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MathWorks Inc r2019a
R2019a, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 420 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/deep+learning+toolbox+model/Computer+Vision+Toolbox/ppr0192534-187-1-0
Average 96 stars, based on 420 article reviews
r2019a - by Bioz Stars, 2026-11
96/100 stars

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Article Title: Longitudinal Multi-Batch Characterisation of Beer Wort Fermentation Foam Using Image-Based Semantic Segmentation.
Article Snippet: Development and within-batch evaluation of the segmentation component (a) Segmentation model and training A convolutional encoder–decoder network was implemented in MATLAB R2024a using the Deep Learning Toolbox and Computer Vision Toolbox.

Article Title: Optical Smart-Wheel Sensing of Tire Deformation for Tire State and Load Estimation.
Article Snippet: The Camera Calibrator app in MATLAB R2025b (Computer Vision Toolbox, version 25.2) was used with checkerboard images acquired at different positions and orientations.

Article Title: A thermopile array and stereo vision fusion system for small-tube temperature sensing.
Article Snippet: For the calibration of the stereo vision system, we used the wellestablished Zhang’s chessboard calibration method, implemented using the MATLAB stereo camera calibrator toolbox.

Article Title: A Novel Image Inpainting Approach for Geometric Calibration of Low-Cost Handheld Thermal Cameras.
Article Snippet: Following preprocessing, geometric calibration was performed using Zhang’s method [22] under a pinhole camera model, implemented in MATLAB R2025b using routines from the Computer Vision Toolbox.

Article Title: Novel image analysis method for estimating muscle strain from ultrasound B-mode images.
Article Snippet: All analyses were performed using MATLAB 2024a and the Computer Vision Toolbox (MathWorks Inc., Natick, MA, USA).

Software:

Article Title: Contextual camera controls during a collaboration session in a heterogenous computing platform
Article Snippet: .. Non-limiting examples of available AI algorithms, software, and libraries that may be utilized within embodiments of systems and methods described herein include, but are not limited to: PYTHON, OPENCV, INCEPTION, THEANO, TORCH, PYTORCH, PYLEARN2, NUMPY, BLOCKS, TENSORFLOW, MXNET, CAFFE, LASAGNE, KERAS, CHAINER, MATLAB Deep Learning, CNTK, MatConvNet (a MATLAB toolbox implementing convolutional neural networks for computer vision applications), DeepLearnToolbox (a Matlab toolbox for Deep Learning from Rasmus Berg Palm), BigDL, Cuda-Convnet (a fast C++/CUDA implementation of convolutional or feed-forward neural networks), Deep Belief Networks, RNNLM, RNNLIB-RNNLIB, matrbm, deeplearning4j, Eblearn.Ish, deepmat, MShadow, Matplotlib, SciPy, CXXNET, Nengo-Nengo, Eblearn, cudamat, Gnumpy, 3-way factored RBM and mcRBM, mPoT, ConvNet, ELEKTRONN, OpenNN, NEURALDESIGNER, Theano Generalized Hebbian Learning, Apache SINGA, Lightnet, and SimpleDNN. ..

Article Title: Systems and methods for connecting a conference room to an ongoing meeting session
Article Snippet: Such AI/ML model(s) may implement: a neural network (e.g., artificial neural network, deep neural network, convolutional neural network, recurrent neural network, autoencoders, reinforcement learning, etc.), fuzzy logic, deep learning, deep structured learning hierarchical learning, Support Vector Machine (SVM) (e.g., linear SVM, nonlinear SVM, SVM regression, etc.), decision tree learning (e.g., classification and regression tree or “CART”), Very Fast Decision Tree (VFDT), ensemble methods (e.g., ensemble learning, Random Forests, Bagging and Pasting, Patches and Subspaces, Boosting, Stacking, etc.), dimensionality reduction (e.g., Projection, Manifold Learning, Principal Components Analysis, etc.), or the like. .. Non-limiting examples of available AI/ML algorithms, models, software, and libraries that may be utilized within embodiments of systems and methods described herein include, but are not limited to: PYTHON, OPENCV, INCEPTION, THEANO, TORCH, PYTORCH, PYLEARN2, NUMPY, BLOCKS, TENSORFLOW, MXNET, CAFFE, LASAGNE, KERAS, CHAINER, MATLAB Deep Learning, CNTK, MatConvNet (a MATLAB toolbox implementing convolutional neural networks for computer vision applications), DeepLearnToolbox (a Matlab toolbox for Deep Learning from Rasmus Berg Palm), BigDL, Cuda-Convnet (a fast C++/CUDA implementation of convolutional or feed-forward neural networks), Deep Belief Networks, RNNLM, RNNLIB-RNNLIB, matrbm, deeplearning4j, Eblearn.lsh, deepmat, MShadow, Matplotlib, SciPy, CXXNET, Nengo-Nengo, Eblearn, cudamat, Gnumpy, 3-way factored RBM and mcRBM, mPOT, ConvNet, ELEKTRONN, OpenNN, NEURALDESIGNER, Theano Generalized Hebbian Learning, Apache SINGA, Lightnet, and SimpleDNN. ..

