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One of the key reasons for the growing interest in machine learning systems is the problems they can solve in computer vision. New neural network architecture will make it possible to perform image segmentation on computing devices with low-power and -compute capacity.

Segmentation is the process of determining the boundaries and areas of objects in images. We humans perform segmentation without conscious effort, but it remains a key challenge for machine learning systems. It is vital to the functionality of mobile robots, self-driving cars, and other artificial intelligence systems that must interact and navigate the real world.

Until recently, segmentation required large, compute-intensive neural networks. This made it difficult to run these deep learning models without a connection to cloud servers.

Artificial intelligence researchers at DarwinAI and the University of Waterloo have managed to create a neural network – AttendSegthat – that provides near-optimal segmentation and is small enough to fit on resource-constrained devices. 

Some of the most common applications of machine learning in computer vision include image classification, object detection, and segmentation.

Image classification determines whether a certain type of object is present in an image or not. Object detection takes image classification one step further and provides the bounding box where detected objects are located, as reported by bdtechtalks.com.