IMAGE PROCESSING SYSTEM USING JAVA PDF

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Internationa onal Journal of Innovative Research ch in Information Security ( IJIRIS)) ISSN: 2349-7017 Issue 04, Vol olume 7 ( April 2020) www.ijir is.com IMAG AGE PROCESSING ING SYSTEM S USING JAVA JA Utkarshsh Gupta Depar tment of Inforor mmation Technology Meer ut Institute of Engineer ing g and ...

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Internationa onal Journal of Innovative Research ch in Information Security ( IJIRIS)) Issue 04, Vol olume 7 ( April 2020)

ISSN: 2349-7017 www.ijir is.com

IMAG AGE PROCESSING ING SYSTEM S USING JAVA JA Meer ut

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Utkarsh sh Gupta Depar tment of Infor or m mation Technology Institute of Engineer ing g and a Technology, Meer ut, INDIA utkar sh.gupta.it.2 [email protected] Sudhanshu shu Kumar Depar tment of Infor or m mation Technology Institute of Engineer ing g and a Technology, Meer ut, INDIA sudhanshu.kumarr [email protected] .it Devansh sh S Singhal Depar tment of Infor or m mation Technology Institute of Engineer ing g and a Technology, Meer ut, INDIA devansh.singhal.it. [email protected] Parth Toma Tomar Depar tment of Infor or m mation Technology Institute of Engineer ing ga and Technology, Meer ut, INDIA par th.tomar .it.20 [email protected] Ajay Ku Kumar Depar tment of Infor or m mation Technology Institute of Engineer ing g and a Technology, Meer ut, INDIA ajay.kumarr @miet.ac.in @

Manuscript History Number : IJIRIS/ RS/ Vol ol.07/ Issue04/ APIS10084 Received: 03, Apr il 2020 20 Final Cor r ection: 14, Apr pr il 2020 Final Accepted: 22, Aprr il i 2020 Published: April 2020 Citation: Utkar sh, Sudh dhanshu, Devansh, Par th & Ajay (20 2020). Image Pr ocessing System Usi sing Java. Inter national Jour nal of Innovative Resear Re ch in Infor mation Secur it y, Volu olume VII,42-46-doi:/ / 10.26562/ I RJCS.2020.APIS10084 R Editor: Dr .A.Ar ul L.S, Chief Ch Editor , IJIRIS, AM Publications, s, IIndia Copyr ight: © 2020 This is is i an open access ar ticle distr ibuted ed u under the ter ms of the Cr eative Comm mons Attr ibution License, Which Per mits unr estr icted use, distr ibution, and r epr oduction ion in any medium, pr ovided the or iginal nal author and sour ce ar e cr edited

Abstract – The ar ticle e is i all about the Image Pr ocessing Sy System that can be defined as, pr ocessing oce and alter ing an existing image in the desir de ed manner . Image is one of the he per ceptible sour ces in application ons of Image Pr ocessing including a lar ge numbe ber of tools and techniques w hich he help to extr act complex featur es off an a image. Pr obably the most pow er ful image pr ocessing system is the human brr ain ai together w ith the eye. The syste stem r eceives, enhances, and stor es images att enor e mous r ates of speed. The ob objective of Image Pr ocessing is s to visually enhance or statistically evaluate some som aspect of an image not r eadily a appar ent in its or iginal for m. Severr al a technologies playing on images in r eal-time e but image pr ocessing is the r eall cor co e. This paper discusses the over er view of development; implementation of ope per ations r equir ed for quality ima mage pr oduction and also discusse sses image pr ocessi ng applications, tools, and d techniques. t Keywords – Aspect Ratio; Ra Br ightness Tr ansfor mation; Color Model; Digital Image; Edge ge Map; image system; pr ocessing ; java securr ity; it I. INTROD DUCTION Digital image pr ocessin ssing is a ver y popular and r apidly gr ow ing ar ea of application underr ccomputer science and Infor mation Technology gy engineer ing. Its gr ow th leads by y technological t innovations in the fields fie of digital imaging, computer pr ocessing and an mass stor age devices. Fields w hich hi have been tr aditionally using an analog imaging ar e now sw itching to digital syst stems, for their edibility and affor dab ability. Some of the impor tant examp mples ar e medicine, and video pr oduction, pho hotogr aphy, r emote sensing, and d secur ity monitor ing. A digitall image is a numer ic r epr esentation of a two wo-dimensional image and may be ed defined as a function, f (x, y), w her e x and y ar e special coor dinates and f is the he amplitude commonly called as gr ey - level at the pair of coor dinate ates (x, y). Digital image pr ocessing deals w ith manipulation m of digital images thr oug ugh a digital computer . Image pr ocessi essing is the application of signal pr ocessing tech chniques to the domain of Images — tw o-dimensional signals such as s photogr ph aphs or video.

