisplay. The enhancement process itself does not positively charged anode.increase the inherent information content in the When the electrons strike a nucleus,data. It simply emphasizes certain specified image energy is released in the form of X-ray radiation. The energy (penetrating power) of X-rays is characteristics. Enhancement algorithms controlled by a voltage applied across the anode,are generally interactive and application-dependent. and by a current applied to the filament in theSome of the enhancement techniques are: cathode. Figure 1.7(a) shows a familiar chest X-ray • Noise Filtering generated simply by placing the patient between an • Histogram modification X-ray source and a film sensitive to X-ray energy. • Contrast StretchingFIELDS THAT USE IMAGEPROCESSING:1. Gamma-Ray Imaging Major uses of imaging based on gammarays include nuclear medicine and astronomicalobservations. In nuclear medicine, the approach isto inject a patient with a radioactive isotope thatemits gamma rays as it decays. Images areproduced from the emissions collected by gammaray detectors. Figure 1.6(a) shows an image of acomplete bone scan obtained by using gamma-rayimaging. Images of this sort are used to locate sitesof bone pathology, such as infections 1891 | P a g e
  5. 5. Some of other applications are briefly discussed [2] Bezdek, J. C. et al. [2005]. Fuzzy Modelsbelow. and Algorithms for Pattern Recognition 1. Imaging in the Ultraviolet Band and 2. Imaging in the Visible and Infrared [3] Image Processing, Springer, New York. Bands 3. Imaging in the Microwave Band [4] Davies, E. R. [2005]. Machine Vision: 4. Imaging in the Radio Band Theory, Algorithms, Practicalities, 5. Remote Sensing Morgan Kaufmann, San Francisco, CA. 6. Medical Imaging [5] Rangayyan, R. M. [2005]. Biomedical 7. Non-destructive Evaluation Image Analysis, CRC Process Boca 8. Forensic Studies Raton, FL. 9. Textiles [6] Mariana S. C. Almeida, Luis B. Almeida, 10. Material Science. "Wavelet-based separation of nonlinear 11. Military show-through and bleed-through image 12. Film industry mixtures," Neurocomputing, Vol. 72, No. 13. Document processing 1-3, pp. 57-70, 2008. 14. Graphic arts [7] Francesco Isgrò, Maurizio Pilu, "A fast 15. Printing Industry and robust image registration method based on an early consensus paradigm,"ADVANTAGES: Pattern Recognition Letters, Vol. 25, No. 1. No scaling – no associated resampling 8, pp. 943-954, 2004. degradations. [8] J. Sauvola, M. Pietikäinen, "Adaptive 2. Shear can be implemented very efficiently document image binarization," Pattern Recognition, Vol. 33, No. 2, pp. 225-236,FUTURE SCOPE: 2000.Adaptive interrogation of large image data bases- [9] John Mashford, Mike Rahilly, Paul Davis,These parameters can be divided into several Stewart Burn, "A morphological approachcategories as follows: to pipe image interpretation based on segmentation by support vector machine,"Engineering, Spacecraft and Automation in Construction, Vol. 19, No.Footprint Data 7, pp. 875-883, 2010.Examples: [10] Yudong Zhang, Lenan Wu, Shuihua Spacecraft Position and Orientation Wang, "A hybrid recognition method forCamera Filter Position Solar Elevation Angle document images,"in 12th IASTEDImage Footprint (Longitude and Latitude) Location International Conference on Intelligentof Digital. Systems and Control, ISC 2009, November 2, 2009 - November 4, 2009,Film Versions Cambridge, MA, United states, 2009, pp.Examples: 62-66.Tape and .File Number of Raw Image Roll and [11] Maya R. Gupta, Nathaniel P. Jacobson,Frame Number for Film Products Microfiche Eric K. Garcia, "OCR binarization andCard Position Dataset Name