Article Title: Systems and methods for remotely provisioning facial recognition data to heterogeneous computing platforms
Article Snippet: .. Non-limiting examples of software and libraries which may be utilized within embodiments of systems and methods described herein to perform AI modeling operations include, but are not limited to: PYTHON, OPENCV, scikit-learn, INCEPTION, THEANO, TORCH, PYTORCH, PYLEARN2, NUMPY, BLOCKS, TENSORFLOW, MXNET, CAFFE, LASAGNE, KERAS, CHAINER, MATLAB Deep Learning, CNTK, MatConvNet (a MATLAB toolbox implementing convolutional neural networks for computer vision applications), DeepLearnToolbox (a Matlab toolbox for Deep Learning from Rasmus Berg Palm), BigDL, Cuda-Convnet (a fast C++/CUDA implementation of convolutional or feed-forward neural networks), Deep Belief Networks, RNNLM, RNNLIB-RNNLIB, matrbm, deeplearning4j, Eblearn.lsh, deepmat, MShadow, Matplotlib, SciPy, CXXNET, Nengo-Nengo, Eblearn, cudamat, Gnumpy, 3-way factored RBM and mcRBM, mPoT, ConvNet, ELEKTRONN, OpenNN, NEURALDESIGNER, Theano Generalized Hebbian Learning, Apache SINGA, Lightnet, and SimpleDNN. ..



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Image Search Results


Learning and prediction by LSTM model.

Journal: PLOS One

Article Title: A new method of off-site inverse carbon accounting and its application in agriculture carbon measurement

doi: 10.1371/journal.pone.0334270

Figure Lengend Snippet: Learning and prediction by LSTM model.

Article Snippet: To model and forecast these trajectories, we employed a univariate LSTM regression model implemented in MATLAB (Deep Learning Toolbox).

Techniques:

Samples generated by LSTM model.

Journal: PLOS One

Article Title: A new method of off-site inverse carbon accounting and its application in agriculture carbon measurement

doi: 10.1371/journal.pone.0334270

Figure Lengend Snippet: Samples generated by LSTM model.

Article Snippet: To model and forecast these trajectories, we employed a univariate LSTM regression model implemented in MATLAB (Deep Learning Toolbox).

Techniques: Generated

a) Decoding emotions from deep convolutional neural network (DCNN) representations using partial least squares regression. b) fc8 layer in the AlexNet model outperformed fc8 layer in EmoNet model in predicting emotion ratings. Each line and dot represent the result of a cross-validated fold. c) AlexNet consistently outperformed EmoNet in predicting emotion ratings across three subsets of BOLD5000 images. d) EmoNet outperformed AlexNet in predicting emotion ratings for the Cowen17 dataset. In contrast, for the BOLD5000 dataset, AlexNet outperformed EmoNet in predicting emotion ratings. Error bars represent the standard error across cross-validated folds.

Journal: bioRxiv

Article Title: Object representations drive emotion schemas across a large and diverse set of daily-life scenes

doi: 10.1101/2025.02.19.638854

Figure Lengend Snippet: a) Decoding emotions from deep convolutional neural network (DCNN) representations using partial least squares regression. b) fc8 layer in the AlexNet model outperformed fc8 layer in EmoNet model in predicting emotion ratings. Each line and dot represent the result of a cross-validated fold. c) AlexNet consistently outperformed EmoNet in predicting emotion ratings across three subsets of BOLD5000 images. d) EmoNet outperformed AlexNet in predicting emotion ratings for the Cowen17 dataset. In contrast, for the BOLD5000 dataset, AlexNet outperformed EmoNet in predicting emotion ratings. Error bars represent the standard error across cross-validated folds.

Article Snippet: The AlexNet DCNN model was implemented via MATLAB’s Deep Learning Toolbox, to extract features from 4913 images.

Techniques:

a) Results of decoding emotions from AlexNet representations. These results show that emotional information is processed hierarchically in a visual object processing system. b) Differences between AlexNet layers in predicting emotion ratings. c) The hierarchical processing of emotional information was consistent across the three subsets of BOLD5000 images. d) The hierarchical processing of emotional information was consistent regardless of whether the BOLD5000 or Cowen17 dataset was used. Error bars represent the standard error across cross-validated folds and each dot represents the result of a cross-validated fold.

Journal: bioRxiv

Article Title: Object representations drive emotion schemas across a large and diverse set of daily-life scenes

doi: 10.1101/2025.02.19.638854

Figure Lengend Snippet: a) Results of decoding emotions from AlexNet representations. These results show that emotional information is processed hierarchically in a visual object processing system. b) Differences between AlexNet layers in predicting emotion ratings. c) The hierarchical processing of emotional information was consistent across the three subsets of BOLD5000 images. d) The hierarchical processing of emotional information was consistent regardless of whether the BOLD5000 or Cowen17 dataset was used. Error bars represent the standard error across cross-validated folds and each dot represents the result of a cross-validated fold.

Article Snippet: The AlexNet DCNN model was implemented via MATLAB’s Deep Learning Toolbox, to extract features from 4913 images.

Techniques:

The representational similarity analysis results indicated that both a) the conv1 layer and b) the fc8 layer of AlexNet exhibited greater within-cluster similarity than between-cluster similarity, suggesting that both layers encode emotional information, regardless of the number of K-means clusters. c) The fc8 layer demonstrated a larger difference in pattern similarity between within-cluster and between-cluster comparisons than the conv1 layer, indicating that the fc8 layer encodes more emotional information. Error bars represent the standard error across clusters and each dot represents the result of a cluster.

Journal: bioRxiv

Article Title: Object representations drive emotion schemas across a large and diverse set of daily-life scenes

doi: 10.1101/2025.02.19.638854

Figure Lengend Snippet: The representational similarity analysis results indicated that both a) the conv1 layer and b) the fc8 layer of AlexNet exhibited greater within-cluster similarity than between-cluster similarity, suggesting that both layers encode emotional information, regardless of the number of K-means clusters. c) The fc8 layer demonstrated a larger difference in pattern similarity between within-cluster and between-cluster comparisons than the conv1 layer, indicating that the fc8 layer encodes more emotional information. Error bars represent the standard error across clusters and each dot represents the result of a cluster.

Article Snippet: The AlexNet DCNN model was implemented via MATLAB’s Deep Learning Toolbox, to extract features from 4913 images.

Techniques: