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How to Use Matrox Imaging Library MIL 9.0 for Machine Vision Applications

How to Use Matrox Imaging Library MIL 9.0 for Machine Vision Applications

Matrox Imaging Library (MIL) 9.0 is a software development kit (SDK) that offers a comprehensive collection of tools for developing and refining machine vision applications. Whether you need to perform image capture, processing, analysis, annotation, display, or archiving, MIL 9.0 has the right tools for you.

matrox imaging library mil 9.0 crack

In this article, we will show you how to use some of the key features of MIL 9.0, such as:

  • Deep learning classification and segmentation

  • 3D data processing and analysis

  • Interactive prototyping environment

By the end of this article, you will have a better understanding of how MIL 9.0 can help you create effective, customized, and future-proofed vision solutions.

Deep learning classification and segmentation

One of the most powerful features of MIL 9.0 is its ability to automatically categorize image content using deep-learning based neural networks for identification and defect detection. MIL 9.0 simplifies training for deep learning and delivers optimized deep learning inference.

To use deep learning classification and segmentation in MIL 9.0, you need to follow these steps:

  • Prepare your training data by labeling images with the desired classes or regions of interest.

  • Select a suitable neural network architecture from the predefined models available in MIL 9.0 or import your own custom model.

  • Train the neural network using the MIL CoPilot interactive environment or the MIL API.

  • Validate the performance of the trained model on new images and fine-tune it if needed.

  • Deploy the trained model on your target platform using the MIL API or the Matrox Design Assistant X flowchart-based IDE.

MIL 9.0 supports various types of neural networks, such as convolutional neural networks (CNNs), fully convolutional networks (FCNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) networks. It also supports various frameworks, such as TensorFlowâ, PyTorchâ, ONNXâ, and Caffeâ.

3D data processing and analysis

MIL 9.0 also has a rich set of tools for performing 3D processing and analysis on point clouds, depth maps, and/or profiles. These tools feature 3D shape finding, blob analysis, metrology, and more.

To use 3D data processing and analysis in MIL 9.0, you need to follow these steps:

  • Capture 3D data using one of the supported methods, such as sheet-of-light triangulation, stereo vision, time-of-flight cameras, or structured light projection.

  • Calibrate the 3D data using straightforward methods and associated tools provided by MIL 9.0. The calibration can combine multiple sheet-of-light sources and 2D camera pairs to work as one, thus avoiding the need for post alignment and merger.

  • Process the 3D data using various operations, such as filtering, smoothing, resampling, cropping, merging, transforming, etc.

  • Analyze the 3D data using various tools, such as shape finding, blob analysis, edge extraction, metrology measurements, etc.

  • Display and archive the 3D data using various formats, such as point cloud data (PCD), polygon file format (PLY), stereolithography (STL), etc.

Interactive prototyping environment

MIL 9.0 comes with MIL CoPilot, an interactive environment that lets you perform deep learning training, set up and experiment with tools, prototype applications without writing program code, and ultimately generate functional program code when ready to proceed with application integration.

To use MIL CoPilot in MIL 9.0, you need to follow these steps:

  • Create a new project or open an existing one in MIL CoPilot.

  • Add images or videos to your project from various sources, such as files, cameras, frame grabbers, etc.

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