This document discusses various applications of digital image processing including:
1) Medical applications such as cancer detection, dermatology, and lung imaging.
2) Remote sensing applications like detecting earthquake damage and monitoring crop traits.
3) Robot vision to allow robots to see and identify objects.
4) Pattern recognition applications including character recognition, signature verification, and biometrics.
5) The document also discusses methodologies of digital image processing including image acquisition, preprocessing, segmentation, and output.
Digital image processing is the use of computer algorithms to perform image processing on digital images. As a subcategory or field of digital signal processing, digital image processing has many advantages over analog image processing.
During past few years, brain tumor segmentation in CT has become an emergent research area in the field of medical imaging system. Brain tumor detection helps in finding the exact size and location of tumor. An efficient algorithm is proposed in this project for tumor detection based on segmentation and morphological operators. Firstly quality of scanned image is enhanced and then morphological operators are applied to detect the tumor in the scanned image. The problem with biopsy is that the patient has to be hospitalized and also the results (around 15%) give false negative. Scan images are read by radiologist but it's a subjective analysis which requires more experience. In the proposed work we segment the renal region and then classify the tumors as benign or malignant by using ANFIS, which is a non-invasive automated process. This approach reduces the waiting time of the patient.
Pre-Processing Image Algorithm for Fingerprint Recognition and its Implementa...ijseajournal
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Fingerprint recognition technology is becoming increasingly popular and widely used for many applications that require a high level of security. We can meet several types of sensors integrated in the fingerprint recognition system as well as several types of image processing algorithm in order to ensure
reliable and fast authentication of people. Embedded systems have a wide variety and the choice of a welldesigned
processor is one of the most important factors that directly affect the overall performance of the system. This paper introduces a preliminary treatment to the image in order to improve the quality, and then present a hardware implementation.
Till now many algorithms are published for fingerprint recognition and these algorithms has different accuracy rate. This paper consists of information of about fingerprint (biometrics) recognition. The novel algorithm is considered for thinning process. Whole process of recognition is explained from image capturing to verification. The image captured is first converted to gray scale then image enrichment is done then thinning process take over charge which is main process then last process which is also equally important as thinning process is feature extraction which extracts ridge ending, bifurcation, and dot. The accuracy depends on the result of the three main process namely pre-processing, thinning process and feature extraction. Keywords: Arch, loop, whorl, Preprocessing, Thinning Process, Feature Extraction, Ridge.
Image processing is the process of transforming an image into a digital form and performing certain operations to get some useful information from it. The image processing system usually treats all images as 2D signals when applying certain predetermined signal processing methods.There are five main types of image processing Visualization Find objects that are not visible in the imageRecognition Distinguish or detect objects in the imageSharpening and restoration Create an enhanced image from the original imagePattern recognition Measure the various patterns around the objects in the imageRetrieval Browse and search images from a large database of digital images that are similar to the original image Supriya Kumari "Image Processing in the Current Scenario" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-5 , August 2022, URL: https://www.ijtsrd.com/papers/ijtsrd51728.pdf Paper URL: https://www.ijtsrd.com/computer-science/other/51728/image-processing-in-the-current-scenario/supriya-kumari
PREPROCESSING ALGORITHM FOR DIGITAL FINGERPRINT IMAGE RECOGNITIONijcsity
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Biometrics is one of the most used technologies in the field of security due to its reliability and performance. It is based on several physical human characteristics but the most used technology is the fingerprint recognition, and since we must carry out an image processing to be able to exploit the data in each fingerprint we propose in this article an image preprocessing procedure in order to improve its quality before extracting the necessary information for the comparison phase.
Digital image processing is the use of computer algorithms to perform image processing on digital images. As a subcategory or field of digital signal processing, digital image processing has many advantages over analog image processing.
During past few years, brain tumor segmentation in CT has become an emergent research area in the field of medical imaging system. Brain tumor detection helps in finding the exact size and location of tumor. An efficient algorithm is proposed in this project for tumor detection based on segmentation and morphological operators. Firstly quality of scanned image is enhanced and then morphological operators are applied to detect the tumor in the scanned image. The problem with biopsy is that the patient has to be hospitalized and also the results (around 15%) give false negative. Scan images are read by radiologist but it's a subjective analysis which requires more experience. In the proposed work we segment the renal region and then classify the tumors as benign or malignant by using ANFIS, which is a non-invasive automated process. This approach reduces the waiting time of the patient.
