ISP Algorithm

Course Sections:

1. Basics of Computer Vision (Beginner Level)

• Introduction to computer vision and its applications
• Image representation: pixels, grayscale, RGB 
• Basic image processing: resize, crop, rotate 
• Basic Python programming 
• Introduction to OpenCV: loading, displaying images 
• Basic image transformations: scaling, translation, rotation, erosion, manipulation, histogram equilization, dilation, opening, and closing etc
• Filtering & Convolutions: blur, sharpen, edge detection (Sobel, Canny)

Course Sections:

1. Basics of Computer Vision (Intermediate Level)

• Advanced image filtering: Gaussian, median, bilateral 
• Feature detection: Keypoint detection (Harris, Shi-Tomasi), Feature descriptors (SIFT, SURF, ORB) 
• Feature matching: using descriptors - Affine and projective transformations, stitching and alignment.
• Image registration and alignment - Camera geometry: pinhole camera model, intrinsic and extrinsic parameters 
• Image Segmentation: Thresholding techniques, Region-based segmentation (watershed, mean shift)
• Basic supervised learning: classification, regression 
• Using OpenCV with ML algorithms: k-NN, SVM 
• Basic object detection: Shape analysis and object recognition, HOG, Haar cascades, sliding window, template matching

Course Sections:

1. Foundation

2. Image Transformations

3. Point Operations

4. Linear Filters

5. Non-Linear Filters