computer vision basics: n blog series
an n-blog series on CV that should cover the basics of CV!
(n for 'no fixed roadmap, just pure curiosity')
part 0
understanding digital images (pixels, channels, and colour spaces), basic image operations (resizing, cropping, and rotating), image preprocessing techniques (normalisation, filtering, and noise reduction)
part 1
understanding image gradients, sobel operators, and canny edge detection, harris corner detection, sift keypoint basics, and feature description, template matching, haar cascades, and sliding window concept
part 2
convolution operations, pooling layers, and activation functions, understanding resnet, vgg, and transfer learning concepts, loss functions, optimisation techniques, and training strategies, training deep models: loss functions, optimisation techniques, and training strategies