1. A
PROJECT REPORT ON
MACHINE VISION
Submitted to
Amity University Uttar Pradesh
In partial fulfilment of the requirements for the award of
the degree of
Bachelor of Technology
In
Computer Science and Engineering
BY
DIGVIJAY PRATAP SINGH
(A2345915037)
UNDER THE GUIDANCE OF
Mr. Raj Kumar Sagar
DEPARTMENT OF COMPUTER SCIENCE & ENGINEERINNG
AMITY SCHOOL OF ENGINEERING AND TECHNOLOGY

2. DECLARATION
I, DIGVIJAY PRATAP SINGH, Student of B.Tech (Computer Science and Engineering)
hereby declare that the Summer project report titled “Machine Vision” which is submitted by
me to Department of Computer Science and Engineering, Amity School of Engineering and
Technology, Amity University Uttar Pradesh, Noida, in partial fulfilment of requirement for the
award of the degree of Bachelor of Technology in Computer Science and Engineering, has not
been previously formed the basis for the award of any degree, diploma or other similar title or
recognition.
Date: 3-06-2016 Signature:
Name: Digvijay Pratap Singh
Roll no.: (A2345915037)

3. CERTIFICATE
On the basis of declaration submitted by DIGVIJAY PRATAP SINGH, Student of B.Tech
Computer Science and Engineering, I hereby certify that the summer project report titled
“MACHINE VISION” which is submitted to Department of Computer Science and Engineering,
Amity School Of Engineering and Technology, Amity University Uttar Pradesh, Noida, in
partial fulfilment of the requirement for the award of the degree of bachelor of Technology in
CSE, is an original contribution with existing Knowledge and faithful record of work carried out
by his under my guidance and supervision. To the best of my Knowledge this work has not been
submitted in part or full for any Degree or Diploma to this University or elsewhere.
Mr. Raj Kumar Sagar
Asst. Professor CSE
Department Of Computer Science and Engineering
ASET, Noida
Date: 03-12-2016

4. ACKNOWLEDGEMENT
The preparation of this summer project report work has been successfuldue to the
help and inspiration received from various sources. I would like to express my
gratitude to all those who gave me the opportunity to complete the project entitled
“MachineVision”.I am very thankful to Prof. Raj Kumar Sagar, Asst.Professor
CSE and prof. Saket for their valuable suggestions. This constant, sincere
inspirations and liberal co-operation in all respects are really unforgettable
experience for me. I have been very much benefited from their technical advice
and supportduring the period of my work on this summer project.
Digvijay Pratap Singh
A2345915037
CSE EVE (2015-19)

5. ABSTRACT
This paper deals with faculty attitudes toward teaching Machine Vision to
Computer and Information Systems undergraduate students. Machine vision (MV)
is the technology and methods used to provide imaging-based automatic inspection
and analysis for such applications as automatic inspection, process control, and
robot guidance in industry. The scope of MV is broad. MV is related to, though
distinct from, computer vision. It helps us increase the efficiency and the precision
of any given task. At the same time it also maintains the required standards. In this
paper, few popular applications of machine vision have been discussed to
showcase the importance of Machine vision.

6. TABLE OF CONTENTS
DECLARATION I
CERTIFICATE II
ACKNOWLEDGEMENT III
ABSTRACT IV
CONTENTS
1. INTRODUCTION
1.1 What is Machine Vision 1
1.2 Need for Machine Vision 1
1.3 Working of Machine Vision 1
2. Application of Machine Vision
2.1 Intelligent Transportation System 2-4
2.2 Condition monitoring of vegetation on
railway embankments 5
2.3 Quality assessmentof row crop plants 6-7
3. Conclusion 8
4. References 9

