Label matrix

Author: m | 2025-04-24

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Documentation Examples Functions Apps Videos Answers Main Content Convert label matrix to cell array of linear indicesSyntaxDescriptionpixelIndexList = label2idx(L) converts the regions described by the label matrix L into linear indices pixelIndexList. exampleExamplescollapse allCalculate Pixel Index List for Small Label MatrixCreate a small sample matrix containing three regions.BW = logical([1 1 1 0 0 0 0 0 1 1 1 0 1 1 0 0 1 1 1 0 1 1 0 0 1 1 1 0 0 0 0 0 1 1 1 0 0 0 1 0 1 1 1 0 0 0 1 0 1 1 1 0 0 1 1 0 1 1 1 0 0 0 0 0]);Create a label matrix from this sample image.L = 8×8 1 1 1 0 0 0 0 0 1 1 1 0 2 2 0 0 1 1 1 0 2 2 0 0 1 1 1 0 0 0 0 0 1 1 1 0 0 0 3 0 1 1 1 0 0 0 3 0 1 1 1 0 0 3 3 0 1 1 1 0 0 0 0 0Get a linear index list of all the pixels in each region. The function returns a cell array with an element for each region it finds in the label matrix.pixelIndexList = label2idx(L)pixelIndexList=1×3 cell array {24x1 double} {4x1 double} {4x1 double}Examine one of the pixel index lists returned. For example, look at the second cell in the returned cell array. It contains the linear indices for all the pixels in the region labeled "2". The upper left corner of the region is pixel BW(2,5), which is the 34th pixel in linear indexing.Input Argumentscollapse allL — Label matrix numeric arrayLabel matrix, specified as a numeric array of any dimension.Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | logicalOutput Argumentscollapse allpixelIndexList — Linear indices of pixels in regions 1-by-n cell arrayLinear indices of pixels in regions, returned as a 1-by-n cell array. Each element of the output, pixelIndexList{n}, is a vector that contains all the Below.• Place the cursor immediately before or after a label reference, then from the Format menu, select Equation Labels, and then Label Reference. Saving a Worksheet with Equation Labels You can set equation labels to be visible or hidden in an execution group, a selected area, or the entire worksheet.• The Format>Equation Labels>Execution Group menu item controls equation label display in an execution group.• The Format>Equation Labels>Selection menu item controls equation label display in a selected region. A selected region may contain displayed and invisible equation labels. By using the Selection menu item (toggle switch), all equation labels in the region will be set to the toggled setting of the first equation label in the region.• The Format>Equation Labels>Worksheet menu item controls equation label display in a worksheet. When you save a worksheet, the settings are also saved. The Display Equation Labels check box in the Options dialog controls equation label display in the entire Maple session or environment. It does not control what areas of the worksheet display equation labels.Notes1. When referring to a mutable structure, such as a module, table, object, Vector, or Matrix, be aware that you will get the current value of that structure, rather than the value of the structure at the time it was printed.2. References to Matrix and Vector objects may be lost after a restart. This means that the commands to generate the objects must be re-executed before the Matrix browser is available for inspecting the contents of a large Matrix.3. Due to the different way in which these objects are stored and modified inside Maple, label references should be avoided when referring to modules, procedures, tables, and arrays (but not the recommended Array data structure). Assign names to these objects instead.Returning a Sequence or a Range of Labeled Values Use the Insert Label dialog to return a sequence or range of labeled values. To return a sequence of labeled values, enter the labels as a comma-separated sequence in the Insert Label dialog. To return a range of labeled values, enter the labels as a range (denoted by two dots) in the Insert Label dialog. See Also Format MenuInterdocument ReferencenameOptions>Display

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Matrix Label Stock Custom Labels

Lines.'off' — Do not label the contour lines. Contour line width, specified as a positive value in points. One point equals 1/72 inch. Label spacing along the contour lines, specified as a scalar value in points, where one point is 1/72 inch. Use this property to control the number of contour labels along the contour lines. Smaller values produce more labels. You must set the ShowText property to 'on' for the LabelSpacing property to have an effect. If you use the clabel function to display the labels, then the LabelSpacing property has no effect and the plot displays one label per line. Output Argumentscollapse allM — Contour matrix matrixContour matrix, returned as a two-row matrix of following form. Z1, x1,1, x1,2, ..., x1,N1, Z2, x2,1, x2,2, ..., x2,N2, Z3, ...N1, y1,1, y1,2, ..., y1,N1, N2, y2,1, y2,2, ..., y2,N2, N3, ...The columns of the matrix define the contour lines. Each contour line starts with a column containing Z and N values:Zi — The height of the ith contour lineNi — The number of vertices in the ith contour line(xij, yij) — The coordinates of the vertices for the ith contour line, where j ranges from 1 to Nic — Contour object Contour objectContour object. Use this object to set properties after displaying the contour plot.Extended CapabilitiesGPU Arrays Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.The contourf function supports GPU array input with these usage notes and limitations:This function accepts GPU arrays, but does not run on a GPU.For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).Distributed Arrays Partition large arrays across the combined memory of your cluster using Parallel Computing Toolbox™.Usage notes and limitations:This function operates on distributed arrays, but executes in the client MATLAB.For more information, see Run MATLAB Functions with Distributed Arrays (Parallel Computing Toolbox).Version HistoryIntroduced before R2006a

