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matlab 2015  (MathWorks Inc)


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    MathWorks Inc matlab 2015
    Matlab 2015, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 409 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/image+analysis+software+matlab+2015a/Computer+Vision+Toolbox/pmc07320174-176-6-6
    Average 96 stars, based on 409 article reviews
    matlab 2015 - by Bioz Stars, 2026-11
    96/100 stars

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    other:

    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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