With cameras, Webcams, Optical Sensors, etc., becoming more common these days, we are faced with an influx of large amounts of data in the form of images and videos. While pictographic evaluation of any information is always easier, one has to agree that the processing of such a large chunk of imagery is easier said than done. Machine Learning is currently a hot topic for research and experimentation, and it essentially involves making the system learn details which it can recognize on subsequent encounters, and thereby process that data very quickly.
Combining the advances in Machine Learning with this rising problem of image processing has led to the creation of a highly efficient streamlined system where Machine Learning models expedite the classification, segmentation, and recognition of images, videos, footage, etc.
With this blog, we aim to help you dive deep into the advanced Machine Learning methodologies that are being used for image and video processing. Thus, let’s get down to learning more about this strong association between image processing and machine learning.
The processing of data in images and documents using Artificial Intelligence (AI) has become a necessity for most businesses today. Automated extraction of data from structured or unstructured images and documents followed by analysis and delivery of actionable insights has completely changed the functioning of all working sectors today. Practical machine learning and image processing pdf documents is currently the most efficient way of intelligent document processing.
With intelligent data processing, operations become automated, compliance becomes easy, and scalability becomes more achievable. In the long run, the improved efficiency also increases your savings by cutting down on unnecessary expenditure.
Deep Learning is an advanced subset of Machine Learning. Deep Learning models make use of Deep Neural Networks to aid Intelligent Data Processing. Deep Neural Networks comprise of the advanced algorithms that can help in recognizing graphics, implementing commands, and even performing an expert review for image processing to take place. Deep Neural Networks make predictions and draw conclusions very efficiently to help the user process images for highly accurate results. You can learn more about practical machine learning and image processing on GitHub.
Convolutional Neural Networks (CNN) is a type of Neural Network that is most commonly used for image recognition and processing. A Convolutional Neural Network processes the entire image block-by-block, starting from the upper left corner, and then moving ahead pixel-by-pixel to complete the verification. The final results of every such block verification are passed through a convolutional layer, where each data element is connected. This ‘passing through the layers’ leads to the production of a result from the verification data, with which the system ultimately identifies the image and its depiction.
The processing of images using machine learning models are reaping wonderful benefits in the following sectors:
Machine learning algorithms are making life easier in the Healthcare industry when it comes to image processing. Experts are no longer dependent on manual analysis to spot and diagnose conditions using radiographic imaging. X-rays for instance can be subjected to an automated image analysis where deep learning models identify anomalies within seconds to help practitioners plan the future course of action. Imaging and videography techniques used in surgical procedures can also be benefitted by Machine Learning in the same way.
Agriculture can also benefit from the amalgamation of Machine Learning models with image processing. Images of crops and yields can be analyzed in an automated manner to identify any shortcomings or substandard quality in the produce. For the farmer, imaging of the field followed by analysis by ML models can help identify weeds effectively, and thus, the farmer can eliminate them from the field before they cause any damage.
Sometimes conditions become so hostile that troops cannot practically enter a location because of the fear of attacks and casualties. In such situations of uncertainty, drone-based image capturing followed by analysis with Deep Learning models can prove highly efficacious. Within seconds, the defence team can identify any irregularities within an area, and then nab the suspects right away with highly accurate information.
Waste segregation using image processing and machine learning can help us in properly managing and disposing of the growing amounts of waste, especially in urban localities. Hardware called a waste segregator can be prepared which is backed by Artificial Intelligence. The image capturing followed by processing using Machine Learning models can help in identifying the type of waste. Then, with the hardware, waste can be physically segregated based on this data. Thus, automatic waste segregation using image processing and machine learning becomes possible.
KlearStack has developed a state-of-the-art AI-backed solution for intelligent data processing that simplifies images for human interpretation. Our image processing algorithms are highly effective in adapting to changing image processing needs. KlearStack’s approach is unlike other basic machine learning solutions that give way once the data becomes complex and the quantity becomes large. Therefore, for any number of images, documents, etc. KlearStack’s advanced solutions can automatically analyze the data and deliver relevant insights within seconds.