In this blog, we will walk through the introduction of image processing and then proceed to talk about a few project ideas that revolve around image processing.
Image processing is a technique used to perform some operations on the image in order to obtain some meaningful information from them. Here, the input will be an image and after applying a few operations we get an enhanced image or some features associated with these images.
In image processing, an image is considered as a two-dimensional array of numbers ranging from 0 to 255. Image compression, sharpening, edge-detection are all achieved by using special filters and operators that transform the input image to the output we wish to achieve. For instance, for brightening the image, the operator or filter will behave in a manner that would increase the pixel value of the image.
These operators perform mathematical operations with the 2-D array and produce a new set of output arrays with the desired result. These operations are being extensively used in domains like, Computer vision and Artificial Intelligence, and Machine learning.
Moving on, now that we have a basic understanding of what is image processing let us dive into some of the project ideas that can be created by leveraging the aforementioned concept on image processing.
Top Image Processing Project Ideas
1. Monitoring Social Distancing
With COVID-19 spreading universally, it is prominent to maintain social distancing while travelling in public places. Here image processing can be a game-changer. By taking input from CCTV Cameras and analyzing one frame at a time we will achieve the task at hand.
Firstly, we use morphological operations and detection techniques to detect pedestrians in a frame. Next, we draw a bounding box surrounding each pedestrian. After which, we calculate the distance of one bounding box enclosing a pedestrian to its adjacent bounding boxes. Next, we decide a threshold for the distance between the bounding boxes and then based on their distance we categorize the pedestrians in the frame as red, yellow, or green.
The red bounding box would mean people in the frame are very close together and therefore at maximum risk. The yellow box would mean that the people are at a considerable distance and the risk is medium. The green boxes would mean people are following the norms and they are safe. Integrating this system with an alerting mechanism (Loudspeakers)could be a great way to alert the pedestrians violating the COVID-19 norms!
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2. Mask Detection
Nowadays, wearing masks have been mandatory since the pandemic was discovered. As social distancing, mask detection is equally important to prevent any further surge in COVID cases. To detect mask. we need to first detect the human face. That can be achieved by identifying the facial landmarks such as eyes nose mouth etc. After detecting faces, we need to build an algorithm that can distinguish a face with a mask and a face without a mask.
This calls for the need for a deep learning model. Training a deep learning model on datasets comprising of both mask and non-mask images. Once the model is trained it will be able to successfully identify mask and no-mask people. Using this, we can alert pedestrians to wear masks whenever they step out of their house.
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3. Lane and Curve Detection
Autonomous vehicles are the future of driving. With the aim to minimize human intervention and also the potential risk involved, many companies are spending extensively on the Research and Development of autonomous vehicle technologies. By using image segmentation for filtering and edge detection with a deep learning model we detect the presence of lane and their orientation.
A stepwise procedure would look like this
- Taking input video as frames.
- Converting each frame into its corresponding grayscale image.
- Reducing the prevalent noise with the help of filters.
- Detecting edges using a canny edge detector.
- Finding the coordinates of the road lanes.
- Using deep learning to efficiently detect lanes and their orientation.
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4. Drowsiness Detection for Drivers
The need for drowsiness detection in vehicles is necessary owing to the large number of accidents caused due to lack of consciousness amongst drivers. With a drowsiness detection system, it can alert the driver if it senses a potential loss of consciousness in the eye of the driver. By understanding and analyzing eye patterns, this system can proactively alert the driver and prevent the occurrence of accidents. This task is achieved by first locating and segmenting the eye portion from the rest of the face.
Then binarization and labelling of images are done so as to understand which images represent the occurrence of drowsiness and which don’t. Then by analyzing the blinks and their duration, the algorithm can detect drowsiness if the eyes are closed for a longer time than the time taken to blink the eye. By integrating this system with an alerting device, it could be useful in mitigating the accidents caused due to lack of consciousness.