---------------------------------------------------------------------------------------------------------- ----------------------------------------------IJIRIS: Me endeley ( Elsevier Indexed) CiteFa Factor Journal Citations Impact Fact actor 1.23 Impact Factor Value Va – SJIF: Innospace, Morocco occo ( 2016) 2 : 4.651| I ndexcopernicus: s: ( IC ICV 2016) : 88.20 © 2014- 20, IJI RIS- All ll Rights R Reserved Page -42

International Journal of Innovative Research in Information Security ( IJIRIS) Issue 04, Volume 7 ( April 2020)

ISSN: 2349-7017 www.ijir is.com

Image pr ocessing does typically involve filter ing or enhancing an image using var ious types of functions i n addition to other techniques to extr act infor mation fr om the images. The most common example is Adobe Photoshop. It is one of the w idely used applications for pr ocessing digital images. It also means "Analysing and manipulating images w ith a computer ". The goal of Image Pr ocessing System can be divided into 3 categor ies. [1] Image pr ocessing in w hich input is an image and output is also an image [2] Image analysis in w hich input is an image and outputs ar e the dimensions or measur ements. [3] Image under standing in w hich input is an image and output is the standar d descr iption of an image. Image An image is an ar r ay, or a matr ix of squar e pixels ar r anged in columns and r ow s. In a (8-bit) gr eyscale image intensity r anges fr om 0 to 255. A gr ey scale image nor mally know n as black and w hite image, but the name emphasizes that such an image w ill also include many shades of gr ey. A nor mal gr eyscale image has 8-bit color depth = 256 gr eyscales. A “tr ue color ” image has 24-bit color depth = 8 x 8 x 8 bits = 256 x 256 x 256 color s = ~ 16million color s. Digital image pr ocessing is a ver y popular and r apidly gr ow ing ar ea of application under computer science engineer ing. Its gr ow th leads by technological innovations in the fields of digital imaging, computer pr ocessing and mass stor age devices. Fields w hich have been tr aditionally using analog imaging ar e now sw itching to digital systems, for their edibility and affor dability. Impor tant examples ar e medicine, and video pr oduction, photogr aphy, r emote sensin g, and secur ity monitor ing Ther e ar e sever al image file for mats w hich ar e commonly used in daily life. A. GIF: An 8-bit (256 color ), non-destr uctively compr essed bitmap for mat. mostly used for w eb. B. JPEG: A ver y efficient (i.e. much infor mation per byte) destr uctively compr essed 24-bitmap for mat. Widely used, especially for w eb and Inter net (bandw idth-limited). C. TIFF: The standar d 24 bit publication bitmap for mat. Compr esses none destr uctively w ith, for instance, LempelZiv-Welch (LZW) compr ession. D. PS: Postscr ipt, a standar d vector for mat. Has numer ous sub-standar ds and can be difficult to tr anspor t acr oss platfor ms and oper ating systems. E. PSD: A dedicated Photoshop for mat that keeps all the infor mation in an image including all the layer s. For science communication, the tw o main colour spaces ar e RGB and CMYK.