Pre-Processing Image Algorithm for Fingerprint Recognition and its Implementa...ijseajournal
Â
Fingerprint recognition technology is becoming increasingly popular and widely used for many applications that require a high level of security. We can meet several types of sensors integrated in the fingerprint recognition system as well as several types of image processing algorithm in order to ensure
reliable and fast authentication of people. Embedded systems have a wide variety and the choice of a welldesigned
processor is one of the most important factors that directly affect the overall performance of the system. This paper introduces a preliminary treatment to the image in order to improve the quality, and then present a hardware implementation.
Till now many algorithms are published for fingerprint recognition and these algorithms has different accuracy rate. This paper consists of information of about fingerprint (biometrics) recognition. The novel algorithm is considered for thinning process. Whole process of recognition is explained from image capturing to verification. The image captured is first converted to gray scale then image enrichment is done then thinning process take over charge which is main process then last process which is also equally important as thinning process is feature extraction which extracts ridge ending, bifurcation, and dot. The accuracy depends on the result of the three main process namely pre-processing, thinning process and feature extraction. Keywords: Arch, loop, whorl, Preprocessing, Thinning Process, Feature Extraction, Ridge.
Image processing is the process of transforming an image into a digital form and performing certain operations to get some useful information from it. The image processing system usually treats all images as 2D signals when applying certain predetermined signal processing methods.There are five main types of image processing Visualization Find objects that are not visible in the imageRecognition Distinguish or detect objects in the imageSharpening and restoration Create an enhanced image from the original imagePattern recognition Measure the various patterns around the objects in the imageRetrieval Browse and search images from a large database of digital images that are similar to the original image Supriya Kumari "Image Processing in the Current Scenario" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-5 , August 2022, URL: https://www.ijtsrd.com/papers/ijtsrd51728.pdf Paper URL: https://www.ijtsrd.com/computer-science/other/51728/image-processing-in-the-current-scenario/supriya-kumari
PREPROCESSING ALGORITHM FOR DIGITAL FINGERPRINT IMAGE RECOGNITIONijcsity
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Biometrics is one of the most used technologies in the field of security due to its reliability and performance. It is based on several physical human characteristics but the most used technology is the fingerprint recognition, and since we must carry out an image processing to be able to exploit the data in each fingerprint we propose in this article an image preprocessing procedure in order to improve its quality before extracting the necessary information for the comparison phase.
IOSR Journal of Electronics and Communication Engineering(IOSR-JECE) is an open access international journal that provides rapid publication (within a month) of articles in all areas of electronics and communication engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electronics and communication engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Face and liveness detection with criminal identification using machine learni...IAESIJAI
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In the past, real-world photos have been used to train classifiers for face liveness identification since the related face presentation attacks (PA) and real-world images have a high degree of overlap. The use of deep convolutional neural networks (CNN) and real-world face photos together to identify the liveness of a face, however, has received very little study. A face recognition system should be able to identify real faces as well as efforts at faking utilizing printed or digital presentations. A true spoofing avoidance method involves observing facial liveness, such as eye blinking and lip movement. However, this strategy is rendered useless when defending against replay assaults that use video. The anti-spoofing technique consists of two modules: the ConvNet classifier module and the blinking eye module, which measure lip and eye movement. The results of the testing demonstrate that the developed module is capable of identifying various face spoof assaults, including those made with the use of posters, masks, or smartphones. To assess the convolutional features in this study adaptively fused from deep CNN produced face pictures and convolutional layers learned from real-world identification. Extensive tests using intra-database and cross-database scenarios on cutting-edge face anti-spoofing databases including CASIA, OULU, NUAA and replay-attack dataset demonstrate that the proposed solution methods for face liveness detection. The algorithm has a 94.30% accuracy rate.
