Introduction to the Computer Vision Practice Project
The Computer Vision Practice project offers an extensive collection of image processing codes and images, tailored for enthusiasts eager to dive deeper into the world of computer vision. The project acts as a central hub where one can explore various facets of image processing using OpenCV and other techniques. Each section within this project is accompanied by detailed annotations and links to in-depth articles, making it an invaluable resource for both beginners and seasoned practitioners.
Theoretical Foundation
The project begins with a strong theoretical foundation, providing essential articles on image and vision theory. One notable piece is "Image and Vision Theory Basics," which can be accessed here.
Image Processing Insights
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Basic Operations:
- This section covers fundamental image operations like reading images and videos, ROI extraction, and commonly used OpenCV functions. Explore more here.
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Arithmetic Operations & Thresholds:
- Delve into numerical calculations, mask operations, and image binarization here.
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Gray Scale Transformations:
- Learn about linear and nonlinear transformations, including logarithmic and gamma transformations here.
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Smoothing Techniques:
- Covering mean, Gaussian, median, and bilateral filtering, this section offers insights into image smoothing here.
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Morphological Processing:
- Discover morphological techniques like erosion, dilation, and edge detection here.
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Gradient and Edge Detection:
- This segment teaches you about Sobel, Scharr, and Laplacian operators, as well as the Canny edge detector here.
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Image Pyramids:
- Learn how to use Gaussian and Laplacian pyramids in image processing here.
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Contour Processing:
- Master contour drawing, retrieval, filling, and approximation here.
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Histogram and Equalization:
- Understand histograms and their role in image equalization here.
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Image Transformations:
- FFT, high-pass and low-pass filtering are some of the transformations explored here here.
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Geometric Transformations:
- Techniques such as scaling, rotating, and translating images are covered here.
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Quantization & Sampling:
- Explore K-Means clustering and mosaic processing here.
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Feature Detection:
- Feature detection methods like Harris corner detection and SIFT here.
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Image Formats:
- Discussion on the differences between common image file formats such as PNG, JPG, and BMP here.
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Saturation and Modulus Operations:
- An introduction to the saturation and modulus operations in image processing here.
Practical OpenCV Projects
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Image Similarity Algorithms:
- Compare pixel variances, use perceptual hash algorithms, and more here.
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Detection and Segmentation:
- Learn to detect and segment target areas in images here.
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Image Augmentation with TensorFlow:
- Enhance image data as part of TensorFlow learning here.
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Document Scanning and OCR:
- Techniques for document scanning, image correction, and OCR recognition here.
VisionPro Study Notes
The project also includes detailed notes on using VisionPro software, an advanced tool for image processing, complementary to OpenCV.
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Software Introduction:
- Basics of using VisionPro here.
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Image Convert Tools:
- Learn about image conversion utilities here.
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Analyzing Bead Patterns:
- The BeadInspectTool is explored here.
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Pattern Inspection:
- Using PatInspect for detailed analysis here.
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Polar Coordinate Tools:
- How to use PolarUnwrapTool here.
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Using QuickBuild:
- Advanced QuickBuild techniques here.
This comprehensive collection within the Computer Vision Practice project provides a solid groundwork and practical applications for anyone interested in the field of image processing, from beginners to advanced users seeking to deepen their knowledge and skills.