图像处理和计算机视觉

采集、处理和分析图像和视频以进行算法开发和系统设计

借助 MathWorks^® 的图像处理和计算机视觉产品，您可以执行端到端的处理工作流，从数据采集和预处理，到增强和分析，一直到部署到嵌入式视觉系统。

这些产品支持面向图像、视频、点云、激光雷达和高光谱数据的各种工作流。使用这些产品，您可以：

使用 App 以交互方式可视化、探查、标注和处理数据。
用算法增强和分析数据。
使用深度学习执行语义分割、目标检测、分类和图像到图像的转换。
与硬件对接，用于图像采集、算法加速、桌面原型构建和嵌入式视觉系统部署。

适用产品：图像处理和计算机视觉

Image Processing Toolbox

执行图像处理、可视化和分析

Computer Vision Toolbox

Design and test computer vision systems

Lidar Toolbox

Design, analyze, and test lidar processing systems

Medical Imaging Toolbox

Visualize, register, segment, and label 2D and 3D medical images

Vision HDL Toolbox

Design image processing, video, and computer vision systems for FPGAs and ASICs

Image Acquisition Toolbox

Acquire images and video from industry-standard hardware

主题

标注和预处理数据

Choose an App to Label Ground Truth Data (Computer Vision Toolbox)
Decide which app to use to label ground truth data: Image Labeler, Video Labeler, Ground Truth Labeler, Lidar Labeler, Signal Labeler, or Medical Image Labeler.
Get Started with Image Preprocessing and Augmentation for Deep Learning (Image Processing Toolbox)
Preprocess data for deep learning applications with deterministic operations such as resizing, or augment training data with randomized operations such as random cropping.
Medical Image Preprocessing (Medical Imaging Toolbox)
Learn common preprocessing steps used in medical image analysis workflows.
Choose Image Registration Technique (Image Processing Toolbox)
Choose from four approaches to image registration: the Registration Estimator app, intensity-based automatic image registration, control point registration, and automated feature matching.

检测目标和特征

Getting Started with Object Detection Using Deep Learning (Computer Vision Toolbox)
Perform object detection using deep learning neural networks.
Local Feature Detection and Extraction (Computer Vision Toolbox)
Learn the benefits and applications of local feature detection and extraction.
Object Detection in Point Clouds Using Deep Learning (Lidar Toolbox)
Detect 3-D bounding boxes for objects in a point cloud.

分割图像

Getting Started with Semantic Segmentation Using Deep Learning (Computer Vision Toolbox)
Segment objects by class using deep learning.
Getting Started with Image Segmenter (Image Processing Toolbox)
Segment an image using different techniques, refine and save the binary mask, and export the segmentation code by using the Image Segmenter app.
Segment Image and Create Mask Using Color Thresholder (Image Processing Toolbox)
Segment an image based on color values and create a binary mask image using Color Thresholder.

增强图像

对比度增强方法 (Image Processing Toolbox)
使用以下方法调整灰度和彩色图像的对比度：强度值映射、直方图均衡化和对比度受限的自适应直方图均衡化。
去除噪声 (Image Processing Toolbox)
使用平均值滤波、中位数滤波和基于图像局部方差的自适应滤波等方法来去除图像噪声。

执行同步定位与地图构建

Choose SLAM Workflow Based on Sensor Data (Computer Vision Toolbox)
Choose the right simultaneous localization and mapping (SLAM) workflow and find topics, examples, and supported features.

采集和标定数据

Get Started with Image Acquisition Explorer (Image Acquisition Toolbox)
Use the Image Acquisition Explorer to preview, configure, acquire, and save image data.
Using the Single Camera Calibrator App (Computer Vision Toolbox)
Prepare camera calibration images and estimate camera intrinsic parameters.
What Is Lidar-Camera Calibration? (Lidar Toolbox)
Fuse lidar and camera data.

在硬件上部署

图像处理的代码生成 (Image Processing Toolbox)
了解如何使用 MATLAB^® Coder™ 从 Image Processing Toolbox™ 函数生成 C 代码。
GPU Code Generation Workflow (GPU Coder)
Design, implement, and verify generated CUDA MEX for acceleration and standalone CUDA code for deployment.
Integrate YOLO v2 Vehicle Detector System on SoC (Vision HDL Toolbox)
Simulate a YOLO v2 vehicle detection algorithm that contains FPGA and ARM sections for deployment to an SoC device.

精选示例

调整图像大小

通过指定乘法缩放因子或指定所需的图像输出大小来更改图像的大小。

Detect Nuclei in Large Whole Slide Images Using Cellpose

Detect cell nuclei in whole slide images (WSIs) of tissue stained using hematoxylin and eosin (H&E) by using Cellpose.

Automatically Detect and Recognize Text Using Pretrained CRAFT Network and OCR

Perform text recognition by using a deep learning based text detector and OCR. In the example, you use a pretrained CRAFT (character region awareness for text) deep learning network to detect the text regions in the input image. You can modify the region threshold and the affinity threshold values of the CRAFT model to localise an entire paragraph, a sentence, or a word. Then, you use OCR to recognize the characters in the detected text regions.