【CS231n】－学习笔记-1-Intro to Computer Vision, historical context.
Class: http://cs231n.stanford.edu
Schedule: http://cs231n.stanford.edu/syllabus.html
Slides: http://vision.stanford.edu/teaching/cs231n/slides/winter1516_lecture1.pdf
Video: https://www.youtube.com/watch?v=NfnWJUyUJYU&feature=youtu.be
Explosion of Data
Sensors enable the explosion
Visual Data is hard to grasp the contents
Help to search the content of data needs visual technology
Problems facing today: massive amount of data and the challenges of the dark matter
To know the problems help you go on
Neuroscience
神经科学
Cognitive sciences
认知科学
optics
光学
Image processing , Speech, NLP,
Big Bang of Evolution: 543million years, B.C. :
Camera Obscura
相机 暗盒
the beginning of visual processing: simple structure of the world
oriented edges
experiments: awake but anaesthetized cats
little needle electrode to push electrons through to the skull
primary visual cortex: do a log of visual processing
early: tons and tons of new orleans
1st stage: back of the brain, the furthest of the eyes, not ear the eyes
the edges define the shape:
Birthday of CV: 1966, MIT Standford, AI lab,
the beginning of deep learning: David Marr, 1970s Stages of Visual Representation
Goal is to reconstruct 3D model: so we can recognize objects
the first wave of visual recognition algorithms went after the 3D model:
the world is composed of simple shapes like blocks
David Lowe, 1987
Normalized Cut (Shi & Malik, 1997)
Face Detection, Viola & Jones, 2001
the first successful high-level visual recognition algorithms being used by consumer product
the first digital camera that has a face detector Fujifilm 2006
deep learning algorithms try to learn simple features
focus on features: “SIFT” & Object Recognition, David Lowe, 1999
since hard to describe the whole thing
ML tools like SVM to recognize scene: Spatial Pyramid Matching, Lazebnik, Schmid & Ponce, 2006
Deformable Part Model: Felzenswalb, McAllester, Ramanan, 2009
PASCAL Visual Object Challenge (20 object categories), [Everingham et al. 2006-2012]
www.image-net.org 22K categories and 14M images,
Deng, Dong, Socher, Li, Li, & Fei-Fei, 2009
The Image Classification Challenge: 1,000 object classes 1,431,167 images
the beginning of deep learning evolution
cool problems:
labeling of the entire scene with perceptual grouping
combining recognition with 3D
CS231n focuses on one of the most important problems of visual recognition – image classification
There is a number of visual recognition problems that are related to image classification, such as object detection, image captioning
Convolutional Neural Network (CNN) has become an important tool for object recognition
Convolutional Neural Network (CNN) is not invented overnight
Pre-requisite
• Proficiency in Python, some high-level familiarity with C/C++
– All class assignments will be in Python (and use numpy), but some of the deep learning libraries we may look at later in the class are written in C++.
– A Python tutorial available on course website
• CollegeCalculus,LinearAlgebra
• Equivalent knowledge of CS229 (Machine Learning)
– We will be formulating cost functions, taking derivatives and performing optimization with gradient descent.
