Image Processing In Industry Automation
Image
Processing in Industry Automation
Introduction
:-
The Fourth Industrial Revolution is the
ongoing automation of traditional manufacturing
and industrial practices, using modern smart technology. Large-scale machine to machine communication (M2M) and
the Internet of Things (IoT) are integrated
for increased automation, improved communication and self-monitoring, and
production of smart machines that can analyze and diagnose issues without the
need for human intervention. There is hardly any manufacturing process
that is conceivable without imaging, Industrial Image processing works as chief
technology in industrial automation.
Image
Processing in Industry :-
Production employees will also benefit from applications
using industrial cameras. However far automation progresses, there are good
reasons why there will always be a place for humans in industrial production.
These include "human" sensory capabilities, flexibility and
affordability.
Important Image Processing techniques used in industry automation
:-
1. Image Rectification :-
This operation
also termed as image restoration. This is because satellite data is prepared in
this step for further processing and analysis and hence it is generally called
data preparation or preprocessing.
These operations
are intended to eliminate or correct the distortions or errors caused due to
geometric distortions, radiometric distortions, and presence of noise in the
data. Etc. The standard products available from National Remote Sensing Agency
are pre-processed to the extent of radiometric and geometric corrections.
2. Image Enhancement :-
Image
enhancements are done to improve the interpretability of the satellite image.
Different techniques of image enhancements were used, of which linear stretch,
standard deviation stretch, histogram equalization and stretching by using
break points are the prominent methods used for image stretching.
Different
techniques give different types of result and one single technique is good for
all types of scenes or interpretations.
3. Image Classification :-
Image
classification is the process of assigning land cover classes to pixels. Image
classification operation is essentially met to substitute the visual analysis
of the remotely sensed satellite data and quantitative assessment.
The
classification of the remotely sensed data can be carried out either without a
priori knowledge about the features present in the scene (unsupervised
classification) or with a priori knowledge about the terrain features
(supervised classification) .
It’s not possible to write about
every application of image processing in Industrial automation, so for In this
blog we will restrict to image processing in Medical industry automation.
Medical
Imaging mainly concentrates on
uncovering and revealing internal structures which are hidden by the
skin and
bones. In addition,
it is used
to analyze , diagnose, recognize
and treat the illness or disease. This technique is
particularly useful for
the specialists to make laparoscopic
surgeries for viewing the
interior parts without actually opening the body.
Some of the main application of image processing in Medical
Industry are:
IMAGING :-
MRI scan,
X-Ray Scans
Endoscopy
Radiography
Electrocardiography (ECG)
Positron Emission Tomography (PET)
Nuclear Medicine Imaging
RESEARCH :-
Gene detection
Electronic microbial detection
Biorhythm analysis
ANALYSIS :-
DNA analysis and comparison
Biochemical synthesis analysis
RNA fingerprinting
These are some of the many fields of the
biomedical sphere where the signal processing is used.
A brief explanation of the main ones of these
systems is given below :
MRI Scan :-
MRI (Magnetic Resonance
Imaging) is a
medical imaging technique
to image the physiological process and anatomy of the human body. MRI scanners form the
medically significant images of the body by using magnetic fields and radio
waves. MRI scans used
to produce variety
of clinical data additional to
digital images.
This is a MRI report, it shows
the different sides of brains as MRI take image of organs as slices and then
computer model uses those images to make a 3-D model.
Endoscopy :-
Endoscopy exams are sometimes used to look for
cancer. For these exams, an endoscope, which is a thin, lighted tube (usually
with a small video camera on the end) is put inside the body to look for
cancer. Also, one of the major advantages of endoscopy exams is that we can
diagnose non invasive way. For e.g., a person has been shot, so now without
endoscopy doctors had to open up that patient from many parts to get to the
bullet and injuries, but now with this technology unnecessary biopsies are now
can be avoided and doctors can treat a patient with more precision and less
risks.
This image is an illustration of how endoscopy
helps doctors
Conclusion
:-
Medical imaging is developing rapidly due to
developments in image processing techniques including image rectification,
classification, and enhancement. Image processing increases percentage &
amount of detected tissues. This topic presents the application of both simple
and sophisticated image analysis techniques in the medical imaging industry. Different image processing techniques are
used to perform different automation tasks. Medical
industry has undergone a drastic change from using
2D image processing to currently using 3D image processing techniques and this
has revolutionized the automation.
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