RGB: RGB uses additive colour mixing and is the basic colour model used in television or any other medium that pr ojects colour w ith light. It is the basic colour model used in computer s and for w eb gr aphics. The secondar y colour s of RGB – cyan, magenta, and yellow – ar e for med by mixing tw o of the pr imar y colour s (r ed, gr een or blue) and excluding the thir d colour . Red and gr een combine to make yellow , gr een and blue to make cyan, and bl ue and r ed for m magenta. The combination of r ed, gr een, and blue in full intensity makes w hite. CMYK: The 4-colour CMYK model used in pr inting lays dow n over lapping layer s of var ying per centages of tr anspar ent cyan (C), magenta (M) and yellow (Y) inks. In addition, a layer of black (K) ink can be added. The CMYK model uses the subtr active colour model. The colour s cr eated by the subtr active model of CMYK don't look exactly like the colour s cr eated in the additive model of RGB Most impor tantly; CMYK cannot r epr oduce the br ightness of RGB colour s. In addition, the CMYK gamut is much smaller than the RGB gamut. Gamut: The r ange, or gamut, of human colour per ception is quite lar ge. The tw o-colour spaces discussed her e span only a fr action of the colour s w e can see. Fur ther mor e, the tw o spaces do not have the same gamut, m eaning that conver ting fr om one colour space to the other may cause pr oblems for colour s in the outer r egions of the gamut’s. II. TECHNIQUES Image Pr ocessing System techniques ar e as follow s: A. Image Enhancement: Image enhancement is the method for pr oviding the r esults of image to be clear er , by impr oving fr om or iginal images so that the r esults ar e mor e suitable for display or fur ther image analysis. It helps in r emoving noise, shar pening the image, or br ightens an image, making it easy to identify key featur es. The pr ocess of enhancing the quality of images fr om the or iginal image by r emoving the noise, pr ovide the enhanced image by shar pening the or iginal image and incr easing contr ast in image. B. Image Restoration: Restor ing the clear image fr om the degr aded or cor r upted image is pr ovided by the technique called image r estor ation. ---------------------------------------------------------------------------------------------------- --------------------------------------------IJIRIS: Mendeley ( Elsevier Indexed) CiteFactor Journal Citations Impact Factor 1.23 Impact Factor Value – SJIF: Innospace, Morocco ( 2016) : 4.651| I ndexcopernicus: ( ICV 2016) : 88.20 © 2014- 20, IJI RIS- All Rights Reserved Page -43

International Journal of Innovative Research in Information Security ( IJIRIS) Issue 04, Volume 7 ( April 2020)

ISSN: 2349-7017 www.ijir is.com

Cor r upted/ Blur images ar e due to noisy, blur images or camer a misfocus. Blur r ing occur s due to for mation of bandw idth r eduction of an ideal image caused by imper fect image for mation pr ocess. Thus the images w ill be r estor ed into or iginal quality by r educing the physical degr adation.

C.

Image Compression: Image compr ession is minimizing the size of bytes of a image file w ithout degr ading the quality of the image in or der to obtain the image in mor e clar ity. The r eduction in file size allow s mor e images to be stor ed in a given amount of disk or memor y space. And also r educes the time dur ing sending of images via netw or ks or dow nloading fr om w eb pages.

D. Image Segmentation: Segmenting or par titioning the or iginal image w ith some defined pixels into number of r egions for the pur pose of image analysis, depicts the featur es hidden in the nor mal image and object r ecognition, undefined boundar y estimation, textur es and motions. It is based on r egion and edges of image, segmentation is car r ied out. E.

Image Recognition: Image r ecognition technique involves in r ecognizing/ identifying and detecting featur es such as objects in video or images. Dur ing the r ecognition mechanism, images fr om the database ar e compar ed w ith the cur r ent image, if the match is found then fur ther execution of pr ocess w ill be car r ied out in r eal time application. III. APPLICATIONS IN IMAGE PROCESSING SYSTEM

Since digital image pr ocessing has w idely applied in many applications and almost all of the technical fields ar e impacted. Digital Image pr ocessing is not just limited to adjust the spatial r esolution of the ever y day images captur ed by the camer a. It is not just limited to incr ease the br ightness of the photo. Electr omagnetic w aves can be thought of as str eam of par ticles, w her e each par ticle is moving w ith the speed of light. Each par ticle contains a bundle of ener gy. This bundle of ener gy is called a photon. In this electr omagnetic spectr um, w e ar e only able to see the visible spectr um. Vi sible spectr um mainly includes seven differ ent color that is ter med as (VIBGOYR). VIBGOYR stands for violet, indigo, blue, gr een, or ange, yellow and Red. But that does not nullify the existence of other stuff in the spectr um. Our human eye can only see the visible por tion, in w hich w e saw all the objects. But a camer a can see the other things that a naked eye is unable to see. For example: x r ays, gamma r ays, etc. Hence the analysis of all that stuff too is done in digital image pr ocessing. Some of the major Application fields in w hich digital image pr ocessing is w idely used ar e mentioned below

A. Image sharpening and restoration: Image shar pening and r estor ation r efer s to pr ocess images that have been captur ed fr om the moder n camer a to make them a better image or to manipulate those images in w ay to achieve desir ed r esult. It r efer s to do w hat Photoshop usually does. This includes Zooming, blur r ing, shar pening, gr ay scale to color conver sion, detecting edges and vice ver sa, Image r etr ieval and Image r ecognition. B. Medical field: The common applications of DIP in the field of medical is, gamma r ay imaging, PET scan Ray Imaging, Medical CT, UV imaging. DNA analysis, finger pr int and facial r ecognition ar e evident applications of image pr ocessing. C.

UV Rays: In the field of r emote sensing, the ar ea of the ear th is scanned by a satellite or fr om a ver y high gr ound and analysed to obtain infor mation about it. One par ticular application of digital image pr ocessing in the field of r emote sensing is to detect infr astr uctur e damages caused by an ear thquake. Even if it is ver y hectic and time consuming pr ocedur e and found a solution in digital image pr ocessing. An image of the affected ar ea is captur ed fr om the above gr ound and analyzed to detect the var ious types of damage done by the ear thquake.

D. Transmission and encoding: The ver y fir st image that has been tr ansmitted over the w ir e w as fr om London to New Yor k via a submar ine cable. The pictur e took thr ee hour s to r each fr om one place to another . Now able to see live video feed, or live CCTV footage fr om one continent to another w ith just a delay of seconds. It means that a lot of w or k has been done in this field too. This field does not only focus on tr ansmission, but also on encoding. E.

Robot vision: One of the biggest challenges still is to incr ease the vision of the r obot. Developed a r obot able to see things, identify them and identify the hur dles etc. Much w or k has been contr ibuted by this field and still developing.

F.

Video processing: A video is the ver y fast movement of pictur es. The quality of the video depends on the number of fr ames/ pictur es per minute and the quality of each fr ame being used. Video pr ocessing involves noise r eduction, detail enhancement, motion detection, fr ame r ate conver sion, aspect r atio conver sion, and color space conver sion

---------------------------------------------------------------------------------------------------- --------------------------------------------IJIRIS: Mendeley ( Elsevier Indexed) CiteFactor Journal Citations Impact Factor 1.23 Impact Factor Value – SJIF: Innospace, Morocco ( 2016) : 4.651| I ndexcopernicus: ( ICV 2016) : 88.20 © 2014- 20, IJI RIS- All Rights Reserved Page -44

International Journal of Innovative Research in Information Security ( IJIRIS) Issue 04, Volume 7 ( April 2020)

