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.
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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.
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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.
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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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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What is image processing?
Any field in which images must be altered, edited, and evaluated relies heavily on image processing and computer vision. Remote sensing, medical imaging, autonomous vehicle navigation, and other applications rely on them. Images are typically used as the input and output to image processing operations. In contrast, computer vision usually works with input photos and produces a scene description or categorization as an output. In practice, image processing is performed as low-level computer vision operations, with the input images being filtered before high-level computer vision reasoning is performed.
What is the importance of image processing?
The significance and requirement of digital image processing originates from two main application areas: the enhancement of input image for human interpretation and the processing of scene data for autonomous machine perception. Remote sensing, picture and data storage for transmission in corporate applications, diagnostic imaging, acoustic imaging, forensic sciences, and industrial automation are only few of the applications of digital image processing. Satellite images are useful for tracking earth resources, topographical mapping, and agricultural crop prediction, as well as weather prediction, flood and fire management.
What is analogue and digital image processing?
Analogue and digital image processing are the two types of image processing methods employed. Hard copies, such as prints and photographs, can benefit from analogue image processing. When employing these visual tools, image analysts employ a variety of interpretive fundamentals. Digital image processing techniques allow for computer-assisted alteration of digital images. Pre-processing, augmentation, and presentation, as well as information extraction, are the three general processes that all sorts of data must go through when using digital techniques.