How Self-Supervised CNNs are Unlocking Accurate Segmentation of Overlapping Objects
Recent developments in computer vision have made it possible to accurately segment overlapping objects in images. A research team from the University of Trento has developed a self-supervised convolutional neural network (CNN) capable of accurately segmenting overlapping objects. This breakthrough could lead to major advances in robotic vision, medical imaging, and other areas.
The team’s CNN model is capable of accurately segmenting multiple objects in an image without relying on manual annotations. This self-supervised approach eliminates the need for manual annotation, which can be time-consuming and expensive. The researchers also showed that their model performed better than existing methods on a standard dataset of overlapping objects.
This research could have significant implications for robotics, medical imaging, and other applications that require accurate segmentation of overlapping objects. The team plans to continue their research into self-supervised CNNs to further improve the accuracy of object segmentation.
source: Phys.org