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necessary resources matlab  (MathWorks Inc)


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    MathWorks Inc necessary resources matlab
    Necessary Resources Matlab, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 404 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/matlab+2021a+programming/Computer+Vision+Toolbox/pm37882990-202-0-2
    Average 96 stars, based on 404 article reviews
    necessary resources matlab - by Bioz Stars, 2026-10
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    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. ..

    other:

    Article Title: Dynamic modulation of social gaze by sex and familiarity in marmoset dyads
    Article Snippet: The corners of the checkerboard were automatically detected via a standard algorithm (detectCheckerboardPoints() function in the Image Processing and Computer Vision toolbox in MATLAB).

    Article Title: A longitudinal, multi-omic atlas reveals the emergence of a spatially organized immunosuppressive ecosystem in resistant melanoma.
    Article Snippet: RNA and DNA were eluted in DNase/RNase free water separately and quantified with Qubit 3.0 Qubit dsDNA HS Assay kit (Cat# Q32851) and/ or RNA HS Assay kit (Cat# Q10210) (Life Technologies; Carlsbad, CA, USA) according to recommended protocols.

    Article Title: Dynamic modulation of social gaze by sex and familiarity in marmoset dyads
    Article Snippet: The intrinsic parameters of each camera were estimated based on the data obtained from the checkerboard corner detection algorithm (estimateCameraParameters() function in Image Processing and Computer Vision toolbox in MATLAB).

    Transformation Assay:

    Article Title: Dynamic modulation of social gaze by sex and familiarity in marmoset dyads
    Article Snippet: .. The information of transformation from world coordinates to camera coordinates was then extracted based on the labeled result (cameraPoseToExtrinsics() function in Image Processing and Computer Vision toolbox in MATLAB). .. GoPro 8 cameras were used and were simultaneously controlled via a Bluetooth remote control (The Remote by GoPro).

    Labeling:

    Article Title: Dynamic modulation of social gaze by sex and familiarity in marmoset dyads
    Article Snippet: .. The information of transformation from world coordinates to camera coordinates was then extracted based on the labeled result (cameraPoseToExtrinsics() function in Image Processing and Computer Vision toolbox in MATLAB). .. GoPro 8 cameras were used and were simultaneously controlled via a Bluetooth remote control (The Remote by GoPro).



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    CartoCell pipeline for high-content epithelial cysts segmentation Phase 1: the “high-resolution raw images” consist of confocal z-stack images, where the cell membrane is stained. These images are segmented and proofread using LimeSeg and a custom <t>MATLAB</t> code for curation to obtain the “high-resolution label images” . Together, the raw and the label images encompass the “training high-resolution dataset.” Number of samples = 21. Phase 2: the “training high-resolution dataset” is down-sampled to obtain the “training down-sampled dataset,” which is the training set for the “model M1.” Phase 3: low-resolution images are obtained from confocal z-stack images, stained in a similar way to phase 1. Number of samples = 293. Scale bar, 100 μm. Next, the “work-flow M1” is applied: inference using “model M1” and subsequent post-processing to obtain individual cell instance predictions and cell masks, followed by the 3D Voronoi algorithm to guarantee that predicted cells remain in close contact. As a result, the “low-resolution label images” are generated. Phase 4: training of the “model M2” on the large “training low-resolution dataset.” Number of samples = 314. Phase 5: high-content segmentation of new low-resolution images (unseen by the pipeline) using the “work-flow M2,” which is equivalent to the “work-flow M1” but using the “model M2.” See also <xref ref-type=Figures S1–S3 ; Tables S2 , , and . " width="250" height="auto" />
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    Image Search Results


    CartoCell pipeline for high-content epithelial cysts segmentation Phase 1: the “high-resolution raw images” consist of confocal z-stack images, where the cell membrane is stained. These images are segmented and proofread using LimeSeg and a custom MATLAB code for curation to obtain the “high-resolution label images” . Together, the raw and the label images encompass the “training high-resolution dataset.” Number of samples = 21. Phase 2: the “training high-resolution dataset” is down-sampled to obtain the “training down-sampled dataset,” which is the training set for the “model M1.” Phase 3: low-resolution images are obtained from confocal z-stack images, stained in a similar way to phase 1. Number of samples = 293. Scale bar, 100 μm. Next, the “work-flow M1” is applied: inference using “model M1” and subsequent post-processing to obtain individual cell instance predictions and cell masks, followed by the 3D Voronoi algorithm to guarantee that predicted cells remain in close contact. As a result, the “low-resolution label images” are generated. Phase 4: training of the “model M2” on the large “training low-resolution dataset.” Number of samples = 314. Phase 5: high-content segmentation of new low-resolution images (unseen by the pipeline) using the “work-flow M2,” which is equivalent to the “work-flow M1” but using the “model M2.” See also <xref ref-type=Figures S1–S3 ; Tables S2 , , and . " width="100%" height="100%">

    Journal: Cell Reports Methods

    Article Title: CartoCell, a high-content pipeline for 3D image analysis, unveils cell morphology patterns in epithelia

    doi: 10.1016/j.crmeth.2023.100597

    Figure Lengend Snippet: CartoCell pipeline for high-content epithelial cysts segmentation Phase 1: the “high-resolution raw images” consist of confocal z-stack images, where the cell membrane is stained. These images are segmented and proofread using LimeSeg and a custom MATLAB code for curation to obtain the “high-resolution label images” . Together, the raw and the label images encompass the “training high-resolution dataset.” Number of samples = 21. Phase 2: the “training high-resolution dataset” is down-sampled to obtain the “training down-sampled dataset,” which is the training set for the “model M1.” Phase 3: low-resolution images are obtained from confocal z-stack images, stained in a similar way to phase 1. Number of samples = 293. Scale bar, 100 μm. Next, the “work-flow M1” is applied: inference using “model M1” and subsequent post-processing to obtain individual cell instance predictions and cell masks, followed by the 3D Voronoi algorithm to guarantee that predicted cells remain in close contact. As a result, the “low-resolution label images” are generated. Phase 4: training of the “model M2” on the large “training low-resolution dataset.” Number of samples = 314. Phase 5: high-content segmentation of new low-resolution images (unseen by the pipeline) using the “work-flow M2,” which is equivalent to the “work-flow M1” but using the “model M2.” See also Figures S1–S3 ; Tables S2 , , and .

    Article Snippet: The output of LimeSeg was processed using an in-house MATLAB program (2021a MathWorks) to detect and curate imperfections during cysts segmentation (see section).

    Techniques: Membrane, Staining, Generated

    Journal: Cell Reports Methods

    Article Title: CartoCell, a high-content pipeline for 3D image analysis, unveils cell morphology patterns in epithelia

    doi: 10.1016/j.crmeth.2023.100597

    Figure Lengend Snippet:

    Article Snippet: The output of LimeSeg was processed using an in-house MATLAB program (2021a MathWorks) to detect and curate imperfections during cysts segmentation (see section).

    Techniques: Recombinant, Software