Bildverarbeitung und Computer Vision

Erweitern von Deep-Learning-Workflows mit Bildverarbeitung, Computer Vision, medizinischer Bildgebung, Lidar und automatisierten Fahranwendungen

Verwenden Sie die Deep Learning Toolbox™, um Deep Learning in Anwendungen mit Bildverarbeitung, Computer Vision, medizinischer Bildgebung, Lidar-Verarbeitung und automatisiertem Fahren einzubinden.

Enthaltene Beispiele

Object Detection Using Faster R-CNN Deep Learning

Train a Faster R-CNN (regions with convolutional neural networks) object detector.

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Increase Image Resolution Using Deep Learning

Create a high-resolution image from a low-resolution image using a very-deep super-resolution (VDSR) neural network.

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Cardiac Left Ventricle Segmentation from Cine-MRI Images Using U-Net Network

Segment 2-D cardiac MRI images using U-Net, and explore predictions using Grad-CAM explainability maps.

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Lidar Object Detection Using Complex-YOLO v4 Network

Detect objects in point clouds using you only look once version 4 (YOLO v4) deep learning network. In this example, you will

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Train Deep Learning Semantic Segmentation Network Using 3-D Simulation Data

Use 3-D simulation data to train a semantic segmentation network and fine-tune it to real-world data using generative adversarial networks (GANs).

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Train a Deep Learning Vehicle Detector

Train a vision-based vehicle detector using deep learning.

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Bildverarbeitung und Computer Vision

Kategorien

Enthaltene Beispiele

Object Detection Using Faster R-CNN Deep Learning

Increase Image Resolution Using Deep Learning

Cardiac Left Ventricle Segmentation from Cine-MRI Images Using U-Net Network

Lidar Object Detection Using Complex-YOLO v4 Network

Train Deep Learning Semantic Segmentation Network Using 3-D Simulation Data

Train a Deep Learning Vehicle Detector