PREPROCESSING ALGORITHM FOR DIGITAL FINGERPRINT IMAGE RECOGNITIONijcsity
Â
Biometrics is one of the most used technologies in the field of security due to its reliability and
performance. It is based on several physical human characteristics but the most used technology is the
fingerprint recognition, and since we must carry out an image processing to be able to exploit the data in
each fingerprint we propose in this article an image preprocessing procedure in order to improve its
quality before extracting the necessary information for the comparison phase
PREPROCESSING ALGORITHM FOR DIGITAL FINGERPRINT IMAGE RECOGNITIONijcsity
Â
Biometrics is one of the most used technologies in the field of security due to its reliability and performance. It is based on several physical human characteristics but the most used technology is the fingerprint recognition, and since we must carry out an image processing to be able to exploit the data in each fingerprint we propose in this article an image preprocessing procedure in order to improve its quality before extracting the necessary information for the comparison phase.
PREPROCESSING ALGORITHM FOR DIGITAL FINGERPRINT IMAGE RECOGNITION ijcsity
Â
Biometrics is one of the most used technologies in the field of security due to its reliability and
performance. It is based on several physical human characteristics but the most used technology is the
fingerprint recognition, and since we must carry out an image processing to be able to exploit the data in
each fingerprint we propose in this article an image preprocessing procedure in order to improve its
quality before extracting the necessary information for the comparison phase.
Design and Implementation of Optical Palm Scannerdbpublications
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This paper proposes the method of designing and implementation of optical palm scanner. An individualâs palm lines do not significantly change after a certain age. This paper explains the method of extracting clear palm print image. This image will highlight creases and ridges pattern in the palm which will be distinct to each disorders and diseases. This image can further be used by physician to monitor the health state of individual.
An efficient method for recognizing the low quality fingerprint verification ...IJCI JOURNAL
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In this paper, we propose an efficient method to provide personal identification using fingerprint to get better accuracy even in noisy condition. The fingerprint matching based on the number of corresponding minutia pairings, has been in use for a long time, which is not very efficient for recognizing the low quality fingerprints. To overcome this problem, correlation technique is used. The correlation-based fingerprint verification system is capable of dealing with low quality images from which no minutiae can be extracted reliably and with fingerprints that suffer from non-uniform shape distortions, also in case of damaged and partial images. Orientation Field Methodology (OFM) has been used as a preprocessing module, and it converts the images into a field pattern based on the direction of the ridges, loops and bifurcations in the image of a fingerprint. The input image is then Cross Correlated (CC) with all the images in the cluster and the highest correlated image is taken as the output. The result gives a good recognition rate, as the proposed scheme uses Cross Correlation of Field Orientation (CCFO = OFM + CC) for fingerprint identification.
Sign language SL is commonly considered as the primary gesture based language for deaf and dumb people. It is a medium of communication for such people. Basically image based and sensor based are the two important sign language recognition methods. Because of the difficulties in wearing complex devices like Hand Gloves, armbands, helmets etc. in sensor based approaches, lots of researches are done by companies and researchers on image based approaches. Sign language is used by these people to communicate with the normal people. Understanding this sign language is a difficult task according to the normal people. To address these difficulties, a real time translator for sign language using deep learning DL is introduced. It enables to reduce the limitations and cons of other methods to a greater extent. With the help of this real time translator, communication will be better and fast without causing any delay. Jeni Moni | Anju J Prakash "Real Time Translator for Sign Language" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-5 , August 2020, URL: https://www.ijtsrd.com/papers/ijtsrd32915.pdf Paper Url :https://www.ijtsrd.com/computer-science/other/32915/real-time-translator-for-sign-language/jeni-moni
IOSR Journal of Electronics and Communication Engineering(IOSR-JECE) is an open access international journal that provides rapid publication (within a month) of articles in all areas of electronics and communication engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electronics and communication engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Face and liveness detection with criminal identification using machine learni...IAESIJAI
Â
In the past, real-world photos have been used to train classifiers for face liveness identification since the related face presentation attacks (PA) and real-world images have a high degree of overlap. The use of deep convolutional neural networks (CNN) and real-world face photos together to identify the liveness of a face, however, has received very little study. A face recognition system should be able to identify real faces as well as efforts at faking utilizing printed or digital presentations. A true spoofing avoidance method involves observing facial liveness, such as eye blinking and lip movement. However, this strategy is rendered useless when defending against replay assaults that use video. The anti-spoofing technique consists of two modules: the ConvNet classifier module and the blinking eye module, which measure lip and eye movement. The results of the testing demonstrate that the developed module is capable of identifying various face spoof assaults, including those made with the use of posters, masks, or smartphones. To assess the convolutional features in this study adaptively fused from deep CNN produced face pictures and convolutional layers learned from real-world identification. Extensive tests using intra-database and cross-database scenarios on cutting-edge face anti-spoofing databases including CASIA, OULU, NUAA and replay-attack dataset demonstrate that the proposed solution methods for face liveness detection. The algorithm has a 94.30% accuracy rate.
