A presentation on Image Recognition, the basic definition and working of Image Recognition, Edge Detection, Neural Networks, use of Convolutional Neural Network in Image Recognition, Applications, Future Scope and Conclusion
We create a group presentation for Simulation & Modeling. This presentation has so many related fields as like artificial intelligence ,Information engineering,Neurology, Signal processing etc.
Mika Kaukoranta presents what computer vision is and how it can be utilized in software testing by gaining high-level understanding from digital images or videos.
A presentation on Image Recognition, the basic definition and working of Image Recognition, Edge Detection, Neural Networks, use of Convolutional Neural Network in Image Recognition, Applications, Future Scope and Conclusion
We create a group presentation for Simulation & Modeling. This presentation has so many related fields as like artificial intelligence ,Information engineering,Neurology, Signal processing etc.
Mika Kaukoranta presents what computer vision is and how it can be utilized in software testing by gaining high-level understanding from digital images or videos.
Efficient and accurate object detection has been an important topic in the advancement of computer vision systems.
Our project aims to detect the object with the goal of achieving high accuracy with a real-time performance.
In this project, we use a completely deep learning based approach to solve the problem of object detection.
The input to the system will be a real time image, and the output will be a bounding box corresponding to all the objects in the image, along with the class of object in each box.
Objective -
Develop a application that detects an object and it can be used for vehicles counting, when the object is a vehicle such as a bicycle or car, it can count how many vehicles have passed from a particular area or road and it can recognize human activity too.
With so much of our lives computerized, it is vitally important that machines and humans can understand one another and pass information back and forth. Mostly computers have things their way we have to & talk to them through relatively crude devices such as keyboards and mice so they can figure out what we want them to do. However, when it comes to processing more human kinds of information, like an old-fashioned printed book or a letter scribbled with a fountain pen, computers have to work much harder. That is where optical character recognition (OCR) comes in. Here we process the image, where we apply various pre-processing techniques like desk wing, binarization etc. and algorithms like Tesseract to recognize the characters and give us the final document. T.Gnana Prakash | K. Anusha"Text Extraction from Image using Python" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-6 , October 2017, URL: http://www.ijtsrd.com/papers/ijtsrd2501.pdf http://www.ijtsrd.com/computer-science/simulation/2501/text-extraction-from-image-using-python/tgnana-prakash
Abstract:
With an everyday increase in the number of cars on our roads and highways, we are facing numerous problems, for example:
• Smuggling of cars
• Invalid license plates
• Identification of stolen cars
• Usage of cars in terrorist attacks/illegal activities
In order to address the above issues, we took up the project of developing a prototype, which can perform license plate recognition (LPR). This project, as the name signifies, deals with reading, storing and comparing the license plate numbers retrieved from snapshots of cars to ensure safety in the country and ultimately help to reduce unauthorized vehicles access and crime.
License Plate Recognition (LPR) has been a practical technique in the past decades. It is one of the most important applications for Computer Vision, Patter Recognition and Image Processing in the field of Intelligent Transportation Systems (ITS).
Generally, the LPR system is divided into three steps, license plate locating, license plate character segmentation and license plate recognition. This project discusses a complete license plate recognition system with special emphasis on the Localization Module.In this study, the proposed algorithm is based on extraction of plate region using morphological operations and shape detection algorithms. Segmentation of plate made use of horizontal and vertical smearing and line detection algorithms. Lastly, template matching algorithms were used for character recognition.
The implementation of the project was done in the platforms of Matlab and OpenCV.
Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos.
A Small Helping Hand from me to my Engineering collegues and my other friends in need of Object Detection
Attendance Management System using Face RecognitionNanditaDutta4
The project ppt presentation is made for the academic session for the completion of the work from Bharati Vidyapeeth Deemed University(IMED) MCA department
In today’s traffic world, ambulance plays a major role when an accident occurs on the road network and the need arises to save valuable human life. Transportation of a patient to an emergency hospital seems quite simple but in actuality, it is quite difficult and gets more difficult during peak hours.
