Applications of Digital Image Processing in Real Time World

INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 8, ISSUE 12, DECEMBER 2019 ISSN 2277-8616

Applications Of Digital Image Processing In Real Time World

B.Sridhar

Abstract :-- Digital contents are the essential kind of analyzing, information perceived and which are explained by the human brain. In our brain, one third of the cortical area is focused only to visual information processing. Digital image processing permits the expandable values of different algorithms to be given to the input section and prevent the problems of noise and distortion during the image processing. Hence it deserves more advantages than analog based image processing.

Index Terms:-- Agriculture, Biomedical imaging, Face recognition, image enhancement, Multimedia Security, Authentication

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2 REAL TIME APPLICATIONS OF IMAGE PROCESSING 1 INTRODUCTION This chapter reviews the recent advances in image Digital image processing is dependably a catching the processing techniques for various applications, which more attention field and it freely transfer the upgraded include agriculture, multimedia security, Remote sensing, multimedia data for human understanding and analyzing Computer vision, Medical applications, Biometric of image information for capacity, transmission, and verification, etc,. representation for machine perception[1]. Generally, the stages of investigation of digital image can be followed 2.1 Agriculture and the workflow statement of the digital image In the present situation, due to the huge density of processing (DIP) is displayed in Figure 1. population, gives the horrible results of demand of food, diminishments in agricultural land, environmental variation

and the political instability, the agriculture industries are trying to find the new solution for enhancing the essence of the productivity and sustainability.―In order to support and satifisfied the needs of the farmers Precision agriculture is employed [2]. It can help with upgrading the cultivating practices by utilizing data innovation instruments, which empowers farmers to listen, evaluate and control the farming practices, such as sufficient amount of fertilizers, pesticides and water usage. Figure 2 identifies the crop detection and seed identification.Precision agriculture is an advanced technology which leads to incorporate the Fig.1: Block diagram of Digital Image Processing devloping techniques to improve farm output and also enrich the farm. Image acquisition is the primary steps of digital image processing in this stage the image is experienced the Table 1:Properties of Different Agriculture Data preprocessing, such as scaling etc. The main goal of types preprocessing techniques is to enhance the quality of the image and diminish the unwanteddistortions and improve Type of Data Resolution Accuracy Crop yield High Low the quality of the input image. Feature extraction begins Topographic High Medium from an initial set of measured information and fabricates Soil Sampling Low High determined esteems planned to be instructive and non- repetitive, facilitating the subsequent learning and Artizzu et al.(3) developed a system based on computer- generalization steps, and now and again prompting better based image analysis determining proportions of crops, human interpretations. weeds and soil in the image. This system considered varying light, soil background texture and crop damage conditions including crop growth stage and size of weeds as hindrances in processing of the images.

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• B. Sridhar, Department of Electronics and Communication Engineering, MLR Institute of Technology, Hyderabad, INDIA- 500043, [email protected]

science related studies. In addition to that it also has used in defence, intelligence, commercial, planning, and humanitarian based applications. The monitoring of a parolee by an ultrasound identification system, Positron Emission Tomography (PET), X-radiation (X-ray), Magnetic Resonance Imaging (MRI)‖, and space probes are the cases of remote sensing. [5]. Different land cover types in an image can be discriminated using some image classification algorithms using spectral features, i.e. the (a) (b) brightness and "colour" information contained in each pixel. The classification can be "supervised" or" unsupervised" and the results are highlighted in figure 4. In supervised classification, the spectral features of some areas of known land cover types are extracted from the image. These areas are known as the "training areas". Every pixel in the whole image is then classified as belonging to one of the classes depending on how close its spectral features are to the spectral features of the training areas. In unsupervised (c) (d) classification, the computer program automatically groups Fig: 2 (a) Input Image, (b) Segmented image, (c) Image the pixels in the image into separate clusters, depending on after crop detection and (d) Weed identification. their spectral features. Each cluster will then be assigned a land cover type by the analyst. 2.2 Multimedia Security Development of multimedia innovation in the present era, the digital content has allowed freely to spreadout. While in wireless medium, an unapproved person may effortlessly acquire to and control the data; in this manner, the shield of information and distinguishing controls is a vital task [4]. There is no difference between the degree of excellence of an original and its copy of digital data. Image processing techniques can be utilized to identify the copyright owners, by novel algorithms. The protection against the hacking attacks on those web or available is plans, there exist distinctive data security framework for multimedia data. (a) These present security frameworks are either using encryption or steganography, or the combination of both. Figure 3 shows the process of watermarking on signals. There is diverse securable image encryption that can be especially for protection against the unauthorized access. Atransferred over the internet having important data of military, security associations, social or adaptable applications. Hence the image security is necessary. The commonly used security mechanisms are DFT, DCT, DWT, etc.The transfer of the image over the unsecured network will pose following attacks such as active and passive attacks.Active attacks: This consists of few data stream (b) modification or false data stream creation. Fig 4: (a) SPOT multispectral image of the test area and (b)

Thematic map derived from the SPOT image using an unsupervised classification algorithm.

The accuracy of the thematic map derived from remote sensing images should be verified by field observation.