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5. License Plate Recognition
Yes, you heard it right, we can automate the license plate detection. Now the traffic police no longer need to manually pen down the license number of the vehicles violating the traffic rules. Thanks to the advancements in the field of image processing that such a task is possible. The steps that are required for license plate detection include- using appropriate filters to remove noise from the input image and then applying morphological operations on them.
Further, on the region of interest i.e the license plat, we apply a technique known as Optical Character Recognition (OCR)to extract text from the images. OCR is a pretrained network that is capable of detecting text from images. Using it directly will help us save the computation cost of training our algorithm by ourselves. Therefore, by following the above steps systematically, one can develop an algorithm/model to identify the license plate and the number associated with it.
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Image Recognition using Deep Learning
Image recognition, a significant branch of computer vision, involves identifying and classifying objects or patterns within digital images. Deep learning techniques, particularly Convolutional Neural Networks (CNNs), have revolutionized image recognition tasks, achieving state-of-the-art performance across various applications.
CNNs are designed to mimic the human visual system, consisting of multiple layers that automatically learn relevant features from images. Here is a step-by-step guide on building an image recognition project using a popular deep learning framework like TensorFlow or PyTorch:
- Data Collection: Obtain a labelled dataset of images relevant to your recognition task. For instance, if the project aims to classify cats and dogs, gather a diverse collection of cat and dog images.
- Data Preprocessing: Resize all images to a consistent resolution and normalize pixel values to improve training efficiency. Split the dataset into training and testing sets to evaluate model performance accurately.
- Model Architecture: Choose a CNN architecture suitable for image recognition. Common choices include VGG, ResNet, or MobileNet. You can also design a custom model tailored to your specific project requirements.
- Model Training: Use the training dataset to train the CNN. During training, the model adjusts its internal parameters to minimize the prediction error. This process involves forward propagation, loss computation, and backpropagation to update weights.
- Hyperparameter Tuning: Experiment with different hyperparameter values, such as learning rate and batch size, to optimize model performance.
- Evaluation: Assess the trained model using the testing dataset. Calculate metrics like accuracy, precision, and recall to gauge how well the model generalizes to new, unseen images.
7. Deployment: Once satisfied with the model’s performance, deploy it to make real-time predictions on new images.
Image Enhancement and Restoration
Image enhancement and restoration techniques aim to improve the visual quality of images by correcting imperfections caused by noise, low resolution, or other factors. These techniques find applications in various fields, including medical imaging, satellite imagery, and historical photograph restoration.
- Contrast Adjustment: This technique enhances the difference between light and dark regions in an image, making it visually more appealing and improving object visibility.
- Denoising: Noise is an inevitable part of image acquisition and transmission. Denoising methods, such as median filtering and wavelet denoising, effectively reduce unwanted noise while preserving image details.
- Super-Resolution: Super-resolution techniques reconstruct high-resolution images from their low-resolution counterparts, enhancing image clarity and sharpness.
Image enhancement and restoration methods find practical use in numerous real-world projects:
- In medical imaging, enhancing the visibility of anatomical structures helps physicians make accurate diagnoses and treatment decisions.
- In satellite imagery, denoising can improve the quality of remote sensing data, leading to better analysis and interpretation of the Earth’s surface.
- In historical photograph restoration, these techniques aid in preserving old images by reducing degradation effects and restoring missing details, allowing for a glimpse into the past with improved clarity.
As technology advances, image enhancement and restoration continue to play a crucial role in enhancing visual information and enabling a wide range of applications across various industries.
Until now, we have seen 5 examples where image processing can be applied to solve the issue at hand. However, let me tell you that image processing has diversified into almost every industry almost every field is dependent on it directly or indirectly. Because it uses python as its programming language, it is convenient to use and easier to understand.
This post gives you an overview as to what is image processing and few projects associated with it. However, we do encourage you to identify more pressing problems that can be solved by leveraging the concepts of image processing.
To conclude, developing algorithms pertaining to image processing requires skill and if mastered can help you advance in your professional life at a rapid pace whilst solving real-world problems.
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