In our Ambulance Booking System, people can easily book an ambulance. There are three major modules namely User, Ambulance, and Hospital. Users can register and log in using credentials. Users can edit their profile and change their password in an emergency. Any Upcoming Ambulance Booking details if anyone wants to Book an Ambulance or if there is an Emergency.
For booking an ambulance users have to select ambulance size, pick-up point & hospital, and date & time. In an emergency will automatically book the nearest ambulance & hospital. Users will get a list of All the bookings of Ambulances. The front-end involves Html, CSS, and JavaScript and the back-end involves Python. The framework used is Django and the database is MySQL.
In this system, there are four entities User, Ambulance and Hospital. The user must register and log in using a username and password. After logging in, the user can Book Ambulance, Book Hospital, View Nearby Hospitals, View Previous Booked Ambulances and Hospitals, and it can also change its password.
When the user books an ambulance and hospital, a booking request is sent to the respective representatives of the ambulance and hospital. In view, Nearby Hospitals the user can view the nearest hospitals in their location. The ambulance driver has to register and then login in using a username and password.
After logging in, the driver can view booking requests, nearby hospitals and their previous bookings i.e., previously accepted requests. In Booking requests, it can either accept or decline the user requests. The hospital has to register and log in using a username and password. After login in the hospital representative can view the booking request and either accept or decline the user request.In today’s traffic world, ambulance plays a major role when an accident occurs on the road network and the need arises to save valuable human life. Transportation of a patient to an emergency hospital seems quite simple but in actuality, it is quite difficult and gets more difficult during peak hours.
In our Ambulance Booking System, people can easily book an ambulance. There are three major modules namely User, Ambulance, and Hospital. Users can register and log in using credentials. Users can edit their profile and change their password in an emergency. Any Upcoming Ambulance Booking details if anyone wants to Book an Ambulance or if there is an Emergency.
For booking an ambulance users have to select ambulance size, pick-up point & hospital, and date & time. In an emergency will automatically book the nearest ambulance & hospital. Users will get a list of All the bookings of Ambulances. The front-end involves Html, CSS, and
Efficient and accurate object detection has been an important topic in the advancement of computer vision systems.
Our project aims to detect the object with the goal of achieving high accuracy with a real-time performance.
In this project, we use a completely deep learning based approach to solve the problem of object detection.
The input to the system will be a real time image, and the output will be a bounding box corresponding to all the objects in the image, along with the class of object in each box.
Objective -
Develop a application that detects an object and it can be used for vehicles counting, when the object is a vehicle such as a bicycle or car, it can count how many vehicles have passed from a particular area or road and it can recognize human activity too.
With so much of our lives computerized, it is vitally important that machines and humans can understand one another and pass information back and forth. Mostly computers have things their way we have to & talk to them through relatively crude devices such as keyboards and mice so they can figure out what we want them to do. However, when it comes to processing more human kinds of information, like an old-fashioned printed book or a letter scribbled with a fountain pen, computers have to work much harder. That is where optical character recognition (OCR) comes in. Here we process the image, where we apply various pre-processing techniques like desk wing, binarization etc. and algorithms like Tesseract to recognize the characters and give us the final document. T.Gnana Prakash | K. Anusha"Text Extraction from Image using Python" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-6 , October 2017, URL: http://www.ijtsrd.com/papers/ijtsrd2501.pdf http://www.ijtsrd.com/computer-science/simulation/2501/text-extraction-from-image-using-python/tgnana-prakash
Abstract:
With an everyday increase in the number of cars on our roads and highways, we are facing numerous problems, for example:
• Smuggling of cars
• Invalid license plates
• Identification of stolen cars
• Usage of cars in terrorist attacks/illegal activities
In order to address the above issues, we took up the project of developing a prototype, which can perform license plate recognition (LPR). This project, as the name signifies, deals with reading, storing and comparing the license plate numbers retrieved from snapshots of cars to ensure safety in the country and ultimately help to reduce unauthorized vehicles access and crime.
