When it comes to OpenCV processing images and computer vision tasks, there are many common specific algorithms and functions. Here are some more specific breakdowns:
Image processing algorithm:
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Image denoising : including mean denoising, Gaussian denoising, median filtering, etc., used to reduce noise in images.
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Histogram equalization : used to enhance the contrast of images, especially suitable for low-contrast images.
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Edge detection : such as Sobel, Scharr, Laplacian, etc., used to detect edges in images.
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Image segmentation : including threshold segmentation, region growing, watershed segmentation, etc., used to divide images into different regions or objects.
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Morphological operations : corrosion, expansion, opening operations, closing operations, etc., used for image processing and segmentation.
Feature extraction and descriptor algorithms:
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Corner detection : Such as Shi-Tomasi corner detection, FAST corner detection, etc., used to detect corner points in images.
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Feature descriptors such as ORB, SIFT, and SURF : used to detect and describe key features in images, often used for object matching and recognition.
Object Detection and Tracking:
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Target detection : including object detection based on Haar cascade classifier, YOLO (You Only Look Once) and other deep learning models.
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Target tracking : Use methods such as Kalman filtering, mean shift, and optical flow to track the movement of objects.
Deep Learning Support:
- DNN module : OpenCV's deep learning module allows the use of pre-trained deep learning models, such as Caffe, TensorFlow, PyTorch, etc., for tasks such as object detection and image classification.
Computer vision tasks:
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Face Detection and Recognition : Use Haar cascade classifiers, Dlib library or deep learning models for face detection and recognition.
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Optical flow estimation : Estimating the motion of pixels in an image, used to analyze dynamics in videos.
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Stereo vision and depth estimation : Use stereo cameras or deep learning models to estimate the depth of objects in a scene.
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Image stitching : combine multiple images into a panoramic image.
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Image calibration : used to correct distortion in images, often used for camera calibration.
These subdivisions are part of what OpenCV covers, and each has its own specific algorithms and techniques.