Results 71 to 80 of about 254,373 (189)

Deep Geodesic Learning for Segmentation and Anatomical Landmarking [PDF]

IEEE Transactions on Medical Imaging, 2019
In this paper, we propose a novel deep learning framework for anatomy segmentation and automatic landmark- ing. Specifically, we focus on the challenging problem of mandible segmentation from cone-beam computed tomography (CBCT) scans and identification of 9 anatomical landmarks of the mandible on the geodesic space.
Neslisah Torosdagli, Denise K. Liberton, Payal Verma, Murat Sincan, Janice S. Lee, Ulas Bagci   +5 more
openaire   +3 more sources

Dense Face Alignment

, 2017
Face alignment is a classic problem in the computer vision field. Previous works mostly focus on sparse alignment with a limited number of facial landmark points, i.e., facial landmark detection.
Jourabloo, Amin, Liu, Xiaoming, Liu, Yaojie, Ren, William   +3 more
core   +1 more source

Cross-Task Representation Learning for Anatomical Landmark Detection [PDF]

, 2020
Recently, there is an increasing demand for automatically detecting anatomical landmarks which provide rich structural information to facilitate subsequent medical image analysis. Current methods related to this task often leverage the power of deep neural networks, while a major challenge in fine tuning such models in medical applications arises from ...
Fu, Z, Jiao, J, Suttie, M, Noble, JA
openaire   +2 more sources

Space Science Research in Africa: Publication Trends, Citation Analysis, and Collaborative Patterns

Earth and Space Science
Content assessment of research metrics plays a pivotal role in the evaluation of scientific productivity globally, especially in a selected field and region.
Babatunde O. Adebesin, Akeem B. Rabiu, Bolarinwa J. Adekoya, Elijah O. Falayi, Shola J. Adebiyi, Stephen O. Ikubanni, Tomiwa Akinyemi, Racheal F. Oloruntola, Mathew A. Duhunpar, Ayooluwa Aregbesola   +9 more
doaj   +1 more source

Unsupervised Landmark Learning from Unpaired Data

, 2020
Recent attempts for unsupervised landmark learning leverage synthesized image pairs that are similar in appearance but different in poses. These methods learn landmarks by encouraging the consistency between the original images and the images reconstructed from swapped appearances and poses.
Xu, Yinghao, Yang, Ceyuan, Liu, Ziwei, Dai, Bo, Zhou, Bolei   +4 more
openaire   +2 more sources

Discrete Multi-modal Hashing with Canonical Views for Robust Mobile Landmark Search

, 2017
Mobile landmark search (MLS) recently receives increasing attention for its great practical values. However, it still remains unsolved due to two important challenges.
He, Xiangnan, Huang, Zi, Liu, Xiaobai, Song, Jingkuan, Zhou, Xiaofang, Zhu, Lei   +5 more
core   +1 more source

Structure-Aware Shape Synthesis

, 2018
We propose a new procedure to guide training of a data-driven shape generative model using a structure-aware loss function. Complex 3D shapes often can be summarized using a coarsely defined structure which is consistent and robust across variety of ...
Balashova, Elena, Chen, Terrence, Funkhouser, Thomas, Singh, Vivek, Teixeira, Brian, Wang, Jiangping   +5 more
core   +1 more source

Visual motion-sensitive neurons in the bumblebee brain convey information about landmarks during a navigational task

Frontiers in Behavioral Neuroscience, 2014
Bees use visual memories to find the spatial location of previously learnt food sites. Characteristic learning flights help acquiring these memories at newly discovered foraging locations where landmarks - salient objects in the vicinity of the goal ...
Marcel eMertes, Laura eDittmar, Martin eEgelhaaf, Norbert eBoeddeker   +3 more
doaj   +1 more source

Learning to Use Visualizations (an example with elevation and temperature) [PDF]

, 2003
The purpose of this activity is to introduce students to visualizations as a tool for scientific problem-solving, using elevation and temperature as an example.
The GLOBE Program, UCAR (University Corporation for Atmospheric Research)
core  

Heliconius butterflies use wide-field landscape features, but not individual local landmarks, during spatial learning

Royal Society Open Science
Spatial learning is vital in foraging ecology. Many hymenopteran insects are adept spatial foragers that rely on visual cues contained within broader wide-field scenes for central place foraging from a central nest.
P. A. Moura, M. Z. Cardoso, S. H. Montgomery   +2 more
doaj   +1 more source