OSC-CO²: coattention and cosegmentation framework for plant state change with multiple features.

ABSTRACT: Cosegmentation and coattention are extensions of traditional segmentation methods aimed at detecting a common object (or objects) in a group of images. Current cosegmentation and coattention methods are ineffective for objects, such as plants, that change their morphological state while being captured in different modalities and views. The Object State Change using Coattention-Cosegmentation (OSC-CO²) is an end-to-end unsupervised deep-learning framework that enhances traditional segmentation techniques, processing, analyzing, selecting, and combining suitable segmentation results that may contain most of our target object's pixels, and then displaying a final segmented image. The framework leverages coattention-based convolutional neural networks (CNNs) and cosegmentation-based

SUBMITTER: Quinones R 

PROVIDER: S-EPMC10644038 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature