Visión artificial
Amplíe flujos de trabajo de deep learning con aplicaciones de visión artificial
Aplique deep learning en aplicaciones de visión artificial con Deep Learning Toolbox™ y Computer Vision Toolbox™.
Apps
Image Labeler | Label images for computer vision applications |
Video Labeler | Label video for computer vision applications |
Funciones
Temas
Detección de objetos
- Getting Started with Object Detection Using Deep Learning (Computer Vision Toolbox)
Perform object detection and instance segmentation using deep learning neural networks. - Augment Bounding Boxes for Object Detection
This example shows how to perform common kinds of image and bounding box augmentation as part of object detection workflows. - Train Object Detector Using R-CNN Deep Learning
This example shows how to train an object detector using deep learning and R-CNN (Regions with Convolutional Neural Networks). - Import Pretrained ONNX YOLO v2 Object Detector
This example shows how to import a pretrained ONNX™ (Open Neural Network Exchange) you only look once (YOLO) v2 [1] object detection network and use it to detect objects. - Export YOLO v2 Object Detector to ONNX
This example shows how to export a YOLO v2 object detection network to ONNX™ (Open Neural Network Exchange) model format. - Deploy Object Detection Model as Microservice (MATLAB Compiler SDK)
Use a microservice to detect objects in images.
Segmentación semántica
- Getting Started with Semantic Segmentation Using Deep Learning (Computer Vision Toolbox)
Segment objects by class using deep learning. - Train Simple Semantic Segmentation Network in Deep Network Designer
This example shows how to create and train a simple semantic segmentation network using Deep Network Designer. - Augment Pixel Labels for Semantic Segmentation
This example shows how to perform common kinds of image and pixel label augmentation as part of semantic segmentation workflows. - Semantic Segmentation Using Dilated Convolutions
Train a semantic segmentation network using dilated convolutions. - Semantic Segmentation of Multispectral Images Using Deep Learning
This example shows how to perform semantic segmentation of a multispectral image with seven channels using U-Net. - 3-D Brain Tumor Segmentation Using Deep Learning
This example shows how to perform semantic segmentation of brain tumors from 3-D medical images. - Define Custom Pixel Classification Layer with Tversky Loss
This example shows how to define and create a custom pixel classification layer that uses Tversky loss. - Explore Semantic Segmentation Network Using Grad-CAM
This example shows how to explore the predictions of a pretrained semantic segmentation network using Grad-CAM. - Generate Adversarial Examples for Semantic Segmentation (Computer Vision Toolbox)
Generate adversarial examples for a semantic segmentation network using the basic iterative method (BIM).
Clasificación de vídeo
- Activity Recognition from Video and Optical Flow Data Using Deep Learning
This example first shows how to perform activity recognition using a pretrained Inflated 3-D (I3D) two-stream convolutional neural network based video classifier and then shows how to use transfer learning to train such a video classifier using RGB and optical flow data from videos [1]. - Gesture Recognition using Videos and Deep Learning
Perform gesture recognition using a pretrained SlowFast video classifier.