Teaching machines to see — image formation, digital image fundamentals, low-level vision and filtering, edge and line detection, corners and local features (Harris, HoG, SIFT). This vault holds the same resources as every subject: cheatsheet, slides explained, question bank, formula sheet, book explained, and references. Covers CV1–CV7 (mid-sem), AIMLCZG525.
The full mid-sem paper worked end-to-end — collapsible Q&A — plus a makeup-exam study guide on the same concepts.
01 · cheatsheetDense one-glance reference — image formation, histograms, convolution, gradients, Canny, Hough, Harris, HoG, SIFT. CV1–CV7.
02 · slides explainedEvery lecture slide unpacked in plain easy words — the why behind filtering, edges, corners and features. CV1–CV7 (mid-sem).
03 · question bankExam-style questions pulled straight from the slides. Questions only — no answers — plus worked exercises. CV1–CV7.
04 · formula sheetEvery equation for the mid-sem — histogram equalization, convolution, gradients, Canny, Hough, Harris response, HoG, SIFT — each symbol defined.
05 · book explainedSzeliski Ch2–4, chapter by chapter, in easy language with diagrams, worked examples, gotchas, and a concept cheatsheet.
06 · referencesTextbooks (Szeliski, Sonka), Stanford CS231n, and the key papers — Harris, SIFT, HoG — worth keeping.
01 · CHEATSHEET
A dense, scannable card: image-formation and the pinhole model, histograms and intensity transforms, convolution and smoothing, gradients and edge models, Canny's pipeline, the Hough transform, Harris corners, HoG, and SIFT — to cram from before the mid-sem.
02 · SLIDES EXPLAINED
Each lecture deck, slide by slide, rewritten in plain easy words — full concepts not bullet points. The intuition behind image formation, histogram equalization, filtering, edge and line detection, Canny, Hough, Harris corners, HoG and SIFT, with the why spelled out. Covers CV1–CV7 (mid-sem).
03 · QUESTION BANK
Questions extracted directly from the CV1–CV7 slides — questions only, no answers — grouped by lecture, for active recall and exam practice. Work them cold, then check yourself against the slides explained.
04 · BOOK EXPLAINED
Companion notes for Richard Szeliski — Computer Vision: Algorithms and Applications (Ch 2–4) and Sonka, Hlavac & Boyle — Image Processing, Analysis, and Machine Vision, chapter by chapter, in plain language with diagrams and worked examples.
05 · REFERENCES
Key textbooks (Szeliski, Sonka), the foundational papers (Harris & Stephens, Lowe's SIFT, Dalal & Triggs HoG, Canny), lecture series (Stanford CS231n), and extra reads worth keeping.