Curso Aberto

Introduction to Computer Vision

feito por

Georgia Institute of Technology

Offered at Georgia Tech as CS 6476

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Programa Nanodegree

Fundamentos de Deep Learning

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Resumo do curso

This course provides an introduction to computer vision including fundamentals of image formation, camera imaging geometry, feature detection and matching, multiview geometry including stereo, motion estimation and tracking, and classification. We’ll develop basic methods for applications that include finding known models in images, depth recovery from stereo, camera calibration, image stabilization, automated alignment (e.g. panoramas), tracking, and action recognition. We focus less on the machine learning aspect of CV as that is really classification theory best learned in an ML course.

The focus of the course is to develop the intuitions and mathematics of the methods in lecture, and then to learn about the difference between theory and practice in the problem sets. All algorithms work perfectly in the slides. But remember what Yogi Berra said: In theory there is no difference between theory and practice. In practice there is. (Einstein said something similar but who knows more about real life?) In this course you do not, for the most part, apply high-level library functions but use low to mid level algorithms to analyze images and extract structural information.

Valor do curso
Tempo estimado Tempo total entre hoje e dia da formatura depende do seu compromisso semanal. Em média, os nossos graduados completam este nanodegree em 4 meses
4 meses
O curso inclui


Testes interativos

Aulas com profissionais do setor

Ritmo individual de aprendizado

Comunidade de apoio aos alunos

Sua jornada de aprendizagem

Este curso aberto é seu primeiro passo em direção a uma nova carreira com o programa Fundamentos de Deep Learning

Curso Aberto

Introduction to Computer Vision

por Georgia Institute of Technology

Enhance your skill set and boost your hirability through innovative, independent learning.

Icon steps 54aa753742d05d598baf005f2bb1b5bb6339a7d544b84089a1eee6acd5a8543d

Aaron Bobick
Aaron Bobick


Irfan Essa
Irfan Essa


Arpan Chakraborty
Arpan Chakraborty



  • Data structures: You'll be writing code that builds representations of images, features, and geometric constructions.
  • A good working knowledge of Matlab and/or Python with NumPy. The lecture videos use Matlab for occasional demonstration because the instructor is too old to change. Problem sets will be done in Matlab or Python. As mentioned in the resources note below, you can use either Matlab or the open source version Octave.
  • This course has more math than many CS courses: Linear algebra, vector calculus, and linear algebra (that is not a typo).
  • No prior knowledge of vision is assumed though any experience with Signal Processing is helpful.

Por que fazer este curso?

Images have become ubiquitous in computing. Sometimes we forget that images often capture the light reflected from a physical scene. This course gives you both insight into the fundamentals of image formation and analysis, as well as the ability to extract information much above the pixel level. These skills are useful for anyone interested in operating on images in a context-aware manner or where images from multiple scenarios need to be combined or organized in an appropriate way.

Quais são os recursos?
Vídeos dos instrutores Exercícios práticos Aulas com profissionais do setor