7. 1. INTRODUCTION
1.1 What is Machine Vision?
As the name suggests is related to vision i.e. what it sees. Unlike humans machines have to
follow a set of rules and procedures in order to complete the expected and desired tasks. Machine
vision is a technology which uses various methods for inspection and analysis in various
applications.
It all started in 1950 when two dimensional imaging for statistical pattern recognition was
developed. Post this there were many advancements in the field of Machine Vision. However,
over the last 20 years it has evolved with a brisk speed and has established itself as an integral
part of the manufacturing industry.
1.2 Why do we need machine vision?
With the increasing population, the demand for an effective way to monitor, analyse and perform
certain task has evolved into a dire need. With production increasing day by day in order to fulfil
the ever growing demand, it’s important that the quality of the products is maintained. Its
popularity in manufacturing industry has thus increased by the time as it provides a much needed
flexibility and automation options to the manufactures. It helps in sorting products, finding
defects much more effectively. It can complete a wide number of tasks much faster and
efficiently than humans. Another important factor is that it leads to significant reduction in costs
Apart from production, another thing which makes it essential for us is that it removes the result
to be biased by our opinion, view, belief, attitude, feeling or sentiment. This plays an important
role in researches or in compiling a report. It also introduces uniform criteria for analysis and
thus further eradicates the error and increase the effectiveness.
1.3 How does Machine Vision work?
Machine Vision is a technology that involves three basic stages-
 Acquiring information- This involves collection of the data using camera’s, sensors,
sound recorders or other electronic devices. This translates the physiological information
into the digital information which can further be analysed.
 Data processing- This involves the analysis of data that has been acquired. In this
process the data is combed for all the important and required information and the rest is
ignored. This is done by using a set of programs and algorithms which help in filtering
out the useful information from the lot. This step can also be stated as the most complex
one as in this step we have to process a considerable amount of data effectively and in
time.

8.  Output- After the data has been analysed and the output is produced. It may be in the
form of organised data files which can be further used for other work or an action taken
by the machine which includes- Application of brakes in the car, stopping the
manufacturing line if a defected product is discovered, etc.
2. Applications of Machine Vision:
Machine Vision is used in a wide variety of fields in the modern world. With the boost in
technology, its scope has skyrocketed. From agriculture to manufacturing industry, it’s
applications with time is increasing. Few of the applications are as follows:
2.1 In INTELLIGENT TRANSPOTATION SYSTEM (ITS):
In ITS, three basic technologies are deployed, namely-
Image-Acquisition Hardware: For Image acquisition, a conventional TV camera is used which
takes two images with different shutter speeds sequentially. This generates a wide range image
by integrating dark areas that are acquired in the long shutter image and bright areas captured in
short shutter image. Under high intensity contrast, the success rate of lane detection went up
from 60% to 95%. In this method it is assumed that motion between the two images is
negligible.
Real Time Image processors: It’s a challenge to carry out real-time image processing with both
compact and relatively inexpensive processors.
Algorithms: The 3D vision algorithms are of two types-
 Direct Algorithms: Time-of-flight and stereo methods are included in this type of
algorithms
 Indirect Algorithms: In this a single image is used and the distances are calculates based
on the information of focus.
ITS has many further applications. However, four characteristic applications that involve
machine vision are:
1.) Cruise Assistance: This is mainly for the highways where the speed of the vehicles is
normally on the higher end. Therefore, the chances of losing control over the increases
drastically. However, most of the accidents are caused due to human errors and not
machine errors. Therefore by preventing the human interference or by checking them at
right moments, many accidents can be prevented and lives can be saved. Few application
which are useful in doing so are:
 Road and lane detection: Many people die each year by drifting off the road and
crashing into trees and other obstacles. The Road and lane detection system combined
together can help us reduce such accidents as they can alarm the driver before it’s too
late. A camera is used to track down the lane and thereby alarm the driver if the
vehicle goes beyond the safety limits.

