160 research outputs found
Empirical mode decomposition-based facial pose estimation inside video sequences
We describe a new pose-estimation algorithm via integration of the strength in both empirical mode decomposition (EMD) and mutual information. While mutual information is exploited to measure the similarity between facial images to estimate poses, EMD is exploited to decompose input facial images into a number of intrinsic mode function (IMF) components, which redistribute the effect of noise, expression changes, and illumination variations as such that, when the input facial image is described by the selected IMF components, all the negative effects can be minimized. Extensive experiments were carried out in comparisons to existing representative techniques, and the results show that the proposed algorithm achieves better pose-estimation performances with robustness to noise corruption, illumination variation, and facial expressions
Applied sensor fault detection, identification and data reconstruction based on PCA and SOMNN for industrial systems
The paper presents two readily implementable approaches for Sensor Fault Detection, Identification (SFD/I) and faulted sensor data reconstruction in complex systems, in real-time. Specifically, Principal Component Analysis (PCA) and Self-Organizing Map Neural Networks (SOMNNs) are demonstrated for use on industrial turbine systems. In the first approach, Squared Prediction Error (SPE) based on the PCA residual space is used for SFD. SPE contribution plot is employed for SFI. A missing value approach from an extension of PCA is applied for faulted sensor data reconstruction. In the second approach, SFD is performed by SOMNN based Estimation Error (EE), and SFI is achieved by EE contribution plot. Data reconstruction is based on an extension of the SOMNN algorithm. The results are compared in each examining stage. The validation of both approaches is demonstrated through experimental data during the commissioning of an industrial 15MW turbine
Linear vs Nonlinear Extreme Learning Machine for Spectral-Spatial Classification of Hyperspectral Image
As a new machine learning approach, extreme learning machine (ELM) has
received wide attentions due to its good performances. However, when directly
applied to the hyperspectral image (HSI) classification, the recognition rate
is too low. This is because ELM does not use the spatial information which is
very important for HSI classification. In view of this, this paper proposes a
new framework for spectral-spatial classification of HSI by combining ELM with
loopy belief propagation (LBP). The original ELM is linear, and the nonlinear
ELMs (or Kernel ELMs) are the improvement of linear ELM (LELM). However, based
on lots of experiments and analysis, we found out that the LELM is a better
choice than nonlinear ELM for spectral-spatial classification of HSI.
Furthermore, we exploit the marginal probability distribution that uses the
whole information in the HSI and learn such distribution using the LBP. The
proposed method not only maintain the fast speed of ELM, but also greatly
improves the accuracy of classification. The experimental results in the
well-known HSI data sets, Indian Pines and Pavia University, demonstrate the
good performances of the proposed method.Comment: 13 pages,8 figures,3 tables,articl
Learning to In-paint: Domain Adaptive Shape Completion for 3D Organ Segmentation
We aim at incorporating explicit shape information into current 3D organ
segmentation models. Different from previous works, we formulate shape learning
as an in-painting task, which is named Masked Label Mask Modeling (MLM).
Through MLM, learnable mask tokens are fed into transformer blocks to complete
the label mask of organ. To transfer MLM shape knowledge to target, we further
propose a novel shape-aware self-distillation with both in-painting
reconstruction loss and pseudo loss. Extensive experiments on five public organ
segmentation datasets show consistent improvements over prior arts with at
least 1.2 points gain in the Dice score, demonstrating the effectiveness of our
method in challenging unsupervised domain adaptation scenarios including: (1)
In-domain organ segmentation; (2) Unseen domain segmentation and (3) Unseen
organ segmentation. We hope this work will advance shape analysis and geometric
learning in medical imaging
Data-Centric Diet: Effective Multi-center Dataset Pruning for Medical Image Segmentation
This paper seeks to address the dense labeling problems where a significant
fraction of the dataset can be pruned without sacrificing much accuracy. We
observe that, on standard medical image segmentation benchmarks, the loss
gradient norm-based metrics of individual training examples applied in image
classification fail to identify the important samples. To address this issue,
we propose a data pruning method by taking into consideration the training
dynamics on target regions using Dynamic Average Dice (DAD) score. To the best
of our knowledge, we are among the first to address the data importance in
dense labeling tasks in the field of medical image analysis, making the
following contributions: (1) investigating the underlying causes with rigorous
empirical analysis, and (2) determining effective data pruning approach in
dense labeling problems. Our solution can be used as a strong yet simple
baseline to select important examples for medical image segmentation with
combined data sources.Comment: Accepted by ICML workshops 202
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