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- Chen, Zetao, et al.
(författare)
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Distance metric learning for feature-agnostic place recognition
- 2015
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Ingår i: Proceedings of the 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). - : IEEE. ; , s. 2556-2563
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Konferensbidrag (refereegranskat)abstract
- The recent focus on performing visual navigation and place recognition in changing environments has resulted in a large number of heterogeneous techniques each utilizing their own learnt or hand crafted visual features. This paper presents a generally applicable method for learning the appropriate distance metric by which to compare feature responses from any of these techniques in order to perform place recognition under changing environmental conditions. We implement an approach which learns to cluster images captured at spatially proximal locations under different conditions, separated from frames captured at different places. The formulation is a convex optimization, guaranteeing the existence of a global solution. We evaluate the general applicability of our method on two benchmark change datasets using three typical image pre-processing and feature types: GIST, Principal Component Analysis and learnt Convolutional Neural Network features. The results demonstrate that the distance metric learning approach uniformly improves single-image-based visual place recognition performance across all feature types. Furthermore, we demonstrate that this performance improvement is maintained when the sequence-based algorithm SeqSLAM is applied to the single-image place recognition results, leading to state-of-the-art performance.
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| 2. |
- Mishra, Nishant K, et al.
(författare)
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Clinical characteristics and outcomes of patients with post-stroke epilepsy: protocol for an individual patient data meta-analysis from the International Post-stroke Epilepsy Research Repository (IPSERR).
- 2023
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Ingår i: BMJ open. - 2044-6055. ; 13:11
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Tidskriftsartikel (refereegranskat)abstract
- Despite significant advances in managing acute stroke and reducing stroke mortality, preventing complications like post-stroke epilepsy (PSE) has seen limited progress. PSE research has been scattered worldwide with varying methodologies and data reporting. To address this, we established the International Post-stroke Epilepsy Research Consortium (IPSERC) to integrate global PSE research efforts. This protocol outlines an individual patient data meta-analysis (IPD-MA) to determine outcomes in patients with post-stroke seizures (PSS) and develop/validate PSE prediction models, comparing them with existing models. This protocol informs about creating the International Post-stroke Epilepsy Research Repository (IPSERR) to support future collaborative research.We utilised a comprehensive search strategy and searched MEDLINE, Embase, PsycInfo, Cochrane, and Web of Science databases until 30 January 2023. We extracted observational studies of stroke patients aged ≥18 years, presenting early or late PSS with data on patient outcome measures, and conducted the risk of bias assessment. We did not apply any restriction based on the date or language of publication. We will invite these study authors and the IPSERC collaborators to contribute IPD to IPSERR. We will review the IPD lodged within IPSERR to identify patients who developed epileptic seizures and those who did not. We will merge the IPD files of individual data and standardise the variables where possible for consistency. We will conduct an IPD-MA to estimate the prognostic value of clinical characteristics in predicting PSE.Ethics approval is not required for this study. The results will be published in peer-reviewed journals. This study will contribute to IPSERR, which will be available to researchers for future PSE research projects. It will also serve as a platform to anchor future clinical trials.NCT06108102.
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