Image Processing and Computer Vision

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.

(Image Processing Toolbox)

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.

(Medical Imaging Toolbox)

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:

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).

Multiclass Object Detection Using YOLO v2 Deep Learning

Train a YOLO v2 multiclass object detector and evaluate object detector performance across selected classes and overlap thresholds.

(Computer Vision Toolbox)

Spacecraft Pose Estimation Using HRNet Keypoint Detector and PnP Solver

Estimate keypoints and pose of spacecraft.

(Computer Vision Toolbox)

Reidentify People Throughout a Video Sequence Using ReID Network

Track people throughout a video sequence using re-identification with a residual network.

(Computer Vision Toolbox)

Perform Instance Segmentation Using SOLOv2

Segment object instances of randomly rotated machine parts in a bin using a deep learning SOLOv2 network.

(Computer Vision Toolbox)