Image Segmentation

Product Highlights

We provide a solution for automated image segmentation.

The problem

Image segmentation is an application in the Computer Vision domain in which we partition an image into different segments. The goal is to represent the image with easy-to-analysis patterns. In the process, image segmentation aims to assign labels to image pixels, e.g., categorize multiple areas. In the end, usually, the areas with common characteristics will be grouped. Segmentation is done in various applications, such as medical image segmentation.

What makes the problem a problem?

In segmentation, we basically try to distinguish patterns and extract information from a complex image structure. To the human eyes, it might look easy! But, it's far from easy if you think about it in terms of implementation and automation. Deep learning demonstrated superior performance for image segmentation, BUT there is no one-for-all solution. Any application has its own issues, and there is a need for a new design for each case.

Who benefits from this?

Many industry stakeholders are interested in having an image segmentation system. However, for that, they are bound to expensive commercial software and tedious human labeling. Our goal is to provide an automated segmentation system as a service with a much lower price compared to standard alternatives.

The Process

step 1


The customers pass their test data that they desire to be segmented. If similar public domain data is not available, the customers need to pass their data for training purposes. The data will be stored in a secure location on a local system and secure servers with the customer's approval. The segmentation data needs to be labeled for segmentation purposes.

step 2


Instill AI use the data to train a model based on in-house algorithms to produce a useful model. Once the model is trained, the model will be used to evaluate the test data.

step 3


Once the test results were obtained and validated, they will be returned to the customer with proper documentation. After confirmation of satisfactory results, the data will be removed from the secure storage.


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