9.  Vehicle Detection- This is another very important area where to cut down mishaps on
highways. By detecting a vehicle with in the allotted distance, it is possible to avoid a
potential accident from occurring. It not only ensures the safety of the driver but also
of the other person driving another vehicle. Few methods of detecting a vehicle are
detecting the symmetry points, exploiting the stereotypes, etc. If a warning is issued
in time, driver can take the possible steps to avoid the accident.
In case driver isn’t able to respond in time, by the combination of lane and vehicle
detection, the car will take the desired rout and perform the pre-defined tasks which may
include automatic breaking, turning the steering in apt direction, etc.
2.) Urban driving assistance: When it comes to driving in urban areas, there are many things
one needs to keep in mind. The objects and possibilities are endless. However, it’s the
pedestrians who are of prime concern here. Pedestrians unlike cars cannot be generalized
which makes the task even more difficult. Few methods for detecting pedestrians are:
 Frame differencing followed by segmentation.
 Detecting rhythmic change in the shape as the pedestrian walks.
 3D modelling of the human body with specializing the detection of the regular
motion of legs.
3.) Traffic and Road monitoring: Machine-vision is clearly a smart choice when it comes to
traffic and road monitoring. It is because it has no prejudices and takes decisions based
on concerned facts and is unaffected by his own perspective. It involves three steps:
 Segmentation
 Reasoning about various parameters of traffic, few of them which are- vehicle
counts, average speed, queue length at the intersections (particularly in urban
areas), etc.
 Reasoning about wide-area parameters of traffic, few of them which are- link
times and origin-destination counts at a transport management center from where
traffic can be efficiently managed.
4.) Driver Monitoring: Best way, however to decrease the chances of any mishap is to keep a
check on driver by raising alarm at the right time. The alarm may vary from just a beep
sound to the vibrations on steering wheel. The driver is closely monitored and checked
for showing any signs of drowsiness.

10. However there are a few notable challenges:
 The machine-vision system’s cost must come down.
 Under different weather and traffic conditions, the efficiency of machine-vision systems
must stay constant.
 More intelligent sensors are needed.

11. 2.2 In Condition monitoring of vegetationon railway embankments:
Railway Embankments are very important yet often neglected areas. Inclusion of machine vision in this
field will ensure an effective way of monitoring the vegetation growing on the railway embankments. It
often happens that human perception towards a situation or result may be biased or affected by other
factors. For e.g. - It is quite possible that one person might see the vegetation grown as negligible while
the other person might see it as considerable. Therefore, by deploying machine vision technology in this
field, we can ensure a more generalized and accurate assessment.
Mean shift algorithm is used for segmentation of the captured images. The three parameters that
essentially have to be set are:
 The spatial resolution (hs): It affects smoothing. It is chosen depending on the size of the image
and objects.
 The range resolution (hr): It affects the no. of segments or clusters. The hr value should be kept
low if the image contrast is low
 The size of the smallest segment (M): On the basis of noisy patches, this value is chosen.
These three parameters (hs, hr, M) were experimentally set as: hs=2, hr=4 and M=30
Along with Mean Shift Clustering another procedure used for segmentation is- Histogram of Oriented
Gradients (HOG). It is also carried out to ensure the detection of railway tracks. HOG is typically used fir
detection of specified objects in the captured images by segmentation.

12. 2.3 Quality assessment of row crop plants:
In farming, the crop is treated in a more generalized manner as a result of which there’s a large amount of
wastage of water, pesticides and human efforts. Not only the current farming techniques are effecting the
environment adversely but their productivity is too, not good. By introducing machine vision for the
quality assessment of row crop plants, we cannot only minimize the human efforts and make it relatively
cheaper but we can also increase the efficiency by proper management.
As a fact, if this procedure is deployed, the amount of chemicals used in the field is reduced up to 95%
without affecting the crops in any manner.
In this process, we do- weed-crop classification, plant density evaluation, calculate the leave’s area and
also detect the location of weed or crops as per requirement. This entire process is done in various steps
discussed below:
 Work Definition: In this there are two major tasks:
- Task 1: Measurement of position of plants: The plants position, from pre-processed images is
measured by three procedures:
 Firstly, the plant is segmented from the background.
 The leaves that are overlapping each other are separated.
 The step position is assessed on the basis of detected leaves. Once the plant position or
stem position is measured, assessment of plant quality is done then by assigning the
leaves to their corresponding stem. By doing so all the individual plants are separated.
At last the area of plant canopy is calculated.
- Task 1: Assessment of the quality of plants.
- Task 2: Measurement of plant’s position.
 Preparation: The preparation includes 5 steps which need to considered:
 Camera Perspective: Top down, forwards and lateral perspectives are three different
perspectives used.
 Illumination direction: The illumination direction can help us identify the overlapping of
the leaves and at the same time help us identify the edges too. The camera with right
perspective and right illumination together can make this process effective. For eg: If we
take the top-down perspective of a camera and front lighting source is chosen for
capturing image, it’s effectiveness will be maximum.
 Grey scale or colour scale camera: On grey scale images, different illumination
spectrum are calculated for leaves, soil, stones, wood and defoliation. A color
camera issued as it is virtually impossible to segment plants from background by
information that is monochrome on grey scale images. The overlapping edges
can be highlighted much more effectively by doing so.

