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A Tetrahedron-Based Heat Flux Signature for Cortical Thickness Morphometry Analysis [chapter]

Yonghui Fan, Gang Wang, Natasha Lepore, Yalin Wang
2018 Lecture Notes in Computer Science  
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]

Hardie Cate, Fahim Dalvi, Zeshan Hussain
2017 arXiv   pre-print
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]

Dario Sitnik, Ivica Kopriva
2022 arXiv   pre-print
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

Zartash Kanwal, Abdul Basit, Muhammad Jawad, Ihsan Ullah, Anwar Ali
2019 International Journal of Advanced Computer Science and Applications  
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

Ruoqi Wei, Cesar Garcia, Ahmed El-Sayed, Viyaleta Peterson, Ausif Mahmood
2020 IEEE Access  
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]

Sohee Cho, Ginkyeng Lee, Wonjoon Chang, Jaesik Choi
2020 arXiv   pre-print
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]

Weikai Chen, Xiaoguang Han, Guanbin Li, Chao Chen, Jun Xing, Yajie Zhao, Hao Li
2019 arXiv   pre-print
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

S Sri, K. R.
2016 International Journal of Advanced Computer Science and Applications  
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]

Donghui Yan, Timothy W. Randolph, Jian Zou, Peng Gong
2018 arXiv   pre-print
., 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]

Faezeh Nejati Hatamian, Nishant Ravikumar, Sulaiman Vesal, Felix P. Kemeth, Matthias Struck, Andreas Maier
2020 arXiv   pre-print
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]

Qiao Zheng, Hervé Delingette, Kenneth Fung, Steffen E. Petersen, Nicholas Ayache
2019 arXiv   pre-print
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

Najoua Rahal, Maroua Tounsi, Amir Hussain, Adel M. Alimi
2021 IEEE Access  
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

J. C. Luo, J. Zheng, Y. Leung, C. H. Zhou
2003 International Journal of Remote Sensing  
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

Panagiotis Giannakeris, Georgios Meditskos, Konstantinos Avgerinakis, Stefanos Vrochidis, Ioannis Kompatsiaris
2019 Zenodo  
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]

Scott Krig
2014 Computer Vision Metrics  
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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