The main intention of digital image processing (DIP Projects) is to perform some manipulation on the raw image either to amplify/testify the image or to extract the essential features from an image simply. This method obviously increases the quality and sharpness of the image in all aspects.
In addition, it is referred to as image-based signal processing, which takes an image as input and produces an image/part of the image as output where the part represents the features. DIP is the most advanced blooming technology that acts as the kernel of CS and other engineering disciplines in this day and age.
This page discusses the upcoming research areas for incredible DIP Projects!!!
What is Digital Image Processing?
In general, image processing is classified into two major types as analog and digital. On the one hand, analog image processing performs on the 2D analog signal, which is appropriate for photographs and printouts. In the case of applying image visualization, image analysts will utilize different approaches for human/machine interpretation. A digital image is represented in the form of a matrix, which is said to be an image as a finite set of pixel values. Also, DIP focuses on the following two major tasks.
- Enhance the human understand ability by improving the image details
- Improve the machine understand ability without human involvement by image processing, storage, representation, visualize and transmission approaches
As a matter of fact, DIP utilizes several heterogamous algorithms to manipulate the digital image for retrieving the required results. Majorly, it endures the feature extraction, preprocessing, and enhancement techniques in the digital computer for all the image processing applications. For your reference, here we have given the few vital classic image processing algorithms which are commonly used in DIP Projects.
What are the algorithms used in image processing?
- Wavelet Transform Algorithm
- Morphological Algorithm
- Convolutional Neural Network (CNN)
- Gaussian Blur / Filter Algorithm
- Canny Edge Detection Algorithm
- Fast Fourier Transform (FFT) Algorithm
- Generative Adversarial Networks (GAN)
If the problem is dealing with the beginning stage, set the controlled environment based on certain constraints like illumination and background. Similarly, if the same problem is dealing with an advanced level, then set an uncontrolled environment. Ultimately, it will be good practice for designing new algorithms. When modelling new ML-based algorithms, you need to do more experiments for best results, and the following are the important things to follow in conducting experiments.
- Train, Test, and Validate the data
- Model hyperparameters and utilize other models
- Execute the model in multiple environs
Furthermore, our developers have given you some course of action to prepare the data for implementing your DIP Projects. Make a note of it while working with input data.
- Require large input data to model and also it is best to acquire the better outcome
- Assure the quality of the acquired image dataset for more clear insight. For that, it uses deeper neural networks
- At the time of feeding CNN, RGB images are transformed into grayscale
Top Image Processing DIP Projects Idea
For illustration purposes, we have given real-time applications. For that, we have handpicked interesting dip project ideas such as driver drowsiness detection, license plate recognition, and COVID 19 pandemic situations (monitoring social distancing and mask detection), and driverless driving. Let’s see these applications in detail.
Drowsiness Detection for Drivers
- Get the input image and locate the eye portion from the driver’s face
- Implement suitable algorithm for segmentation
- Next, label and binarize the image to distinguish drowsiness and normal face
- Then, analyze the blink duration. If it takes more time to blink, then alert the drivers to through alerting device
License Plate Recognition
- Get the input from CCTV Cameras
- Remove the noise in the image through suitable filter techniques
- Implement OCR and morphological operations to recognize the text and number from license plate
Monitoring Social Distancing
- Get the input from CCTV Cameras for monitoring social distancing and inspect a single frame at a time
- Next, find the pedestrians in a frame by applying detection and morphological algorithms
- Then, set the threshold value for distance and draw a bounding box over every pedestrian to determine the distance between the neighboring bounding boxes
- After all, classify the pedestrians into three classes as red, yellow, or green to signify the distance level based on threshold
Mask Detection
- At first, detect the face of the human through facial features (nose, mouth, eyes, and ears)
- Next, implement the technologies that differentiate the face with/without mask
- Then train the models to classify people into mask people and no-mask people
- Alert the pedestrian to wear the mask through a warning message
Lane and Curve Detection
- Reduce the human involvement and challenges in driverless vehicle driving
- Implement filter operations for image segmentation
- Apply deep learning model for detecting the edges
- Based on the result of the above methods, detect the presence of lanes and curve in the highways
Several programming tools are designed with sophisticated toolboxes, libraries, and packages for developing image processing projects. Choosing a specific tool is also an art of development because the tool has different capabilities. Based on the Dip projects need, one should select the tool. Some of the important tools are Matlab, ENVI, WinSPT, ImageJ, and many more. Further, let’s glance at commonly used Python libraries that give precise results in manipulating the images.
Digital Image Processing Tools
Mahotas
Mahotas is a library with 100+ methods to handle image processing and computer vision operations. These functions and algorithms are applied in C++, which mainly depends on C++ compilers. As a result, the numerical calculations are executed without the NumPy module. Most importantly, it is an independent module with the least dependencies. Here are names of some of the remarkable algorithms available in Mahotas for dip projects:
- Convolution
- Watershed Transform
- Spline interpolation
- Colorspace Conversions
- Hit and miss Transform
- Speeded-Up Robust Features (SURF)
- Thresholding
- Scale Invariant Feature Transform (SIFT)
- Simple Linear Iterative Clustering (SLIC) superpixels
- Morphological Operations (thinning and thickening)
Let’s look at some of the operations that could be done using Mahotas:
- locmax() – Local maximum points in the image
- imread () – Read image as input
- mean () – Compute the mean value
- erode () – Image erosion (morph module)
- dilate () – Image dilation (morph module)
- eccentricity () – Calculate the length of the shortest path through a connected graph (feature module)
OpenCV
Open Source Computer Vision Library is shortly termed as OpenCV, which comprises more than 2000 optimized procedures for handling ML and CV techniques. Below, we have listed few image processing approaches that functioned efficiently in OpenCV for dip projects.
- Execute morphological functions on images
- Construct image pyramids with high resolutions
- Blurring and Smoothing images (For instance: custom filters)
- Segmenting the image based on constraints (using a watershed algorithm)
- Carrying out thresholding on images (For example Adaptive or Simple thresholding)
- Extract the essential foreground content in an image (using GrabCut algorithm)
- Transforming color image into another form through color space (For instance: BGR, gray, BGR, and HSV )
Scikit-image
Scikit-image is a comprehensive open-source image processing library used for image preprocessing. Using this library, we can easily tackle the complexity in the image through machine learning approaches. Also, it is a simple library that has the provision to operate with NumPy arrays for dip projects. Some of the image processing operations are given below,
- Morphology Module
- binary_erosion () and binary_dilation () – Morphological processes
- Exposure Module
- equalize_hist () – Histogram equalization
- equalize_adapthist () – Adaptive equalization
- Filter Module
- try_all_threshold () – Thresholding processes which comprise 7 worldwide thresholding algorithms
- sobel () – Edge detection which transform image into 2D grayscale
- Gaussian () – Gaussian smoothing
- Transform Module
- rotate() – Image Rotation
- rescale() – Image rescaling the image use function from the transform module
Overall, if you are seeking for best research solutions, then communicate with our team. We will give you comprehensive support with innovative trending dip projects guidance throughout the journey.
