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.
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| Dönem | Course CPA | |
|---|---|---|
| 2025-2026 Fall | 3.57 | 1 sec · 3 öğr |
| 2024-2025 Fall | 1.65 | 1 sec · 4 öğr |
| 2023-2024 Spring | 3.40 | 1 sec · 5 öğr |
| 2022-2023 Spring | 3.60 | 1 sec · 5 öğr |
| 2021-2022 Spring | 3.27 | 1 sec · 6 öğr |
| 2020-2021 Fall | 3.62 | 1 sec · 20 öğr |
| 2020-2021 Spring | 2.23 | 1 sec · 6 öğr |
| 2019-2020 Spring | 3.60 | 1 sec · 10 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.
Course Learning Outcomes: Course Learning Outcome Assessment Apply basic concepts such as signals, systems, linearity, time-invariance, stability, frequency spectra, frequency response, and tools such as complex signal representation, transformations, filters Homework Quiz Midterm:Essay/written Design and implement a software system to meet desired needs Homework Term project Participate in a team work Term project Prepare reports with high standards in terms of content, organization, style and
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/
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 Total Workload: 0 Total Workload / 30: 0 / 30 0 ECTS Credits of the Course: 5 Type of Course: Lecture - Project Course Material: Slides Teaching Methods: Lecturing - Assignment - Presentations - Discussion