- perception
- features and boundaries
- Object Recognition
- Camera and imaging
- 3d reconstruction
- Fourier Transform
- High-Dynamic-Range (HDR) Imaging
- Image Formation
- Convolution and Deconvolution
- Working Principles of a Camera
- Scale Space
- Active Contours
Специализация First Principles of Computer Vision
Master the First Principles of Computer Vision. Advance the mathematical and physical algorithms empowering computer vision
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Чему вы научитесь
Master the working principles of a digital camera and learn the fundamentals of imaging processing
Create a theory of feature detection and develop algorithms for extracting features from images
Explore novel methods for using visual cues (shading, defocus, etc.) to recover the 3D shape of an object from multiple images or viewpoints
Get exposed to fundamental perceptions tasks such as image segmentation, object tracking, and object recognition
Приобретаемые навыки
Специализация: общие сведения
Проект прикладного обучения
Learners will develop the fundamental knowledge of computer vision by applying the models and tools including: image processing, image features, constructing 3D scene, image segmentation and object recognition. The specialization includes roughly 250 assessment questions. Proficiency in the fundamentals of computer vision is valued by a wide range of technology companies and research organizations.
Learners should know the fundamentals of linear algebra and calculus. Knowing any programming language is beneficial, but not required.
Learners should know the fundamentals of linear algebra and calculus. Knowing any programming language is beneficial, but not required.
О специализации
Пройти курсы
Специализация Coursera — это серия курсов, помогающих в совершенстве овладеть определенным навыком. Можно сразу записаться на специализацию или просмотреть курсы, из которых она состоит и выбрать тот, с которого вы хотите начать. Подписываясь на курс, который входит в специализацию, вы автоматически подписываетесь на всю специализацию. Можно завершить всего один курс, а потом сделать паузу в обучении или в любой момент отменить подписку. Отслеживайте свои курсы и прогресс на панели управления учащегося.
Практический проект
В каждой специализации есть практический проект, который нужно успешно выполнить, чтобы завершить специализацию и получить сертификат. Если для практического проекта в специализации предусмотрен отдельный курс, прежде чем начать его, необходимо завершить все остальные курсы.
Получите сертификат
Когда вы пройдете все курсы и завершите практический проект, вы получите сертификат, которым можно поделиться с потенциальными работодателями и коллегами.

Специализация включает несколько курсов: 5
Camera and Imaging
This course covers the fundamentals of imaging – the creation of an image that is ready for consumption or processing by a human or a machine. Imaging has a long history, spanning several centuries. But the advances made in the last three decades have revolutionized the camera and dramatically improved the robustness and accuracy of computer vision systems. We describe the fundamentals of imaging, as well as recent innovations in imaging that have had a profound impact on computer vision.
Features and Boundaries
This course focuses on the detection of features and boundaries in images. Feature and boundary detection is a critical preprocessing step for a variety of vision tasks including object detection, object recognition and metrology – the measurement of the physical dimensions and other properties of objects. The course presents a variety of methods for detecting features and boundaries and shows how features extracted from an image can be used to solve important vision tasks.
3D Reconstruction - Single Viewpoint
This course focuses on the recovery of the 3D structure of a scene from its 2D images. In particular, we are interested in the 3D reconstruction of a rigid scene from images taken by a stationary camera (same viewpoint). This problem is interesting as we want the multiple images of the scene to capture complementary information despite the fact that the scene is rigid and the camera is fixed. To this end, we explore several ways of capturing images where each image provides additional information about the scene.
3D Reconstruction - Multiple Viewpoints
This course focuses on the recovery of the 3D structure of a scene from images taken from different viewpoints. We start by first building a comprehensive geometric model of a camera and then develop a method for finding (calibrating) the internal and external parameters of the camera model. Then, we show how two such calibrated cameras, whose relative positions and orientations are known, can be used to recover the 3D structure of the scene. This is what we refer to as simple binocular stereo. Next, we tackle the problem of uncalibrated stereo where the relative positions and orientations of the two cameras are unknown. Interestingly, just from the two images taken by the cameras, we can both determine the relative positions and orientations of the cameras and then use this information to estimate the 3D structure of the scene.
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Колумбийский университет
For more than 250 years, Columbia has been a leader in higher education in the nation and around the world. At the core of our wide range of academic inquiry is the commitment to attract and engage the best minds in pursuit of greater human understanding, pioneering new discoveries and service to society.
Часто задаваемые вопросы
Какие правила возврата средств?
Можно ли зарегистрироваться только на один курс?
Можно ли получить финансовую помощь?
Могу ли я пройти курс бесплатно?
Действительно ли это полностью дистанционный курс? Нужно ли мне посещать какие-либо занятия лично?
Сколько времени занимает получение специализации?
What background knowledge is necessary?
Do I need to take the courses in a specific order?
Получу ли я зачеты в университете за прохождение специализации?
What will I be able to do upon completing the Specialization?
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