15,861 research outputs found
Relational Reasoning Network (RRN) for Anatomical Landmarking
Accurately identifying anatomical landmarks is a crucial step in deformation
analysis and surgical planning for craniomaxillofacial (CMF) bones. Available
methods require segmentation of the object of interest for precise landmarking.
Unlike those, our purpose in this study is to perform anatomical landmarking
using the inherent relation of CMF bones without explicitly segmenting them. We
propose a new deep network architecture, called relational reasoning network
(RRN), to accurately learn the local and the global relations of the landmarks.
Specifically, we are interested in learning landmarks in CMF region: mandible,
maxilla, and nasal bones. The proposed RRN works in an end-to-end manner,
utilizing learned relations of the landmarks based on dense-block units and
without the need for segmentation. For a given a few landmarks as input, the
proposed system accurately and efficiently localizes the remaining landmarks on
the aforementioned bones. For a comprehensive evaluation of RRN, we used
cone-beam computed tomography (CBCT) scans of 250 patients. The proposed system
identifies the landmark locations very accurately even when there are severe
pathologies or deformations in the bones. The proposed RRN has also revealed
unique relationships among the landmarks that help us infer several reasoning
about informativeness of the landmark points. RRN is invariant to order of
landmarks and it allowed us to discover the optimal configurations (number and
location) for landmarks to be localized within the object of interest
(mandible) or nearby objects (maxilla and nasal). To the best of our knowledge,
this is the first of its kind algorithm finding anatomical relations of the
objects using deep learning.Comment: 10 pages, 6 Figures, 3 Table
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Simultaneous Localization and Mapping (SLAM)consists in the concurrent
construction of a model of the environment (the map), and the estimation of the
state of the robot moving within it. The SLAM community has made astonishing
progress over the last 30 years, enabling large-scale real-world applications,
and witnessing a steady transition of this technology to industry. We survey
the current state of SLAM. We start by presenting what is now the de-facto
standard formulation for SLAM. We then review related work, covering a broad
set of topics including robustness and scalability in long-term mapping, metric
and semantic representations for mapping, theoretical performance guarantees,
active SLAM and exploration, and other new frontiers. This paper simultaneously
serves as a position paper and tutorial to those who are users of SLAM. By
looking at the published research with a critical eye, we delineate open
challenges and new research issues, that still deserve careful scientific
investigation. The paper also contains the authors' take on two questions that
often animate discussions during robotics conferences: Do robots need SLAM? and
Is SLAM solved
Domain independent goal recognition
Goal recognition is generally considered to follow plan recognition. The plan recognition problem is typically deïŹned to be that of identifying which plan in a given library of plans is being executed, given a sequence of observed actions. Once a plan has been identiïŹed, the goal of the plan can be assumed to follow. In this work, we address the problem of goal recognition directly, without assuming a plan library. Instead, we start with a domain description, just as is used for plan construction, and a sequence of action observations. The task, then, is to identify which possible goal state is the ultimate destination of the trajectory being observed. We present a formalisation of the problem and motivate its interest, before describing some simplifying assumptions we have made to arrive at a ïŹrst implementation of a goal recognition system, AUTOGRAPH. We discuss the techniques employed in AUTOGRAPH to arrive at a tractable approximation of the goal recognition problem and show results for the system we have implemented
Enhancing allocentric spatial recall in pre-schoolers through navigational training programme
Unlike for other abilities, children do not receive systematic spatial orientation training at school, even though navigational training during adulthood improves spatial skills. We investigated whether navigational training programme (NTP) improved spatial orientation skills in pre-schoolers. We administered 12-week NTP to seventeen 4- to 5-year-old children (training group, TG). The TG children and 17 age-matched children (control group, CG) who underwent standard didactics were tested twice before (T0) and after (T1) the NTP using tasks that tap into landmark, route and survey representations. We determined that the TG participants significantly improved their performances in the most demanding navigational task, which is the task that taps into survey representation. This improvement was significantly higher than that observed in the CG, suggesting that NTP fostered the acquisition of survey representation. Such representation is typically achieved by age seven. This finding suggests that NTP improves performance on higher-level navigational tasks in pre-schooler
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