The process of converting an image into a digital format and carrying out specific procedures to extract some useful information from it is known as Image Processing or Digital Image Processing. The digital image can be made available in any format such as improved image, X-Ray, photo negative, etc. Image Processing helps to improve the interpretation of images for humans. Information extracted from image processing can be used for machine interpretation. The density and contrast of the image’s pixels can be adjusted according to the need. Images can be easily stored, found, and transmitted electronically to a third party.
Applications of Image Processing
- Image sharpening, contrasting, and restoration
- Medical technology
- Filters for editing apps and social media
- Machine vision and computers
- Video processing
- Pattern recognition
- Advancing traffic sensing technology
- Face detection
- Fingerprint detection
- Processing aerial and satellite images for weather prediction
- Pollution particle study
- Machine vision for product assembly and quality control
Image processing can be done using programming languages like python, Matlab, C++, and java. Python and Matlab are popular programming languages for image processing projects. Here in this article, we will talk about image processing projects using Matlab and image processing projects using python
Top 5 Image Processing Projects Using Matlab
Below are the project topic ideas on image processing projects using Matlab for students and beginners who want to get a hands-on experience in image processing.
Fingerprint recognition
It uses a specific type of biometric technology to compare the fingerprints of two individuals. These are primarily used to determine a person’s consistency and uniqueness. Digital image processing, or DIP, allows for the manipulation of images in a computer, and it can be done by creating an algorithm that uses a computer to process the images. It is widely applied in the processes of feature extraction, pattern identification, morphology, and segmentation in digital images.
Character recognition
Popularly known as an optical character otherwise (OCR). It is used to translate electronic or mechanical images drawn by hand, written by hand type otherwise printed text which is edited through the machine. This type of document recognition is used for numerous document input tasks, it is a practical and affordable technique for recognizing the characters.
Speech recognition
Nowadays speech technologies have advanced substantially. Through the voice user interface, humans and machines can communicate. Cross-correlation was used in MATLAB to analyze multiple signals and identify the most precise one amongst them while this method was being developed. Cross-correlation is mostly used in this situation to determine how similar the recorded signal and the tested signal are. In order to work with signals that machines can distinguish between, this technique is used.
Attendance marking system
The traditional attendance marking system had its own drawbacks and the automated attendance marking system has a solution for it. Matlab image processing uses two techniques Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG) to recognize the faces of individual students and compare them with the stored data in a database to mark the attendance. Once the student’s face is recognized it is marked as present and it will extract the data in an excel sheet automatically.
Fake Currency Detection
Fake currency detection using Matlab can be done by employing methods like image processing, edge detection, segmentation of the image, and comparing both images. Matlab includes all the necessary libraries and functions to develop fake currency detection applications. It is high in demand in the banking sector, hospitals, government offices, etc.
Top 5 Image Processing Projects Using Python
Here is the list of top project topic ideas on image processing projects using Python for students and beginners to get practical disclosure to image processing.
Smart Traffic Light System
Everything is becoming smart and so can traffic lights. Standing for long at a traffic signal on a hot sunny day when there is no traffic congestion can be so annoying. The live image feed is taken to decide the traffic density and horn sounds are also taken into consideration, using OpenCV a vigorous system can be developed. This system will give frequent green signals when the traffic congestion is low and more controlled flow when there is heavy congestion. A preference for allowing emergency vehicles to go through signals faster can also be added.
Drowsiness Detection for Drivers
Drowsiness detection is a safety feature that can prevent accidents that are caused by drivers who fell asleep while driving. This project aims to create a drowsiness detection system that can recognize when someone’s eyes are closed for a brief period of time. When fatigue is found, this system will warn the driver. In order to determine if a driver’s eyes are “Open” or “Closed,” a Deep Learning model will classify the webcam photos gathered using OpenCV.
Air Pollution checker
The only concern where the whole world stands united. Everyone is doing their best to improve AQI. An air pollution checker can be one of the best projects for beginners in image processing. You will require a sizable dataset of images with a cloudy, polluted, and clear sky for this project. Some of it can be found on Kaggle. Depending on the deployment, the user’s image would be the application’s input, and the application would process it before displaying the results on the screen.
Cancer Detection
The abnormal growth of skin cells known as skin cancer can be fatal. However, a dermatologist can treat and get rid of it if it’s detected early. Applying Python’s TensorFlow framework. Benign and malignant skin conditions can be categorized, which may assist a Dermat in earlier diagnosis of cancer.
Image Editor
Python can also be used to create an image editor application from scratch. Tkinter, a Python GUI library, can be used to create the GUI for this. All the operations, including edit, crop, color change, background blur, image merging, and rotation, resizing, and dragging can be customized using Numpy and OpenCV. Creating the routines for all the necessary procedures may take a long time, but this project will give you a concrete understanding of image processing.
Conclusion
In this article, we’ve seen 5 topics each for image processing projects using Matlab and Python that demonstrate how image processing can be used to address a specific problem in society and businesses. However, we urge you to recognize more crucial issues that can be resolved by utilizing the principles of image processing using Matlab and Python. To sum up, creating algorithms for image processing involves skills, and if you master them, you can grow professionally while resolving issues in the real world.