PREPROCESSING ALGORITHM FOR DIGITAL FINGERPRINT IMAGE RECOGNITIONijcsity
Â
Biometrics is one of the most used technologies in the field of security due to its reliability and
performance. It is based on several physical human characteristics but the most used technology is the
fingerprint recognition, and since we must carry out an image processing to be able to exploit the data in
each fingerprint we propose in this article an image preprocessing procedure in order to improve its
quality before extracting the necessary information for the comparison phase
PREPROCESSING ALGORITHM FOR DIGITAL FINGERPRINT IMAGE RECOGNITIONijcsity
Â
Biometrics is one of the most used technologies in the field of security due to its reliability and performance. It is based on several physical human characteristics but the most used technology is the fingerprint recognition, and since we must carry out an image processing to be able to exploit the data in each fingerprint we propose in this article an image preprocessing procedure in order to improve its quality before extracting the necessary information for the comparison phase.
PREPROCESSING ALGORITHM FOR DIGITAL FINGERPRINT IMAGE RECOGNITION ijcsity
Â
Biometrics is one of the most used technologies in the field of security due to its reliability and
performance. It is based on several physical human characteristics but the most used technology is the
fingerprint recognition, and since we must carry out an image processing to be able to exploit the data in
each fingerprint we propose in this article an image preprocessing procedure in order to improve its
quality before extracting the necessary information for the comparison phase.
Design and Implementation of Optical Palm Scannerdbpublications
Â
This paper proposes the method of designing and implementation of optical palm scanner. An individualâs palm lines do not significantly change after a certain age. This paper explains the method of extracting clear palm print image. This image will highlight creases and ridges pattern in the palm which will be distinct to each disorders and diseases. This image can further be used by physician to monitor the health state of individual.
An efficient method for recognizing the low quality fingerprint verification ...IJCI JOURNAL
Â
In this paper, we propose an efficient method to provide personal identification using fingerprint to get better accuracy even in noisy condition. The fingerprint matching based on the number of corresponding minutia pairings, has been in use for a long time, which is not very efficient for recognizing the low quality fingerprints. To overcome this problem, correlation technique is used. The correlation-based fingerprint verification system is capable of dealing with low quality images from which no minutiae can be extracted reliably and with fingerprints that suffer from non-uniform shape distortions, also in case of damaged and partial images. Orientation Field Methodology (OFM) has been used as a preprocessing module, and it converts the images into a field pattern based on the direction of the ridges, loops and bifurcations in the image of a fingerprint. The input image is then Cross Correlated (CC) with all the images in the cluster and the highest correlated image is taken as the output. The result gives a good recognition rate, as the proposed scheme uses Cross Correlation of Field Orientation (CCFO = OFM + CC) for fingerprint identification.
Sign language SL is commonly considered as the primary gesture based language for deaf and dumb people. It is a medium of communication for such people. Basically image based and sensor based are the two important sign language recognition methods. Because of the difficulties in wearing complex devices like Hand Gloves, armbands, helmets etc. in sensor based approaches, lots of researches are done by companies and researchers on image based approaches. Sign language is used by these people to communicate with the normal people. Understanding this sign language is a difficult task according to the normal people. To address these difficulties, a real time translator for sign language using deep learning DL is introduced. It enables to reduce the limitations and cons of other methods to a greater extent. With the help of this real time translator, communication will be better and fast without causing any delay. Jeni Moni | Anju J Prakash "Real Time Translator for Sign Language" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-5 , August 2020, URL: https://www.ijtsrd.com/papers/ijtsrd32915.pdf Paper Url :https://www.ijtsrd.com/computer-science/other/32915/real-time-translator-for-sign-language/jeni-moni
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
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Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Acetabularia Information For Class 9 .docxvaibhavrinwa19
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Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
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Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
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The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
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This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
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Analytical Study On Digital Image Processing Applications
1. SSRG International Journal of Computer Science and Engineering (SSRG-IJCSE) â Volume 7 Issue 6 â June 2020
ISSN: 2348 â 8387 www.internationaljournalssrg.org Page 4
Analytical Study on Digital Image Processing
Applications
A. Bindhu#1
, Dr. K. K. Thanammal*2
#1
Research Scholar , Department of Computer Science & Research Centre, S.T. Hindu College, 629002,
Affiliated to Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli â 627012, Tamil Nadu, India
*2
Associate Professor, Department of Computer Science & Application, S.T. Hindu College,629002,
Affiliated to Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli â 627012, Tamil Nadu, India.
