462 research outputs found
Predicting Blossom Date of Cherry Tree With Support Vector Machine and Recurrent Neural Network
Our project probes the relationship between temperatures and the blossom date
of cherry trees. Through modeling, future flowering will become predictive,
helping the public plan travels and avoid pollen season. To predict the date
when the cherry trees will blossom exactly could be viewed as a multiclass
classification problem, so we applied the multi-class Support Vector Classifier
(SVC) and Recurrent Neural Network (RNN), particularly Long Short-term Memory
(LSTM), to formulate the problem. In the end, we evaluate and compare the
performance of these approaches to find out which one might be more applicable
in reality.Comment: 6 Pages, 6 Figure
PL-CVIO: Point-Line Cooperative Visual-Inertial Odometry
Low-feature environments are one of the main Achilles' heels of geometric
computer vision (CV) algorithms. In most human-built scenes often with low
features, lines can be considered complements to points. In this paper, we
present a multi-robot cooperative visual-inertial navigation system (VINS)
using both point and line features. By utilizing the covariance intersection
(CI) update within the multi-state constraint Kalman filter (MSCKF) framework,
each robot exploits not only its own point and line measurements, but also
constraints of common point and common line features observed by its neighbors.
The line features are parameterized and updated by utilizing the Closest Point
representation. The proposed algorithm is validated extensively in both
Monte-Carlo simulations and a real-world dataset. The results show that the
point-line cooperative visual-inertial odometry (PL-CVIO) outperforms the
independent MSCKF and our previous work CVIO in both low-feature and
rich-feature environments
Multi Task Consistency Guided Source-Free Test-Time Domain Adaptation Medical Image Segmentation
Source-free test-time adaptation for medical image segmentation aims to
enhance the adaptability of segmentation models to diverse and previously
unseen test sets of the target domain, which contributes to the
generalizability and robustness of medical image segmentation models without
access to the source domain. Ensuring consistency between target edges and
paired inputs is crucial for test-time adaptation. To improve the performance
of test-time domain adaptation, we propose a multi task consistency guided
source-free test-time domain adaptation medical image segmentation method which
ensures the consistency of the local boundary predictions and the global
prototype representation. Specifically, we introduce a local boundary
consistency constraint method that explores the relationship between tissue
region segmentation and tissue boundary localization tasks. Additionally, we
propose a global feature consistency constraint toto enhance the intra-class
compactness. We conduct extensive experiments on the segmentation of benchmark
fundus images. Compared to prediction directly by the source domain model, the
segmentation Dice score is improved by 6.27\% and 0.96\% in RIM-ONE-r3 and
Drishti GS datasets, respectively. Additionally, the results of experiments
demonstrate that our proposed method outperforms existing competitive domain
adaptation segmentation algorithms.Comment: 31 pages,7 figure
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