defter*
defter / katalog / CS 484
CS 484

Introduction to Computer Vision

Image acquisition, sampling and quantization. Spatial domain processing. Image enhancement. Texture analysis. Edge detection. Frequency domain processing. Color image processing. Mathematical morphology. Image segmentation and region representations. Statistical and structural scene descriptions. Applications.

Credit3
ECTS5
BölümComputer Engineering
FacultyFaculty of Engineering
Prereq(CS 102 or CS 114 or CS 115) and (MATH 225 or MATH 220 or MATH 224 or MATH 241) and (MATH 230 or MATH 255 or MATH 260)

Hocalar 0 bu dönem · 4 geçmiş

Geçmişte ders veren (4 kişi)
Shervin Rahimzadeh Arashloo, Sedat Özer, Selim Aksoy, Ramazan Gökberk Cinbiş

→ STARS müfredatı / syllabus

Materyal — 0 dosya

Bu derste henüz materyal yok.

İlk dosyayı sen ekleyebilirsin — notlar, geçmiş finaller, çözümler, cheat-sheet, ne varsa. Drive linki / PDF / ZIP / fotoğraf, hepsi olur.

Şu an: mail at, ben düzenleyip yayına alayım. Form/upload UX yakında geliyor (Kimya tasarlıyor).

+ mail at ← katalog
↑ konuya CS 484 yaz

Geçmiş GPA dağılımı 22 dönem · ort. 2.70

DönemCourse CPA
2025-2026 Fall 2.87 1 sec · 40 öğr
2024-2025 Fall 2.62 1 sec · 48 öğr
2023-2024 Spring 2.44 1 sec · 60 öğr
2022-2023 Spring 2.70 1 sec · 56 öğr
2021-2022 Spring 2.62 1 sec · 35 öğr
2020-2021 Fall 2.75 1 sec · 33 öğr
2020-2021 Spring 3.11 1 sec · 22 öğr
2019-2020 Fall 3.09 1 sec · 37 öğr
2019-2020 Spring 3.12 1 sec · 29 öğr
2018-2019 Fall 2.71 1 sec · 51 öğr

Aggregate course GPA — Bilkent STARS'tan public data. Hoca-bazlı per-section detayı için STARS evaluation report →. Öğrenci anket cevapları KVKK kapsamında defter'de tutulmaz.

Müfredat detayı STARS syllabus

📚 Önerilen kaynaklar

  • Önerilen Computer Vision L. G. Shapiro and G. C. Stockman · 2001 · Prentice Hall
  • Önerilen Computer Vision: Algorithms and Applications R. Szeliski · 2010 · Springer
  • Önerilen Computer Vision: A Modern Approach D. A. Forsyth and J. Ponce · 2002 · Prentice Hall

⚠️ FZ engelleyen şartlar

There is no final exam.

🤖 GenAI politikası

We follow the Generative AI policy guideline of Bilkent University which can be found here: https://w3.bilkent.edu.tr/bilkent/generative-artificial-intelligence-genai-guideline/

📅 Haftalık müfredat

Introduction Digital Image Fundamentals Binary Image Analysis Linear Filtering Edge Detection Local Feature Detectors Color Image Processing Texture Analysis Image Segmentation Representation and Description Case Studies (Image classification, object recognition, deep learning) Case Studies (Image classification, object recognition, deep learning) Case Studies (Image classification, object recognition, deep learning) Case Studies (Image classification, object recognition, deep learning) ECTS - Workload Table: Activities Number Hours Workload Preparation for Quiz 2 4 8 Midterm exam 1 2 2 Individual or group work 14 2 28 Project (including preparation and presentation if applicable) 1 25 25 Preparation for Midterm exam 1 18 18 Course hours 14 3 42 Quiz 2 ,5 1 Homework 3 10 30 Total Workload: 154 Total Workload / 30: 154 / 30 5.13 ECTS Credits of the Course: 5 Type of Course: Lecture - Project Course Material: PP - Written Teaching Methods: Lecture - Case studies - Exercises - Assignment - Presentations