activity
20192022
most citedU-Net Based Architecture for an Improved Multiresolution Segmentation in Medical Images

7 citations · 9 across the 4 of their papers we have counts for

collaborators

5 papers

eess.IV20221 cited

DoubleU-Net++: Architecture with Exploit Multiscale Features for Vertebrae Segmentation

Simindokht Jahangard, Mahdi Bonyani, Abbas Khosravi

Accurate segmentation of the vertebra is an important prerequisite in various medical applications (E.g. tele surgery) to assist surgeons. Following the successful development of d…

cs.CV2021

Predicting Driver Intention Using Deep Neural Network

Mahdi Bonyani, Mina Rahmanian, Simindokht Jahangard

To improve driving safety and avoid car accidents, Advanced Driver Assistance Systems (ADAS) are given significant attention. Recent studies have focused on predicting driver inten…

cs.CV20201 cited

Persian Handwritten Digit, Character and Word Recognition Using Deep Learning

Mehdi Bonyani, Simindokht Jahangard, Morteza Daneshmand

Digit, letter and word recognition for a particular script has various applications in todays commercial contexts. Nevertheless, only a limited number of relevant studies have deal…

eess.IV20207 cited

U-Net Based Architecture for an Improved Multiresolution Segmentation in Medical Images

Simindokht Jahangard, Mohammad Hossein Zangooei, Maysam Shahedi

Purpose: Manual medical image segmentation is an exhausting and time-consuming task along with high inter-observer variability. In this study, our objective is to improve the multi…

cs.CV2019

In Situ Cane Toad Recognition

Dmitry A. Konovalov, Simindokht Jahangard, Lin Schwarzkopf

Cane toads are invasive, toxic to native predators, compete with native insectivores, and have a devastating impact on Australian ecosystems, prompting the Australian government to…