Deep-Dive AI

Edge detection Convert a 2D image into a set of curves Extracts salient features of the scene More compact than pixels Origin of edges Edges are caused by a variety of factors Images as functions Edges look like steep cliffs Characterizing edges An edge is a place of rapid change in the image intensity function Image derivatives How can we differentiate a digital image $F[x,y]$ Option 1: reconst..

1. What is an image? A grid (matrix) of intensity values (common to use one byte per value: 0 = black, 255 = white) Can think of a (grayscale) image as a function $f$ from $R^2$ to $R$ $f(x,y)$ gives the intensity at position (x,y) A digital image is a discrete (sampled, quantized) version of this function Image transformations As with any function, we can apply perators to an image Today we'll ..

What is computer vision? Why study computer vision? Course overview Images & Image filtering [time permitting] 1. What is computer vision? Goal of computer vision: percieve the story behind the picture Compute properties of the world 3D shape Names of people or objects What happened? Can computers match human perception? Yes and No (maninly no) computers can be better at 'easy' things humans are..

핸즈온머신러닝의 챕터 8을 공부하고 정리합니다. 참고한 글은 아래 Reference로 남겨두었습니다. 이번 챕터 8의 주제는 Dimension Reduction입니다. 책의 목차는 아래와 같습니다. 8.0 개요 8.1 차원의 저주 8.2 차원 축소를 위한 접근 방법 8.3 PCA 8.4 커널 PCA 8.5 LLE 8.6 다른 차원 축소 기법 책 내용을 그대로 정리하진 않을 것 같고, 중요하다고 생각하는 키워드와 개념만 뽑아서 정리해보도록 하겠습니다. | Keyword | Curse of dimensionality, Projection, Manifold, PCA, Kernel PCA, LLE Curse of dimensionality 차원이란 말은 각 sample을 정의하는 정보들로 생각할 수 있습니다. ..