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A Tetrahedron-Based Heat Flux Signature for Cortical Thickness Morphometry Analysis
[chapter]
2018
Lecture Notes in Computer Science
Preserved Fulltext
using Complementary T1-weighted Information 562 Deep Convolutional Gaussian Mixture Model for Stain-Color Normalization of Histopathological Images 566 Towards MR-Only Radiotherapy Treatment Planning: ...
Non-local Deep Feature Fusion for Malignancy Characterization of Hepatocellular Carcinoma 334 Deep Reinforcement Learning for Surgical Gesture Segmentation and Classification 339 Omni-supervised learning ...
doi:10.1007/978-3-030-00931-1_48
pmid:30338317
pmcid:PMC6191198
fatcat:dqhvpm5xzrdqhglrfftig3qejq
DeepFace: Face Generation using Deep Learning
[article]
2017
arXiv
pre-print
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In Section 3, we describe the methods used to fine-tune our CNN and generate new images using a novel approach inspired by a Gaussian mixture model. ...
Our classification system has 82\% test accuracy. Furthermore, our generation pipeline successfully creates well-formed faces. ...
We employ a novel technique that models distributions of feature activations within the CNN as a customized Gaussian mixture model. ...
arXiv:1701.01876v1
fatcat:mse2bpun6bdztcxwxc5v7n3xjy
Clustering and classification of low-dimensional data in explicit feature map domain: intraoperative pixel-wise diagnosis of adenocarcinoma of a colon in a liver
[article]
2022
arXiv
pre-print
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Results are supported by a discussion of interpretability using Shapely additive explanation values for predictions of linear classifier in input space and aEFM induced space. ...
To partially overcome this gap, this paper explores the approximate explicit feature map (aEFM) transform of low-dimensional data into a low-dimensional subspace in Hilbert space. ...
As opposed to nonlinear classification models, linear models are easily interpretable and explainable when using Shapely additive values to explain individual feature contribution towards model prediction ...
arXiv:2203.03636v1
fatcat:zy2dxl4fyngljlgzbtdvpeu4re
Overlapped Apple Fruit Yield Estimation using Pixel Classification and Hough Transform
2019
International Journal of Advanced Computer Science and Applications
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Researchers proposed various visual based methods for estimating the fruit quantity and performing qualitative analysis, they used ariel and ground vehicles to capture the fruit images in orchards. ...
We used the fine tuned morphological operators to refine the blobs received from the previous step and remove the noisy regions followed by the Gaussian smoothing. ...
They used global mixture of Gaussian (GMOG) that worked on the principles of mixture of Gaussian (MOG) for motion detection. ...
doi:10.14569/ijacsa.2019.0100271
fatcat:qih3jhasnffx7cr4htj75lkzxi
Variations in Variational Autoencoders - A Comparative Evaluation
2020
IEEE Access
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Here, the term "visual feature learning" refers to basic features (e.g., color, shape) and non-basic features (e.g., different directions).
FIGURE 14. ...
Gaussian Mixture VAE (GMVAE) Although VaDE is simple and performs GMM on the latent space for clustering, it cannot be considered as a real GMM for data generation due to having independent gaussian distributions ...
doi:10.1109/access.2020.3018151
fatcat:elyvmvz7bzcvtphrw4z36qcqp4
Interpretation of Deep Temporal Representations by Selective Visualization of Internally Activated Nodes
[article]
2020
arXiv
pre-print
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Recently deep neural networks demonstrate competitive performances in classification and regression tasks for many temporal or sequential data. ...
However, it is still hard to understand the classification mechanisms of temporal deep neural networks. ...
We applies various methods, including K-means, Gaussian Mixture Model (GMM), K-shape, and Self Organizing Map (SOM) methods. The results of clustering methods are below. ...
arXiv:2004.12538v2
fatcat:arvdxnjitng3hoqsswmlqwmkca
Deep RBFNet: Point Cloud Feature Learning using Radial Basis Functions
[article]
2019
arXiv
pre-print
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We demonstrate that the proposed network with a single RBF layer can outperform the state-of-the-art Pointnet++ in terms of classification accuracy for 3D object recognition tasks. ...
In this paper, we propose a simple yet effective framework for point set feature learning by leveraging a nonlinear activation layer encoded by Radial Basis Function (RBF) kernels. ...
