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.
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이 강좌에 대하여
Students should know the fundamentals of linear algebra and calculus. The knowledge of any programming language is beneficial, though not required.
배울 내용
Learn how to detect edges and corners in images.
Develop active contours (snakes) to find complex object boundaries.
Learn about the Hough Transform for finding simple parametric shapes in images.
Learn about image transformations and how to estimate the homography between two images.
귀하가 습득할 기술
- Scale Space
- Active Contours
- Image Transformations
- Edge and Corner Detection
- SIFT Detector
Students should know the fundamentals of linear algebra and calculus. The knowledge of any programming language is beneficial, though not required.
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컬럼비아대학교
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.
강의 계획표 - 이 강좌에서 배울 내용
Getting Started: Features and Boundaries
Edge Detection
Boundary Detection
SIFT Detector
First Principles of Computer Vision 특화 과정 정보
This specialization presents the first comprehensive treatment of the foundations of computer vision. It focuses on the mathematical and physical underpinnings of vision and has been designed for learners, practitioners and researchers who have little or no knowledge of computer vision. The program includes a series of 5 courses. Any learner who completes this specialization has the potential to build a successful career in computer vision, a thriving field that is expected to increase in importance in the coming decades.

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