In the last two decades, the research and development in the field of artificial intelligence have expanded manifold across the globe. It is under this targeted approach of scientists that newer methodologies have been developed for computer vision also. The traditional computer vision technology has long been forgotten with several changes being incorporated at a fast rate. Image segmentation is one of the techniques that computer vision systems have to utilize to allow machine learning models to understand images in a better way. Here’s a detailed guide about image segmentation deep learning and how it has found application in different industries.
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What Is Image Segmentation?
Image segmentation is one of the most important manifestations of computer vision technology. It refers to the partitioning of images or video frames to help in the deep understanding of every aspect of the image to train machine learning models. Image segmentation has always been an important task to support automation in industries.
Earlier technologies like MATLAB were used for this purpose, which was gradually replaced by traditional computer vision techniques. Today with a better understanding of deep neural networks, developers are using Deep Learning to facilitate image segmentation on a large scale. Overall, image segmentation refers to the pixel-level classification of different regions of an image. Semantic segmentation is another common term that you may come across, which refers to assigning labels to every pixel of an image so that it can be described in a better way.
Image Segmentation and Image Classification
Image classification and image segmentation are two minutely different tasks that are widely being automated today. Any artificial intelligence system like the Google Vision AI helps users in attaching labels to every image based on its characters, therefore, allowing easy categorization.
On the other hand, image segmentation is required when you want to know what every different pixel of the image is representing. Machine learning image segmentation comes into the picture when a detailed analysis of an image has to be done, to find something which is otherwise not very apparent or obvious at the first glance.
Uses Of Image Segmentation Deep Learning
● Biometric Recognition
Image segmentation can be roughly described as an object-class recognition drill, which ultimately helps in the categorization of images. Such technology is very beneficial in facial recognition and biometrics. The whole point of having a biometric database is that these are the only details of the human body that set every individual apart. Using image segmentation deep learning models, one can train machine learning models to identify specific biometric characters and recognize faces or thumbprints for individual identification.
● Medical Imaging
Medical image segmentation deep learning is coming up in a big way, with widespread acceptability in the medical fraternity. In many cases, even when the radiological imaging results are out, it is somehow difficult for the naked eye to spot deviations from normal anatomy. When the image is segregated into different regions, all of which are analyzed through machine learning models, the job of the practitioner becomes easier. The automation not only cuts down the time taken to arrive at a diagnosis but also ensures that it is much more accurate.
● Industrial Imaging
Quality control in industries is a very important aspect of business, considering how heavily industries are fined by authorities for not meeting the standards. But many times, even after the standard production methods are followed, the final product may have some faults that are difficult to pinpoint at the end.
With image segmentation deep learning, the images of the final products can be subjected to thorough computer analysis. Machine learning models can be trained to visualize the details of the standard product which is acceptable in the market. Comparing the standard details with every new unit manufactured at the plant individually, one can ensure that the quality is always up to the mark without having to check every piece manually.
● Space And Environmental Imaging
While we already have very high-resolution cameras that take brilliant pictures of objects in space, the fact that detailed analysis of these pictures requires a lot of time and expertise cannot be undermined. It is probably one of the biggest challenges faced by scientists in exploring something new from these pictures. In the most elite space organizations of the world, image segmentation deep learning is being utilized to ensure that every region or aspect of the images clicked during space missions is scrutinized and anything valuable does not get missed.
Environment protection has become a very big concern for the world today. By taking pictures of forests, mountains, etc., and then making deep analysis using image segmentation, we can arrive at conclusions regarding the degree of damage that human Deeds have caused to natural surroundings.
KlearStack Advantage For Image Segmentation Deep learning
KlearStack has actively been involved in incorporating the advancing trends of deep learning into common operational tools in the current workspace. Under our endeavors, we have managed to combine deep learning capabilities with the standard Optical Character Recognition technology. With our OCR software, users get to witness highly accurate outputs where deep learning and computer vision provide errorless outputs with a better understanding of details that helps in the generation of actionable insights.
Very different from traditional optical character recognition software, the KlearStack solution performs in-depth analysis of every detail of an image, or a PDF file to convert it into a digital form and place it before the users. Hundreds of commercial workspaces have turned to KlearStack for its state-of-the-art optical character recognition solution. To know more about our work and contributions, contact the KlearStack team today.