License Plate Recognition (LPR) has been a practical technique in the past decades. It is one of the most important applications for Computer Vision, Patter Recognition and Image Processing in the field of Intelligent Transportation Systems (ITS).
Generally, the LPR system is divided into three steps, license plate locating, license plate character segmentation and license plate recognition. This project discusses a complete license plate recognition system with special emphasis on the Localization Module.In this study, the proposed algorithm is based on extraction of plate region using morphological operations and shape detection algorithms. Segmentation of plate made use of horizontal and vertical smearing and line detection algorithms. Lastly, template matching algorithms were used for character recognition.
The implementation of the project was done in the platforms of Matlab and OpenCV.
Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos.
A Small Helping Hand from me to my Engineering collegues and my other friends in need of Object Detection
Attendance Management System using Face RecognitionNanditaDutta4
The project ppt presentation is made for the academic session for the completion of the work from Bharati Vidyapeeth Deemed University(IMED) MCA department
In today’s traffic world, ambulance plays a major role when an accident occurs on the road network and the need arises to save valuable human life. Transportation of a patient to an emergency hospital seems quite simple but in actuality, it is quite difficult and gets more difficult during peak hours.
In our Ambulance Booking System, people can easily book an ambulance. There are three major modules namely User, Ambulance, and Hospital. Users can register and log in using credentials. Users can edit their profile and change their password in an emergency. Any Upcoming Ambulance Booking details if anyone wants to Book an Ambulance or if there is an Emergency.
For booking an ambulance users have to select ambulance size, pick-up point & hospital, and date & time. In an emergency will automatically book the nearest ambulance & hospital. Users will get a list of All the bookings of Ambulances. The front-end involves Html, CSS, and JavaScript and the back-end involves Python. The framework used is Django and the database is MySQL.
In this system, there are four entities User, Ambulance and Hospital. The user must register and log in using a username and password. After logging in, the user can Book Ambulance, Book Hospital, View Nearby Hospitals, View Previous Booked Ambulances and Hospitals, and it can also change its password.
When the user books an ambulance and hospital, a booking request is sent to the respective representatives of the ambulance and hospital. In view, Nearby Hospitals the user can view the nearest hospitals in their location. The ambulance driver has to register and then login in using a username and password.
After logging in, the driver can view booking requests, nearby hospitals and their previous bookings i.e., previously accepted requests. In Booking requests, it can either accept or decline the user requests. The hospital has to register and log in using a username and password. After login in the hospital representative can view the booking request and either accept or decline the user request.In today’s traffic world, ambulance plays a major role when an accident occurs on the road network and the need arises to save valuable human life. Transportation of a patient to an emergency hospital seems quite simple but in actuality, it is quite difficult and gets more difficult during peak hours.
In our Ambulance Booking System, people can easily book an ambulance. There are three major modules namely User, Ambulance, and Hospital. Users can register and log in using credentials. Users can edit their profile and change their password in an emergency. Any Upcoming Ambulance Booking details if anyone wants to Book an Ambulance or if there is an Emergency.
For booking an ambulance users have to select ambulance size, pick-up point & hospital, and date & time. In an emergency will automatically book the nearest ambulance & hospital. Users will get a list of All the bookings of Ambulances. The front-end involves Html, CSS, and
Computer Vision and various subcategories will have drastic changes in the future, and will surely lead to the betterment of services. Along with increased capacity, future algorithms will be easy to train on such massive data. The intervention of other technologies of the same sub-family will lead to surprising results.
So let us study what is computer vision and how it works.
https://www.datatobiz.com/blog/what-is-computer-vision/
The power of Computer Vision for precise Object Detection and Tracking. Explore Technology for seamless visual analysis. Elevate your projects with Nexgits expertise.
What is Computer Vision and How Does it Work.pdfSoftmaxAi
According to an artificial intelligence development company, there are many types of computer vision out of which the most common types of computer vision are object detection, image classification, pose estimation, semantic segmentation, image restoration, etc.