13.  Spectrum of illumination: Visible spectrum is expected naturally due to
application of colour camera. Near Infrared lighting (NIR) along with grey scale
camera is effective in detecting overlapping edges. Uses of White LED or halogen
lamps are also effective.
 Potential applications of 3D camera: Due to groove structure, unevenness of
field, low depth resolution of 3D camera. 3D cameras will not be considered in
this work.
 Plant Position Measurement:
 Plant Segmentation: The segmentation of plant from its background is an
important process in order to locate the plant and its boundaries in the image. This
holds importance as it will affect the accuracy of further analysis like- area of
leaves, stem position assessment, etc.
 Leaves Segmentation: It is often possible that the weed is of same or similar
colour as the plant. In such cases leaves segmentation is used to differentiate
between the plant and weed. In leaves segmentation the leaves overlapping the
most challenging aspect as it makes it difficult to
The most challenges of leaves segmentation is that, the leaves overlap with each other.
Therefore, the critical issue of leaves segmentation is to detect the overlapping edges.
For this purpose a proper camera system should be chosen. Two principles which help us in
doing so are:
 Suppress the texture information of leaves
 Suppress the fake edges caused by shadowing, wrinkle, etc.
A grey scale camera is recommended with NIR front lighting for detection of overlapping edges.
 Stem Position Assessment: Stem positions are difficult to detect as the stems are
obscured by leaves due to top-down perspective.
 Plant Quality assessment: On the basis of obtained stem positions, the leaves should be
assigned in a proper order to their relevant stems in order to separate the plants
effectively. The quality assessment of the plants can thus be done.

14. 3. Conclusion
The machine vision technology is the answer to the rising future demands. With increase in the demands,
it is difficult to keep up with the quality along with the quantity of produced goods. Machine vision not
only makes it’s cheaper for manufacturing goods but also increases the effectiveness drastically. In
research sector machine vision has a clear cut advantage as the results or observations presented by it are
unbiased and unaffected by ideologies or perspective. As a technology there are still a lot of
advancements needed in order to make it more effective in a many fields such as agriculture, research,
etc. It not only helps us cut the extra cost but at the same time increase the efficiency of the work for
which this technology is deployed. The examples undertaken in this paper clearly shows it’s important in
making roads and transportation safer for us. At the same time it also helps us in saving costs in
agriculture sector by minimizing the use of fertilizers and pesticides. Thus, we can say it is to some extent
environment friendly and sustainable. It also helps us in researches by providing the data uniformly
irrespective of situations.

15. 4. REFERENCES
 Machine Vision Systems for intelligent Transport Systems by lchiro Masaki, Massachusetts
Institute of Technology
 Machine Vision for Condition Monitoring Vegetation on Railway Embankments by
R. G. Nyberg*, N. K. Gupta, S. Yella, M. S. Dougherty, Edinburgh Napier University,
School of Engineering & the Built Environment
 Quality assessment of row crop plants by using a machine vision system by Michael
Weyrich (Institute of Industrial Automation and Software Engineering University of Stuttgart,
Stuttgart, Germany) and Yongheng Wang, Matthias Scharf (Chair of Automated Manufacturing
and Assembly, University of Siegen, Siegen, Germany)