on Disk image preprocessing for searching historical documents," PatternII. UTILIZATION OF GRAPHICS DATA WITH Recognition, Vol. 40, No. 2, pp. 389-397,IMAGERY 2007.III. DISPLAY OF MULTISPECTRAL IMAGERY [12] F. v Wegner, M. Both, R. H. A. Fink, "Automated Detection of ElementaryCONCLUSION: Calcium Release Events Using the À In this paper we have discussed Trous Wavelet Transform," Biophysicalmathematical explanation and methods of image Journal, Vol. 90, No. 6, pp. 21512163,processing. We have seen traditional classification 2006.of image processing and how patterns are generated [12] Daisuke Deguchi, Kensaku Mori,in this scheme. Image processing has lots Yasuhito Suenaga, Jun-ichi Hasegawa,application in different fields. So we can say that Jun-ichiro Teriyaki, Hiroshi Natori,this method is very useful for recent scenario. Hirotsugu Takabatake, "New calculation method of image similarity for endoscopeREFERENCES tracking based on image registration in [1] Prince, J. L. and Links, J. M. [2006]. endoscope navigation," International Medical Imaging, Signals, and Systems, Congress Series, Vol. 1256, No. pp. 460- Prentice Hall, Upper Saddle River, NJ. 466, 2003. 1892 | P a g e
  6. 6. [13] Yasushi Ogasawara, Kinya Katayama, Algorithm," Journal of Convergence Atsushi Minami, Miyuki Otsuka, Tadashi Information Technology, Vol. 6, No. 1, Eguchi, Katsumi Kakinuma, "Cloning, pp. 108-115, 2011. Sequencing, and Functional Analysis of [25] Wencheng Wang, Faliang Chang, Tao Ji, the Biosynthetic Gene Cluster of Guoqiang Zhang, "Fusion of Multi-focus Macrolactam Antibiotic Vicenistatin in Images Based on the 2-Generation Streptomyces halstedii," Chemistry & Curvelet Transform," JDCTA: Biology, Vol. 11, No. 1, pp. 79-86, 2004. International Journal of Digital Content[14] Pierrick Coupé, José V. Manjón, Elias Technology and its Applications, Vol. 5, Gedamu, Douglas Arnold, Montserrat No. 1, pp. 32-42,2011. Robles, D. Louis Collins, "Robust Rician noise estimation for MR images," Medical Image Analysis, Vol. 14, No. 4, pp. 483- 493, 2010.[15] Dugong Zhang, Lenan Wu, "Improved Image Filter based on SPCNN," Science in China Eedition: Information Science, Vol. 51, No. 12, pp. 2115-2125, 2008.[16] Julie Noriega Rivera Rio, Diego Fabian Lozano-Garcia, "Spatial Filtering of Radar Data (RADARSAT) for Wetlands (Brackish Marshes) Classification," Remote Sensing of Environment, Vol. 73, No. 2, pp. 143-151, 2000.[17] Isabelle Bloch, "Duality vs. adjunction for fuzzy mathematical morphology and general form of fuzzy erosions and dilations," Fuzzy Sets and Systems, Vol. 160, No. 13, pp. 1858-1867, 2009.[18] A. Bourquard, F. Aguet, M. Unser, "Optical imaging using binary sensors," Opt Express, Vol. 18,No. 5, pp. 4876-88, Mar 1 2010.[19] Dan S. Bloomberg, "Implementation Efficiency of Binary Morphology," in International Symposium for Mathematical Morphology VI, Sydney, Australia, 2002.[20] Yudong Zhang, Shuihua Wang, Yuankai Huo, Lenan Wu, "Feature Extraction of Brain MRI by Stationary Wavelet Transform and its Applications," Journal of Biological Systems, Vol. 18, No.s, pp. 115-132, 2010.[21] Yudong Zhang, Lenan Wu, Nabil Naggaz, Shuihua Wang, Geng Wei, "Remote- sensing Image Classification Based on an Improved Probabilistic Neural Network," Sensors, Vol. 9, No. 9, pp. 7516-7539, 2009.[22] Yudong Zhang, Shuihua Wang, Lenan Wu, Yuankai Huo, "Artificial Immune System for Protein folding model," Journal of Convergence Information Technology, Vol. 6, No. 1, pp. 55-61, 2011.[24] Yuankai Huo, Yudong Zhang, Shuihua Wang, Lenan Wu, "Diffusion Tensor Image Registration Based on Demon- Affine Method and FSGA-BFGS 1893 | P a g e