Scale Invariant Feature Transform Based Face Recognition from a Single Sample...ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Real Time Vision Hand Gesture Recognition Based Media Control via LAN & Wirel...IJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
Artificial intelligence for cctv cameras, video surveillanceHIGHMARK SECURITY
Artificial intelligence for cctv cameras, video surveillance
This era is called to be Artificial intelligence (AI) and Internet of Things (IoT), it is operated by robots and they are replaced the human. This article we mention the Artificial Intelligence for CCTV Cameras, Video Surveillance, it applies for this industry.
What is Artificial Intelligence ?
Artificial intelligence technology for CCTV Cameras
Artificial intelligence solutions for CCTV Cameras Companies
There has never been a better time to protect your property with CCTV; modern innovations have allowed security camera companies to create high-definition, fully manoeuvrable, internet-connecting cameras that provide their operators with an even wider range of flexibility than ever before. So where next for the CCTV camera industry?
Regardless of the industry in mind, automation seems to be the next step in their evolution – and CCTV is no different. Machine learning techniques are being tested in the hope that they’ll provide CCTV cameras with the ability to spot ‘troubling behaviour’ without the need for a human operator.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
2. What is Image Recognition ?
The recent advancement in artificial intelligence and machine learning has contributed to the
growth of computer vision and image recognition concepts. From controlling a driver-less car
to carrying out face detection for a biometric access, image recognition helps in processing
and categorizing objects based on trained algorithms.
Image recognition is a term for computer technologies that can recognize certain people,
animals, objects or other targeted subjects through the use of algorithms and machine
learning concepts.
The term “image recognition” is connected to “computer vision,” which is an overarching label
for the process of training computers to “see” like humans, and “image processing,” which is a
catch-all term for computers doing intensive work on image data.
3. How is it used ?
Computer views visuals as an array of numerical values and looks for patterns in the digital image, be
it a still, video, graphic, or even live, to recognize and distinguish key features of the image.
Computer vision uses Image Processing Algorithms to analyze and understand visuals from a
single image or a sequence of images.
With the advent of autonomous or semi autonomous cars, drones, wearables the potential of
computer vision is growing and many organizations are investing in image recognition to interpret
and analyze data coming primarily from visual sources for a number of uses such as medical image
analysis, identifying objects in autonomous cars, face detection for security purpose, etc.
Image recognition is the ability of a system or software to identify objects, people, places, and
actions in images. It uses machine vision technologies with artificial intelligence and trained
algorithms to recognize images through a camera system.
4. Techniques
The use of convolutional neural networks to filter images through a series of artificial neuron layers.
The convolutional neural network was specifically set up for image recognition and similar image
processing.
Through a combination of techniques such as max pooling, stride configuration and padding,
convolutional neural filters work on images to help machine learning programs get better at
identifying the subject of the picture.
A digital image represents a matrix of numerical values. These values represent the data associated
with the pixel of the image. The intensity of the different pixels, averages to a single value,
representing itself in a matrix format.
The information fed to the recognition systems is the intensities and the location of different pixels in
the image. With the help of this systems learn to map out a relationship or pattern.
5.
6. Limitations of Regular Neural Networks for Image
Recognition
The huge availability of data makes it difficult to process it due to the limited hardware
availability.
Difficulty in interpreting the model since the vague nature of the models prohibits its
application in a number of areas.
Development takes longer time and hence, the flexibility is compromised with the
development time. Although the availability of libraries like Keras makes the development
simple, it lacks flexibility in its usage. Also, the Tensorflow provides more control, but it is
complicated in nature and requires more time in development.
7. Challenges of Image Recognition
Viewpoint Variation: In a real world, the entities within the image are aligned in different directions
and when such images are fed to the system, the system predicts inaccurate values.
Scale Variation: Variations in size affect the classification of the object. The closer you view the
object the bigger it looks in size and vice-versa
Deformation: Objects do not change even if they are deformed. The system learns from the perfect
image and forms a perception that a particular object can be in specific shape only.