Hamza and Krim [5] further apply geodesic distance for 3D shape classification, making it possible to capture pose-invariant features. ...
arXiv:1812.04302v2
fatcat:ma3vjx47mbcsnjoje3tljrdpq4
Gaussian Mixture Model and Deep Neural Network based Vehicle Detection and Classification
2016
International Journal of Advanced Computer Science and Applications
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enable efficient feature space for further classification. ...
Furthermore, scale made towards its classification. ...
doi:10.14569/ijacsa.2016.070903
fatcat:ti4lbwvf3bhcxmxuaxmapslzii
Incorporating Deep Features in the Analysis of Tissue Microarray Images
[article]
2018
arXiv
pre-print
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., hierarchical clustering and recursive space partition. ...
Inspired by the recent success of deep learning, we propose to incorporate representations learnable through computation. ...
Our simulations on the Gaussian mixtures provide insights on when such deep features may help. ...
arXiv:1812.00887v1
fatcat:lo2gh7nsvbcwllptajbworg3cy
The Effect of Data Augmentation on Classification of Atrial Fibrillation in Short Single-Lead ECG Signals Using Deep Neural Networks
[article]
2020
arXiv
pre-print
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The results show that deep learning-based AF signal classification methods benefit more from data augmentation using GANs and GMMs, than oversampling. ...
In this study, we investigate the impact of various data augmentation algorithms, e.g., oversampling, Gaussian Mixture Models (GMMs) and Generative Adversarial Networks (GANs), on solving the class imbalance ...
Deep CNNs have also been adopted for ECG signal classification. Rajpurkar et al. ...
arXiv:2002.02870v2
fatcat:pygtexuvkzfyfld3mrafhwn5eq
Unsupervised shape and motion analysis of 3822 cardiac 4D MRIs of UK Biobank
[article]
2019
arXiv
pre-print
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Second, a feature selection is performed to remove highly correlated feature pairs. Third, clustering is carried out using a Gaussian mixture model on the selected features. ...
First, with a feature extraction method previously published based on deep learning models, we extract from each case 9 feature values characterizing both the cardiac shape and motion. ...
Khanji, Filip Zemrak, Valentina Carapella and Young Jin Kim for contributing in the manual analysis of the UK Biobank cases. Steffen E. ...
arXiv:1902.05811v1
fatcat:kqwdz3zvbvbzzpabtqs7ymchmm
Deep sparse auto-encoder features learning for Arabic text recognition
2021
IEEE Access
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We propose a novel hybrid network, combining a Bag-of-Feature (BoF) framework for feature extraction based on a deep Sparse Auto-Encoder (SAE), and Hidden Markov Models (HMMs), for sequence recognition ...
INDEX TERMS Arabic text recognition, feature learning, bag of features, sparse auto-encoder, hidden Markov models. ...
It is improved by 3.25% compared to the k-means codebook.
4) IMPACT OF DIFFERENT NUMBERS OF GAUSSIAN MIXTURES The basic benefit of the Gaussian mixtures is their power to model complicated shapes of ...
doi:10.1109/access.2021.3053618
fatcat:p7jhbokjsjbunceuq4lu7xnmci
A knowledge-integrated stepwise optimization model for feature mining in remotely sensed images
2003
International Journal of Remote Sensing
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distributions of feature space, and hence to influence accuracy and interpretability of the results in the course of analysis. ; Extending on the method of Gaussian mixture modeling and decomposition ...
in a feature space. ...
The authors thank the reviewers for their comments. ...
doi:10.1080/0143116031000114833
fatcat:ze2ifok2bbg4lfubfjjnjl4qey
Real-time Recognition of Daily Actions Based on 3D Joint Movements and Fisher Encoding
2019
Zenodo
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The low-level descriptors are then aggregated into discriminative high-level action representations by modeling prototype pose movements with Gaussian Mixtures and then using a Fisher encoding schema. ...
In this work, we propose a novel framework for the recognition of actions of daily living from depth-videos. ...
Directly computing displacement vectors in 3D space will result in inconsistent results due to lack of invariance to the subjects' natural body shapes. ...
doi:10.5281/zenodo.3502918
fatcat:glp7zmztdrhenophhsdz3zhwve
Local Feature Design Concepts, Classification, and Learning
[chapter]
2014
Computer Vision Metrics
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Classification of Features and Objects Classification is another term for recognition, and it includes feature space organization and training. ...
Distribution Models
Gaussian Mixture
Models [356]
Iterative methods of finding
maximum likelihood of model
parameters. ...
doi:10.1007/978-1-4302-5930-5_4
fatcat:va2d2tylszhu7h3uefu54s76a4
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