ISSN: 2349-7017 www.ijir is.com

G. Video processing: A video is the ver y fast movement of pictur es. The quality of the video depends on the number of fr ames/ pictur es per minute and the quality of each fr ame being used. Video pr ocessing involves noise r eduction, detail enhancement, motion detection, fr ame r ate conver sion, aspect H. Pattern recognition : Patter n r ecognition involves study on image pr ocessing and fr om var ious fields that includes machine lear ning. In patter n r ecognition, image pr ocessing is used for identifying the objects in images and then machine learning is used to tr ain the system for the change in patter n. Patter n r ecognition is u sed in computer aided diagnosis, r ecognition of handw r iting and r ecognition of images. IV. SCOPE OF FUTURE APPLICATION The futur e of image pr ocessing w ill involve scanning the heavens for other intelligent life out in space. Also new intelligent, digital species cr eated entir ely by r esear ch scientists in var ious nations of the w or ld w ill include advances in image pr ocessing applications. Due to advances in image pr ocessing and r elated technologies ther e w ill be millions and millions of r obots in the w or ld in a few decades time, tr ansfor ming the w ay the w or l d is managed. Advances in image pr ocessing and ar tificial intelligence6 w ill involve spoken commands, anticipating the infor mation r equir ements of gover nments, tr anslating languages, r ecognizing and tr acking people and things, diagnosing medical conditions, per for ming sur ger y, r epr ogr amming defects in human DNA, and automatic dr iving all for ms of tr anspor t. With incr easing pow er and sophistication of moder n computing, the concept of computation can go beyond the pr esent limits and in futur e, image pr ocessing technology w ill advance and the visual system of man can be r eplicated. The futur e tr end in r emot e sensing w ill be tow ar ds impr oved sensor s that r ecor d the same scene in many spectr al channels. Gr aphics data is becoming incr easingly impor tant in image pr ocessin g app1ications. The future image pr ocessing applications of satellite based imaging r anges fr om planetar y explor ation to sur veillance applications. Using lar ge scale homogeneous cellular ar r ays of simple cir cuits to per for m image pr ocessing tasks and to demonstr ate patter n-for ming phenomena is an emer ging topic. The cellular neur al netw or k is an implementable alter native to fully connected neur al netw or ks and has evolved into a par adigm for futur e imaging techniques. The usefulness of this technique has applications in the ar eas of silicon r etina, patter n for mation, etc. One of the basic image pr ocessing functions of some image pr ocessing softw ar e is the “object counter ,” w hich is based on the Blob analysis. The Blob (Binar y Lar ge Object) algor ithm is used for detecting the par ameter of single obj ects inside an image. In plain ter ms, a Blob is an ar ea of a digital image, in w hich some char acter istics such as br ightness or color ar e constant and differ fr om the backgr ound. In figur e 5 for example ever y chocolate dr op has an or i ginal Blob, w hich is silhouetted against the backgr ound w ith its gr ay and color values. The Blob-analysis now allow s the separ ation of the r elevant objects fr om the backgr ound (so called binar ization) and then classifying the objects due to their size, geometr y, position and or ientation. In the ear ly days on the field of the Blob-analysis, it w as used to maintain an image r egion (r egion of inter est) for fur ther pr ocessing. Those image r egions could signal the pr esence of an object or par ts of the object in an image, w ith the set task to r ecognize or tr ace objects. One of the fir st and also most popular Blob-analysis is based on the Laplacian of the Gaussian (LoG), w hich is a special for m of the discr ete Laplace-Filter s and is used for detecting edges. In new er w or ks the Blob descr iptor s ar e mor e and mor e used as inter est -oper ator s. These algor ithms extr act distinctive ar eas in images and deliver at the same time one or mor e par ameter s. Distinctive ar eas ar e points, w hich ar e in a bor der ed sur r ounding that is as unique as possible. Today the Blob-analysis can be used in many applications w ith time consuming calculations. Ther eby it can exclude connected r egions based on...