Inter-class Variation: Certain object varies within the class. They can be of different shape, size, but
still represents the same class. For example, buttons, chairs, bottles, bags come in different sizes and
appearances.
Occlusion: Certain objects obstruct the full view of an image and result in incomplete information
being fed to the system. It is necessary to devise an algorithm that is sensitive to these variations and
consist of a wide range of samples of the data
8. Recognition Methods
Optical Character Recognition
Pattern Matching & Gradient Matching
Face Recognition
License Plate Matching
Scene Identification or Scene Change Detection
Image Recognition Using Machine Learning : A machine learning approach to image recognition
involves identifying and extracting key features from images and using them as input to a machine
learning model. An example of this is classifying digits using HOG features and an SVM Classifier.
Image Recognition Using Deep Learning : A deep learning approach to image recognition may
involve the use of a convolutional neural network to automatically learn relevant features from
sample images and automatically identify those features in new images.
9. Role of Convolutional Neural Networks in
Image Recognition
Convolutional Neural Networks play a crucial role in solving the problems stated above. Its basic
principles have taken the inspiration from our visual cortex.
The inputs of CNN are not fed with the complete numerical values of the image. Instead, the
complete image is divided into a number of small sets with each set itself acting as an image.
A small size of filter divides the complete image into small sections. Each set of neurons is
connected to a small section of the image.
10. Uses of Image Recognition
Drones: Drones equipped with image recognition capabilities can provide vision-based automatic
monitoring, inspection, and control of the assets located in remote areas.
Manufacturing: Inspecting production lines, evaluating critical points on a regular basis within the
premises. Monitoring the quality of the final products to reduce the defects.
Military Surveillance: Detection of unusual activities in the border areas and automatic decision-
making capabilities can help prevent infiltration and result in saving the lives of soldiers.
Forest Activities: Unmanned Aerial Vehicles can monitor the forest, predict changes that can result
in forest fires, and prevent poaching. It can also provide a complete monitoring of the vast lands,
which humans cannot access easily.
Autonomous Vehicles: Autonomous vehicles with image recognition can identify activities on the
road and take necessary actions. Mini robots can help logistics industries to locate and transfer the
objects from one place to another. It also maintains the database of the product movement history
to prevent the product from being misplaced or stolen.
11. Latest Trends
Google Vision : can detect whether you’re a cat or a human, as well as the parts of your face.
It tries to detect whether you’re posed or doing something that wouldn’t be okay for Google
Safe Search—or not. It even tries to detect if you’re happy or sad. Google Cloud Vision API PI
enables developers to understand the content of an image by encapsulating powerful
machine learning models in an easy to use REST API.
Apple’s Face Recognition : Face ID uses a "TrueDepth camera system", which consists of
sensors, cameras, and a dot projector at the top of the iPhone display in the notch to create a
detailed 3D map of your face. It provides more privacy than Google Pixel and Samsung Galaxy
S series mobile phones.
12. Threats of Image Recognition
China facial recognition: Law professor sues Hangzhou Safari Park is violating consumer
protection law by compulsorily collecting visitors individual characteristics, after it suddenly
made facial recognition registration a mandatory requirement for visitor entrance.
Mass surveillance fears as India readies facial recognition system : As India prepares to
install a nationwide facial recognition system in an effort to catch criminals and find missing
children, human rights and technology experts warn of the risks to privacy from increased
surveillance.
US government keeps its use of facial recognition tech secret : The ACLU is suing FBI the
Department of Justice, and the DEA to get documents that explain how the US government is
using facial recognition technology.
13. Latest Research in 2019
Automatic Video Surveillance System For Pedestrian Crossing using Digital Image Processing
Remote Sensor Networks for Prediction of Chilli Crop Diseases using Infra-Red Image
processing techniques.
Natural Language Processing technique for Image Spam Detection
Food Image Processing Techniques