Computer vision is a field of artificial intelligence (AI) that focuses on enabling computers to "see" and interpret images and videos, just like humans do.

Vision is one of the most powerful senses of a human.

Source

Source

Building Machines that can "see"

\( \text{Dr. Junsong} \)

\( \text{Yuan}\)

\( \text{Dr. Chen} \)

\( \text{Wang}\)

\( \text{Dr. Vishnu} \)

\( \text{Lokhande}\)

\( \text{Dr. David} \)

\( \text{Doermann}\)

... many more. Explore more here

Nerfies

Gaussian Splatting

OmniVision

• Basics of Computer Vision:
  • Image Formation, Camera Calibration
  • Image Processing, Feature Detection
• Intermediate Computer Vision:
  • Stereo Vision, Depth Estimation
  • Object Detection (I), ...other applications
• Learning-based Vision:
  • Convolutional Neural Networks,
  • Segmentation, Object Detection (II)
• Intro to Generative AI:
  • Variational Auto Encoders
  • Diffusion Models

\( \text{Zoom (Live)}\)

Course Website: naresh-ub.github.io/cvip

\( \text{Zoom (Live)}\)

Office Hours: Tuesday, Thursday 3:00 PM to 4:00 PM

• \( \text{Can this course be taken completely remote?} \)
  • \( \text{Yes, Including the Final Exams.} \)
• \( \text{Does this course satisfy the Capstone Course requirement?} \)
  • ​\( \text{Yes, if Fall 2025 is your Final Semester.} \)
• \( \text{Is there a Capstone Project associated with the course?} \)
  • ​\( \text{Yes.} \)​
• ​\( \text{What about the Attendance Policy?} \)
  • \( \text{Class Participation has \textbf{5\% Grade}.}\)

• \( \text{Naresh Kumar Devulapally} \)
  • \( \text{Computer Vision PhD Student} \)

• \( \text{Naresh Kumar Devulapally} \)
  • \( \text{Computer Vision PhD Student} \)
• \( \textbf{My Teaching Principles:}\)
  • \( \text{Intuition}\)
    • \( \text{Animated Content} \)
  • \( \text{Curiosity}\)
    • \( \text{Live Coding}\) 

• \( \text{Reveal.JS Slides}\) 
• \( \text{Manim}\) 

naresh-ub.github.io/cvip/website-features

• \( \text{Naresh Kumar Devulapally} \)
  • \( \text{Computer Vision PhD Student} \)
• \( \textbf{My Teaching Principles:}\)
  • \( \text{Intuition}\)
    • \( \text{Animated Content} \)
  • \( \text{Curiosity}\)
    • \( \text{Live Coding}\) 

• \( \text{Jupyter-Book}\) 
• \( \text{Teachbooks}\) 

naresh-ub.github.io/cvip/website-features

• \( \textbf{Curiosity} \)
• \( \textbf{Intent to Learn}\) 
• \( \text{Pre-Requisites:}\) 
  • \( \text{Linear Algebra}\) 
  • \( \text{Calculus}\)

• \( \textbf{Try to attend all classes!}\)
• \( \textbf{You will learn something new everyday!} \)

\( \text{Easy 5\%}\)

• \( \textbf{Any Regrading Requests must be made} \) \( \textbf{within 72 hours over Piazza or email.} \)
• \( \text{Grades will be curved based on the Class Average.}\)
• \( \text{There will be plenty of opportunities to score well in the course.}\)

• \( \textbf{Do Not Cheat! It's not worth it!} \)
  • \( \text{The entire course is designed to \textbf{encourage learning},}\)\( \text{rather than scoring well in exams.}\)
  • \( \text{Submissions with significant similarity (>70\%) }\) \( \text{shall be reported to Academic Integrity Office.}\)
  • \( \text{This will result in an F grade.}\)
  • \( \textbf{Reminder: This is a Capstone Course (Grad Students).}\)

• \( \textbf{Slides adapted from:}\) 
  • \( \text{CSE 4/573: CVIP by Dr. Junsong Yuan, Dr. David Doermann.}\)
  • \( \text{ML-4360 (Universität Tübingen) by Dr. Andreas Geiger.}\)
  • \( \text{First Principles of Computer Vision by Dr. Shree Nayar.}\)
  • \( \text{CMSC 491/691(UMBC): by Dr. Tejas Gokhale.}\) 

\( \textbf{Reference Books:}\)