uCertify

Computer Vision

From self-driving cars to medical imaging, unlock the future of visual intelligence with our comprehensive Computer vision course. 

(COMP-VISION.AU1)
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About This Course

In an era where visual data is everywhere, understanding how to process it is a survival skill for AI engineers. Our Computer vision program is designed for practitioners who want to move beyond theory. Using the award-winning uCertify platform, you will engage with Learning materials that simulate real-world environments. Whether you are preparing for a specific computer vision certification exam or looking to broaden your expertise through learning certifications, this course offers a structured approach. We bridge the gap between basic pixel manipulation and advanced neural networks, ensuring you are ready for a high-impact career.

Skills You’ll Get

  • Visual Fundamentals: Master image representation, color models, and camera geometry.
  • Deep Learning for Vision: Build and train Convolutional Neural Networks (CNNs) for object detection and classification.
  • Advanced Processing: Learn spatial filtering, edge detection, and feature extraction (SIFT/SURF).
  • Project Mastery: Use uCertify labs to build real projects, from facial recognition systems to autonomous navigation models.
Download Course Outline

1

Preface

  • Notes on the Second Edition
2

Introduction

  • What is computer vision?
  • A brief history
  • Course overview
  • Sample syllabus
  • A note on notation
  • Additional reading
3

Image formation

  • Geometric primitives and transformations
  • Photometric image formation
  • The digital camera
  • Additional reading
  • Exercises
4

Image processing

  • Point operators
  • Linear filtering
  • More neighborhood operators
  • Fourier transforms
  • Pyramids and wavelets
  • Geometric transformations
  • Additional reading
  • Exercises
5

Model fitting and optimization

  • Scattered data interpolation
  • Variational methods and regularization
  • Markov random fields
  • Additional reading
  • Exercises
6

Deep Learning

  • Supervised learning
  • Unsupervised learning
  • Deep neural networks
  • Convolutional neural networks
  • More complex models
  • Additional reading
  • Exercises
7

Recognition

  • Instance recognition
  • Image classification
  • Object detection
  • Semantic segmentation
  • Video understanding
  • Vision and language
  • Additional reading
  • Exercises
8

Feature detection and matching

  • Points and patches
  • Edges and contours
  • Contour tracking
  • Lines and vanishing points
  • Segmentation
  • Additional reading
  • Exercises
9

Image alignment and stitching

  • Pairwise alignment
  • Image stitching
  • Global alignment
  • Compositing
  • Additional reading
  • Exercises
10

Motion estimation

  • Translational alignment
  • Parametric motion
  • Optical flow
  • Layered motion
  • Additional reading
  • Exercises
11

Computational photography

  • Photometric calibration
  • High dynamic range imaging
  • Super-resolution, denoising, and blur removal
  • Image matting and compositing
  • Texture analysis and synthesis
  • Additional reading
  • Exercises
12

Structure from motion and SLAM

  • Geometric intrinsic calibration
  • Pose estimation
  • Two-frame structure from motion
  • Multi-frame structure from motion
  • Simultaneous localization and mapping (SLAM)
  • Additional reading
  • Exercises
13

Depth estimation

  • Epipolar geometry
  • Sparse correspondence
  • Dense correspondence
  • Local methods
  • Global optimization
  • Deep neural networks
  • Multi-view stereo
  • Monocular depth estimation
  • Additional reading
  • Exercises
14

3D reconstruction

  • Shape from X
  • 3D scanning
  • Surface representations
  • Point-based representations
  • Volumetric representations
  • Model-based reconstruction
  • Recovering texture maps and albedos
  • Additional reading
  • Exercises
15

Image-based rendering

  • View interpolation
  • Layered depth images
  • Light fields and Lumigraphs
  • Environment mattes
  • Video-based rendering
  • Neural rendering
  • Additional reading
  • Exercises
A

Appendix A: Linear algebra and numerical techniques

  • A1 Matrix decompositions
  • A2 Linear least squares
  • A3 Non-linear least squares
  • A4 Direct sparse matrix techniques
  • A5 Iterative techniques
B

Appendix B: Bayesian modeling and inference

  • B1 Estimation theory
  • B2 Maximum likelihood estimation and least squares
  • B3 Robust statistics
  • B4 Prior models and Bayesian inference
  • B5 Markov random fields
  • B6 Uncertainty estimation (error analysis)
C

Appendix C: Supplementary material

  • C1 Datasets and benchmarks
  • C2 Software
  • C3 Slides and lectures

1

Introduction

2

Image formation

3

Image processing

4

Model fitting and optimization

5

Deep Learning

6

Recognition

7

Feature detection and matching

8

Image alignment and stitching

9

Motion estimation

10

Computational photography

11

Structure from motion and SLAM

12

Depth estimation

13

3D reconstruction

14

Image-based rendering

Any questions?
Check out the FAQs

Still have unanswered questions and need to get in touch?

Contact Us Now

  While the official computer vision certification exam typically carries a cost, we offer a computer vision certification free introductory module. This allows you to explore the uCertify platform and master the basics of image processing before committing to the full track.

It is a structured program that teaches the fundamentals and advanced techniques for enabling machines to interpret, process, and analyze visual data (images and videos). Key topics include image processing, object detection, and deep learning.

  Yes, it is a high-demand field with roles like Computer Vision Engineer, Deep Learning Engineer, and Research Scientist.

  Yes, most courses require basic knowledge of AI, Machine Learning, and programming proficiency in Python or C++.

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