Table 2: Class Vs Land cover type Fig: 3 Block diagram of Watermarking Class No. Land cover Type (Colour in Map) 1 (black) Clear water 2.3 Remote sensing Dense Forest with closed 2 (green) In remote sensing techniques the acquisition of an image is canopy performed by on-site observation without any contact with 3 (yellow) Shrubs, Less dense forest the object. Remote sensing is utilized a various fields, 4 (orange) Grass including to find the location, surveying of land and earth 5 (cyan) Bare soil, built-up areas 3355 IJSTR©2019 www.ijstr.org INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 8, ISSUE 12, DECEMBER 2019 ISSN 2277-8616

Turbid water, bare soil, built-up 6 (blue) areas Body area networks (BANs) is shown in figure 6. BSN can 7 (red) bare soil, built-up areas tackle health care monitoring and delivery challenges 8 (white) bare soil, built-up areas through wireless technology and mobile and cloud

computing using independent sensors and actuators

attached to the body. In a wireless BAN, the nodes or 2.4 Computer Vision sensors are placed on the body or on everyday clothing. Another mile stone of Image Processing is Computer Vision Several of these sensors are connected to a central Image processing, because it not like the basic processor, which transfers the data to a medical network fundamentals of compression, filtering, and enhancement. where health care professionals assess the user’s health Additionally, the investigation of PC vision made the condition. A key attribute of a BSN is that it allows medical conceivable assignments such as 3D reproduction of data to be sampled, processed, and transmitted while the scenes, movement catching, and protest acknowledgment, user is at home or on the move. Biomedical sensors are which are vital for considerably more elevated the quantity interconnected into a system to form a body area sensor of knowledge such as image and video understanding, and network (BASN). The term BASN is used when referring to motion understanding. Branches in the computer vision are telemedicine or m-health that involves mobile shown in figure 5.The relative case of these computer communication, networking, and computing. A BASN node vision frameworks includes control and detect. An acts as an interface, helping in processing and transmitting autonomous vehicle or an industrial robot is the suitable data in medical applications example for process control, and visual surveillance is for

object detection [6]. 2.6 Biometric Verification

Biometric Verification is discuss with the recognition of human characteristics are identified and control by using efficient algorithms. The main reason of employing the biometric verification algorithm is to give the assurance that the authenticated users only accessed by a legitimate user and nobody else. The flow of biometric verification as displayed in figure 7.Based on the place of application this smart biometric system is employed as verification mode or identification mode. [8].

Fig 5: Branches in Computer vision

2.5 Medical Applications Medical imaging is the process of making visual representations of the interior structure of a body for clinical investigation and medical intervention, and additionally visual representation of the function of some organs or tissues. ―Moreover, in advance investigation digital methods, such as Computed Tomography (CT) or Magnetic Resonance Imaging (MRI) are employed to identify the disease. Content Based Medical Image Retrieval (CBMIR) technology is an active research area, and it is employed to medical database systems are regularly increasing [7]‖.The purpose of medical database classification has frequently Fig 7: Process of Biometric verification defined as the delivery of the required information at the correct time. CBMIR based image retrieval has been broad 3 Conclusion range of medical specializations. Digital image processing handling manages control of digital images through a digital computer. Generally, image processing contributes on building up a computer system that is able to perform processing on an image. The digital image processing includes robust intelligent system, robots which are in automation made altogether by look into researchers in different parts of the world. It incorporates advancements in different digital image processing applications.

References Fig 6: Body Area Sensor Networks

[1] R. C. Gonzalez and R. E. Woods, ―Digital Image Processing‖, 3rd Edition, Prentice Hall of India, 2009. [2] D. J. Mulla, ―Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps‖, Biosystems Engineering, vol. 114, pp. 358–371, 2013. [3] X. P. B. Artizzu, A. Ribeiro, A. Tellaeche, G. Pajares, C. F. Quintanilla (2009), Improving weed pressure assessment using digital images from an experience-based reasoning approach, Computers and Electronics in Agriculture, 65, pp. 176–185, 2009. [4] B.Sridhar, ―Cross-layered embedding of watermark on image for high authentication‖, Pattern Recognition and Image Analysis, vol.29, no.1, pp.194–199, 2019. [5] Y. Yang, Wei, L. Nishikawa, ―Microcalcification Classification Assisted by Content Based Image Retrieval for Breast Cancer Diagnosis‖, IEEE International Conference on Image Processing, vol. 5, pp. 1-4, 2007. [6] S. K. Dewangan, ―Devnagari Handwritten Signature Recognition Using Neural Network‖, Lambert Academic Publications (LAP), ISBN: 978- 3-659-26595-2, Germany, 2012. [7] Quellec, G., Lamard, M., Cazuguel, G., Cochener, B. and Roux, C, ―Fast Wavelet-Based Image Characterization for Highly Adaptive Image Retrieval‖, Image Processing, IEEE Transactions on, pp. 1613 – 1623, 2012, [8] Shailendra Kumar Dewangan, ―Human Authentication Using Biometric Recognition‖, International Journal of Computer Science & Engineering Technology (IJCSET), ISSN: 2229- 3345, Vol. 6, No. 4, pp. 240-245, April 2015.