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Each label. Click on the Training Confusion Matrix image to make it larger. The Training Confusion Matrix is based on how well the model predicted labeled data used during training. From a training data perspective, the dark blue diagonal, top left through the center to the bottom right is good. This shows that the training data provided was sufficient for the model to converge on all classes. Click on the Validation Confusion Matrix image to make it larger. The Validation Confusion Matrix is based on how well the model predicted labels not used during training. Although validation data is provided to the model during training, it is only used for comparison at the end of each epoch to calculate the epochs error. The error is indicated by the loss score throughout training. The image below is much different than the training confusion matrix. It indicates several possible issues with the data or data distribution. It is possible the distribution of validation data was limited for examples containing Roof Damage labels, and more so for Structural Damage labels. Class Roof Damage is confused with background data and Rubble. While class Structural Damage is confused with Rubble and Roof Damage. Given the type of features to learn, even for a person, it could be difficult to differentiate Structural Damage from Rubble. The next image shows a side-by-side comparison of each label type, total pixels, total labels, and the final epochs train and validation confusion matrix scores. Training Deep Learning models, especially with complicated data will usually require trial and error. Retraining the model in this tutorial will likely produce a different set of results and statistics every time. There is no perfect answer for creating good models, but an educated assessment goes along way for improving a model. Some observations looking at the image above and the CM Validation Score: Blue Tarps scored the highest out of all classes and has the least amount of training data. You could speculate that Blue Tarps compared to the other classes is a much simpler feature to learn, and unique in color compared to the others. Rubble was the second highest scoring class per validation, and arguably many features in the training labels look very similar to Rubble. Additionally, Rubble labels offer the highest number of pixels used during training. Roof Damage was the third highest scoring class per the validation score. Roof Damage scored a 0.24 as Unclassified, and 0.30 as Rubble, with an actual label score of 0.46. Roof tops in general have a similar look as parking lots and roadways which could explain the unclassified error. Roof Damage is almost always mixed closely with Rubble and Structural Damage and may simply need. Teklynx Label Matrix LABEL MATRIX - BARCODE LABEL DESIGN SOFTWARE FROM TEKLYNX. LABEL MATRIX is a feature-rich label design application for low-complexity LABEL MATRIX LABEL MATRIX is feature-rich label design software for smaller companies looking for a trusted solution that is simple to install and easy to use. LABEL MATRIX is an

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"dataColors": [ "#118DFF", "#12239E", "#E66C37", "#6B007B", "#E044A7", "#744EC2", "#D9B300", "#D64550", "#197278", "#1AAB40" ], "good": "#1AAB40", "neutral": "#D9B300", "bad": "#D64554", "maximum": "#118DFF", "center": "#D9B300", "minimum": "#DEEFFF", "null": "#FF7F48"}Set structural colorsNext, you can add various color classes, such as background and firstLevelElements. These color classes set the structural colors for elements in the report, such as axis gridlines, highlight colors, and background colors for visual elements.The following table shows the six color classes you can format. The Color class names correspond to the names in the "Advanced" subsection of the "Name and Colors" section in the Customize theme dialog box.Color class preferred nameAlso calledWhat it formatsfirstLevelElementsforegroundLabel background color (when outside data points)Trend line colorTextbox default colorTable and matrix values and totals font colorsData bars axis colorCard data labelsGauge callout value colorKPI goal colorKPI text colorSlicer item color (when in focus mode)Slicer dropdown item font colorSlicer numeric input font colorSlicer header font colorScatter chart ratio line colorLine chart forecast line colorMap leader line colorFilter pane and card text colorModern visual tooltips text and icon color (Preview)secondLevelElementsforegroundNeutralSecondaryLight secondary text classesLabel colorsLegend label colorAxis label colorTable and matrix header font colorGauge target and target leader line colorKPI trend axis colorSlicer slider colorSlicer item font colorSlicer outline colorLine chart hover colorMulti-row card title colorRibbon chart stroke colorShape map border colorButton text font colorButton icon line colorButton outline colorthirdLevelElementsbackgroundLightAxis gridline colorTable and matrix grid colorSlicer header background color (when in focus mode)Multi-row card outline colorShape fill colorGauge arc background colorApplied filter card background colorDisabled button fill color (when the background is FFFFFF)Disabled button outline color (when the background is FFFFFF)fourthLevelElementsforegroundNeutralTertiaryLegend dimmed colorCard category label colorMulti-row card category labels colorMulit-row card bar colorFunnel chart conversion rate stroke colorDisabled button text font colorDisabled button icon line colorbackgroundLabel background color (when inside data points)Slicer dropdown items background colorDonut chart stroke colorTreemap stroke colorCombo chart background colorButton fill colorFilter pane and available filter card background colorModern visual tooltips background color (Preview)secondaryBackgroundbackgroundNeutralTable and matrix grid outline colorShape map default colorRibbon chart ribbon fill color (when match series option is turned off)Disabled button fill color (when the background color isn't FFFFFF)Disabled button outline