Abstract
The extreme flexibility of the digital method
of image processing makes a wide variety of linear
and nonlinear processes possible. The digital image
processing techniques developed have been applied
to images from a wide range of disciplines. In this
paper, we have discussed about various applications
such as Image sharpening and restoration, medical
field, remote sensing, robotics, color processing,
pattern and character recognition, video processing,
agriculture, finger print biometrics, forensic, medical
palmistry, signature recognition, vehicle detection
from satellite images ,etc.
Keywords â Digital Image Processing, Analytical
study on DIP, UV Imaging, Biometrics, Segmentation.
.
I. INTRODUCTION
An image is defined by the mathematical
function f(x, y) where x and y are the two co-
ordinates horizontally and vertically. Image
processing is a method to perform some operations
on an image, in order to get an enhanced image or to
extract some useful information from it. Digital
Image Processing (DIP) is a rapidly evolving field
with growing applications in science and engineering.
Image processing holds the possibility of developing
the ultimate machine that could perform the visual
functions of all living beings [1].
Figure 1.1. How DIP works
In the above figure, an image has been
captured by a camera and has been sent to a digital
system to remove all the other details, and just focus
on the water drop by zooming it in such a way that
the quality of the image remains the same
II. APPLICATIONS OF DIGITAL IMAGE
PROCESSING
Some of the major fields in which digital
image processing is widely used are mentioned
below.
ďˇ Image sharpening and restoration
ďˇ Medical field
ďˇ Remote sensing
ďˇ Transmission and encoding
ďˇ Machine/Robot vision
ďˇ Color processing
ďˇ Pattern recognition
ďˇ Video processing
ďˇ Microscopic Imaging
A. Image sharpening and restoration
Sharpness is a combination of two
factors: resolution and acutance. Resolution is
straightforward and not subjective. Sharpening is a
technique for increasing the apparent sharpness of
an image. The image restoration are the removal or
reduction of degradations which are included during
the acquisition of images e.g.; Noise, pixel value
errors, out of focus blurring or camera motion
blurring using prior knowledge of the degradation
phenomenon. This means it deals with the modeling
of the degradation and applying the process
(inverse) to reconstruct the image. The image
restoration has got a wide scope of usage.
B. Medical field
The common applications of DIP in the field
of medical are Gamma ray imaging, PET scan, X Ray
Imaging, Medical CT, Imaging in the ultraviolet band,
Imaging in the microwave band [2]. Clustered micro
calcifications (MC) can be an important early sign of
breast cancer. They appear as bright spots of calcium
2. SSRG International Journal of Computer Science and Engineering (SSRG-IJCSE) â Volume 7 Issue 6 â June 2020
ISSN: 2348 â 8387 www.internationaljournalssrg.org Page 5
Figure 2.1. Image restoration and sharpening
deposits. Individual MCs are sometimes difficult to
detect because of the surrounding breast tissue, their
variation in shape (from granular to rod shapes),
orientation, brightness and diameter size. Due to the
subtlety in the appearance of individual MCs, there is
a significant risk that a radiologist may misclassify
some cases in breast cancer diagnosis. Recently
developed a content-based mammogram retrieval
system as a diagnostic aid to the radiologists in their
interpretation of mammograms [3]. Digital
dermoscopy is a widely used non-invasive tool that
combines optical magnification and special
illumination techniques to render an improved
dermoscopic image for clinical diagnosis of
melanoma. Dermatologists have regularly applied
this tool for several decades to analyze the surface
structure of human skin that is invisible to the naked
eyes [4][5]. The ANN method gives the best
performance as it neglects the background and
displays the required portion of an image that we
need. This image processing technique is one of the
most efficient ways of detecting lung cancer [6].
