Abstract: Simultaneous localization and mapping (SLAM) enables robots to localize in uncertain environments and has been widely used in the field of robotics. However, traditional vision SLAM systems ...
Abstract: Data annotation in medical image segmentation is time-consuming and expensive. Semi-supervised learning (SSL) presents a viable solution. However, unlike organ segmentation, current ...
Abstract: Human trafficking is a serious problem that requires new ideas and data-driven approaches to identify and solve. In this study, we analyze large datasets to identify patterns and anomalies ...
Abstract: The fast growth of internet and communications networks has drastically enhanced data transport, allowing tasks like Speech Emotion Recognition (SER), an essential aspect of human-computer ...
Abstract: Remote sensing image segmentation is a fundamental task in Earth observation. Rapid development has been made in the past decade owing to the deep learning techniques. Most of the existing ...
Abstract: Glaucoma, a leading cause of irreversible blindness, requires precise segmentation of the optic disc and optic cup in fundus images for early diagnosis and progression monitoring. This study ...
Abstract: The poultry industry has been driven primarily by broiler chicken production and has grown into the world’s largest animal protein sector. Automated detection of chicken carcasses on ...
Abstract: The integrated Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS) system has been widely used in vehicular positioning and navigation. However, the complex ...
Abstract: This study proposes a robust and efficient two-stage deep learning framework aimed at the accurate classification of Chest X-ray images into NORMAL and PNEUMONIA categories. The methodology ...
Abstract: The shape and size of the placenta are closely related to fetal development in the second and third trimesters of pregnancy. Accurately segmenting the placental contour in ultrasound images ...