AI73 1.2 Application of computer vision Today, we will learn about the application of computer vision. ● Object Recognition ● Face Detection - skin filter, eye magnification, eye lash density ● Search ● Tango ● Computer Vision for VR ● Vision in Cars ● Facebook video style transfer 2016 Aside from these, industry wants more and more CV talent. In next class, we will learn about Image Filtering. 2020. 7. 31. 1.1 What is Computer Vision? This is the first lecture of the category 'Computer Vision Material'. First things first. We will learn about what is computer vision today. What a person sees is different from what a computer sees. Why are we able to interpret the image above? The goal of computer vision is to give computers human-level visual perception and when possible super-human perception! Let's see (typical) Computer Vi.. 2020. 7. 31. 8.X TF-IDF In this lecture, we will learn about TF-IDF. As we learned in last lecture, Vector Space Model (aka Bag-of-Words) works as below. A document (datapoint) is a vector of counts over each word (feature) Vd is just a histogram over words. n( · ) counts the number of occurences. What is the similarity between two documents? We can use any distance but the cosine distance is fast. But not all words ar.. 2020. 7. 31. 8.2 BoW Classification In this lecture, we are going to learn about BoW Classification. Once again, let's see how Classical Image Classification Pipeline looks. Among 4 steps, today we are going to focus on 'encoder', 'bag of visual words' encoder. ● BoW encoder In BoW encoder view, we can define 'encode' and 'decode' as below. ⊙ Encode 1. Dictionary Learning a. Extract features from image * Regular grid - Vogel & Sch.. 2020. 7. 30. 이전 1 2 3 4 5 6 7 ··· 19 다음
