Explore computer vision with image processing techniques like smoothing, edge detection, thresholding, contour detection, segmentation, SIFT, and image blending. Use PILLOW, implement KNN classification, and apply watermarks and steganography.
Learn computer vision techniques: smoothing, edge detection, thresholding, segmentation, SIFT, and image blending. Use PILLOW, KNN for classification, and add watermarks or steganography.
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Introduction to Computer Vision and Its Applications
Read, Write, and Color Conversion of Images
Apply Smoothing Techniques for Images
Detecting Edges from Images
Purpose of Morphology Techniques
Different Thresholding Techniques
Contour Detection Technique
What is Otsu Thresholding?
Inserting Shapes and Texts to Images
Detection of Lines Using Hough Lines
Detection of Multiple Shapes Using Edge Detection
How to Segment an Image Using K-Means?
How to Blend Images Using Image Pyramids
Image Segmentation Using Watershed Algorithm
Background Subtraction Using Threshold
Implementation of Template Matching
Working with Scalar Invariant Feature Transform (SIFT)
Multiple Filters Used in Image Processing
Image Steganography
Working with PILLOW Package
Image Classification Using K-Nearest Neighbors (KNN)
Applying Watermark for Images
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Computer vision is a field of AI that enables computers to interpret and understand visual information from images and videos. Applications include autonomous driving, facial recognition, medical imaging, and security systems.
Using libraries like OpenCV, you can read (cv2.imread( ) ), display (cv2.imshow( ) ), and save (cv2.imwrite( ) ) images. Color conversion functions like cv2.cvtColor( ) allow converting between color spaces (e.g., RGB to grayscale).
Smoothing techniques, like Gaussian and median filters, reduce noise by averaging pixel values with neighbors, improving image clarity. Functions like cv2.GaussianBlur() and cv2.medianblur() are commonly used.
Morphological techniques, like erosion and dilation, process binary images to remove noise, close gaps, and refine object shapes, useful in tasks like segmentation.
Otsu’s method automatically selects the optimal threshold value for binary conversion by minimizing intra-class variance, ideal for bimodal images.
Image steganography hides information within images. One common method involves altering the least significant bits of pixel values.
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