C. UV imaging
One important application of digital image
processing in the field of remote sensing is to detect
infrastructure damages caused by an earthquake. As
it takes longer time to grasp damage, even if serious
damages are focused on. Since the area affected by
the earthquake is sometimes so wide, that it not
possible to examine it with human eye in order to
estimate damages. It is very hectic and time
consuming process. So a solution to this is found in
digital image processing. An image of the affected
area is captured from the above ground and then it is
analysed to detect the various types of damage done
by the earthquake.
D. Robot vision
Apart from the many challenges that a robot
face today, one of the biggest challenge still is to
increase the vision of the robot. Make robot able to
see things, identify them, identify the hurdles etc.
Much work has been contributed by this field and a
complete other field of computer vision has been
introduced to work on it.
E. Line follower robot
Most of the robots today work by following
the line and thus are called line follower robots. This
helps a robot to move on its path and perform some
tasks. This has also been achieved through image
processing.
F. Colour processing
Color processing includes processing of
colored images and different color spaces that are
used. For example RGB color model, YCbCr, HSV.
It also involves studying transmission, storage, and
encoding of these color images.
G. Pattern and Character recognition
Pattern recognition involves study from
image processing and from various other fields that
includes machine learning (a branch of artificial
intelligence). In pattern recognition, image
processing is used for identifying the objects in
images and then machine learning is used to train the
system for the change in pattern. Pattern recognition
is used in computer aided diagnosis, recognition of
handwriting, recognition of images etc. Character
recognition, usually known as optical character
recognition and abbreviated as OCR. It is mechanical
or electronic translation of images of either
handwritten or printed text (usually captured by a
scanner) into machine editable text. It is a wide area
for researchers in pattern recognition, artificial
intelligence and machine vision. For many document
input tasks, character recognition is the most cost
effective and speedy method available [7].
H. Video processing
A video is nothing but just the very fast
movement of pictures. The quality of the video
depends on the number of frames/pictures per
minute and the quality of each frame being used.
Video processing involves noise reduction, detail
enhancement, motion detection, frame rate
conversion, aspect ratio conversion, color space
conversion etc.
I. Signature Recognition
Signature verification and recognition is also
an important application, which is to decide, whether
a signature belongs to a given signer based on the
image of signature and a few sample images of the
original signatures of the signer. Handwritten
signatures are imprecise in nature as their corners are
not always sharp, lines are not perfectly straight, and
curves are not necessarily smooth. Furthermore, the
fonts can be drawn in different sizes and orientation
in contrast to handwriting which is often assumed to
be written on a baseline in an upright position.
Therefore, a robust handwritten signature recognition
system has to account for all of these factors [7].
3. SSRG International Journal of Computer Science and Engineering (SSRG-IJCSE) â Volume 7 Issue 6 â June 2020
ISSN: 2348 â 8387 www.internationaljournalssrg.org Page 6
J. Human authentication by face and fingerprint
biometrics
People are required to be verified as a valid
individual to be able to access ATMs, airports, labs,
buildings, files, etc. Biometric enables an identity-
based method which can provide sufficient security
for these applications. Currently, biometric systems
make use of finger prints, voiceprints, face
characteristics, iris features, retina images, signature
etc. Biometrics cannot be stolen and forging is
practically impossible [8].
K. Medical Palmistry
Palmistry is a science which observes
human palm by different aspects and derives
conclusions about nature of the person. Since from
ancient times, many civilizations like Indian, Chinese,
Persian, Egyptian, Roman and Greek, people were
used to get guidance about their present and future by
means of palmistry. It includes attributes of human,
like, health, psychology, intelligence, lifestyle and
other related entities. Medical palmistry can be
considered as one of the branches of palmistry. By
using this medical palmistry, probable diseases can
be identified by observing some symbols in human
palms such as iceland, cross, grill, spot, star, square
and circle. Additionally shapes of palm and fingers
also play very important role in such decision making
for identification of diseases [9].
L. Vehicle Detection from Satellite Images
The approach described that the accuracy
rate of vehicles captured from satellite images. It
simply workout the full numbers of vehicles within
the desired space in the satellite image and vehicles
are shown underneath the bounding box as a small
spots [10].
