Computer Vision
A structured course dashboard for lectures, assessment guidance, and curated resources in modern computer vision.
Theory sequence
Course Modules
Introduction, Image Foundations and the Vision Pipeline
Frames computer vision as an ambiguous inverse problem with a wide application footprint, walks the task family and the end-to-end pipeline, then builds the image-signal foundations (sampling, color, convolution, gradients, scale) and the linear-algebra, calculus, and probability bridge used for the rest of the course.
Local Features and Feature Matching
Derives corner detection from the windowed SSD through the structure tensor and its eigenvalues to Harris and Shi-Tomasi, covers scale-space detectors (FAST, DoG/SIFT, SURF) and their repeatability and invariance, then builds SIFT, ORB, and HOG descriptors with the distances, ratio and cross-check tests, and failure analysis that make matching robust. Quiz 1 falls in this module.
Geometry, Projection and Camera Calibration
Builds the geometry core: the 2D/3D transformation hierarchy with composition and inversion, homogeneous and projective geometry with incidence and points at infinity, the pinhole camera and its K/R/t projection with scale ambiguity, camera calibration by DLT and Zhang's method, and homography estimation with Hartley normalization. The midterm examination follows this module.
Outcome mapping
Course Outcomes
Vision Foundations & Pipeline
Explain and analyze the fundamental concepts of computer vision, including the vision pipeline, image formation, representation, and basic visual structure, and relate these to real-world applications.
Feature Detection & Geometry
Apply and analyze classical feature-based and geometry-based techniques—feature detection and description, robust estimation, homography, and camera geometry—to establish correspondences and spatial relationships.
Motion, 3D Vision & Recognition
Interpret and evaluate motion analysis, 3D vision, classical object recognition, and learning-based pipelines to understand dynamic scenes and object-level perception in images and videos.
Vision Systems Design & Lab
Use computer vision concepts and tools to design, implement, and document complete vision systems for real-world applications through hands-on lab experiments.
Assessment structure
Marks Distribution
Reference desk
Teaching Materials & Tools
Textbooks
- Computer Vision: Algorithms and ApplicationsRichard Szeliski / 2nd EditionPrimary textbook
- Multiple View Geometry in Computer VisionHartley & Zisserman / Cambridge University PressGeometry reference
- Computer Vision: A Modern ApproachDavid Forsyth and Jean Ponce / Prentice Hall
- Pattern Recognition and Machine LearningChristopher M. Bishop / Springer