Semantic Segmentation is very useful for tasks that consist of image analysis. It is done by describing the process of linking each pixel of an image source with a class label (they can be named after objects like rocks, sky, etc.)
Applications are numerous for semantic segmentation which include Autonomous driving, medical surgeries, and land-area calculation to name a few.
There are various benefits of partitioning an image into multiple segments. The ultimate goal of segmentation is to simplify or change the representation of an image into something that is more meaningful and easier to analyse.
Each pixel of the image source must be labelled with a corresponding class of what it represents, this is the main goal for any semantic segmentation.
With dense prediction, the ability to pick out objects inside a crowded image becomes much more easier and gives an advantage to computer vision, where the model handles a complex image.
Semantic Segmentation has several applications that can be used for a variety of things, such as:
Applications are endless, so long as the creativity and thirst for knowledge remains, all knowledge can be integrated into one another, and it only requires an experienced eye to see it. Let Axximum AI help you on your journey towards the future.
Precision is everything when it comes to managing data annotations. Machine learning is tedious and requires precise & quality data.
Certified with SOC 2 TYPE 1, we maintain latest industry standards in terms of data security while working for our clients & ensuring privacy.
Automating our processes & utilising skilled professionals to deliver results on time no matter how large, a complete scalable solution.