M. Forensic
Digital image forensics (DIF) aims at
providing tools to support blind investigation. This
brand new discipline stems from existing multimedia
security-related research domains (e.g. Watermarking
and Steganography) and exploits image processing
and analysis tools to recover information about the
history of an image. Two principal research paths
evolve under the name of Digital Image Forensics.
The first one includes methods that attempt at
answering question a), by performing some kind of
ballistic analysis to identify the device that captured
the image, or at least to determine which devices did
not capture it. These methods will be collected in the
following under the common name of image source
device identification techniques. The second group of
methods aims instead at exposing traces of semantic
manipulation (i.e. forgeries) by studying
inconsistencies in natural image statistics [11].
N. Agriculture
Plant breeders need an efficient tool to monitor a
number of plant traits to achieve a higher yield. A
long boom was attached to a farm vehicle to carry
different sensors, cameras and other measurement
equipment. A program was developed to read sensors
signals and to geo-tag data using GPS for future
retrieval. Three programs were developed for image
acquisition via webcam and still cameras and a
central program for data processing and data
visualization. The efficiency of different system
architecture including different data transmission
networks was examined by conducting several
laboratory and field tests [12].
III. METHODOLOGY
The following are the phases of a digital
image processing. The block diagram of digital
image processing is as shown in the figure 3.1.
Figure 3.1.Block diagram of digital image processing
A. Image Acquisition
It is the first step or fundamental step of
digital image processing. Under image acquisition the
image is given in digital format. Generally, this stage
of image acquisition stage involves pre-processing,
such as scaling etc. An image can be made input by
some sort of scanner, digital cameras or with the help
of aerial cameras .This image should be a high
quality image with greater resolution, which helps in
proper image analysis.
B. Preprocessing
Some pre-processing operations are required
to be performed on the input image. The aim of pre-
processing techniques is to improve the image data to
suppress the unwanted distortions and to enhance
some features of the input image. When processing
high resolution images, the image size is needed to be
reduced because of the reason that processing on high
resolution images takes a longer time. Then after the
color image is converted into grey scale image,
because less information is needed to be provided for
each pixel. In fact grey color is the one in which the
red, blue and green components contain equal
intensities; therefore it is necessary to specify a single
value of intensity level for each pixel.
C. Edge Detection and Segmentation
Edge detection is an image processing
technique for finding the boundaries of objects within
images. It works by detecting discontinuities in
brightness. Edge detection is used for image
Result
Image Acquisition Preprocessing Segmentation
Feature Extraction Comparison with
database
4. SSRG International Journal of Computer Science and Engineering (SSRG-IJCSE) â Volume 7 Issue 6 â June 2020
ISSN: 2348 â 8387 www.internationaljournalssrg.org Page 7
segmentation and data extraction in areas such as
image processing, computer vision, and machine
vision. Image segmentation is the process of
partitioning a digital image into multiple segments.
The goal of segmentation is to simplify and/or
change the representation of an image into something
that is more meaningful and easier to analyze. Image
segmentation is typically used to locate objects
and boundaries (lines, curves, etc.) in images. More
precisely, image segmentation is the process of
assigning a label to every pixel in an image such that
pixels with the same label share certain
characteristics.
D. Image Restoration
Image restoration is an area, in which the
appearance of an image is improved. Image
restoration techniques are based on mathematical
models or probabilistic analysis of an image. There
are various filter available or can be designed for the
restoration and to enhance the quality of an image.
E. Output Image
After using various image processing
techniques accompanied with morphological
operation on digital image, the object of interest from
the given image can be obtained.
IV. CONCLUSION
In this paper we have analyzed the various
applications of digital image processing in different
areas such as image sharpening and restoration, UV
imaging, medical field, human authentication by face
and fingerprint biometrics, Forensic, Vehicle
Detection from Satellite Images, Agriculture and
Medical Palmistry. New findings in image processing
area will change the world. Advance researches in
image processing and artificial intelligence will
involve voice commands, language translation,
recognizing and tracking people and recognizing and
tracking people and things, diagnosing medical
conditions, performing operation & surgery. We can
also use the digital image processing technique in
finding the anomalies of respiratory system, which
leads to the detection of covid-19.
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