Information Package / Course Catalogue
| Course Code | : ST427 |
| Course Type | : Area Elective |
| Couse Group | : First Cycle (Bachelor's Degree) |
| Education Language | : Turkish |
| Work Placement | : N/A |
| Theory | : 2 |
| Prt. | : 0 |
| Credit | : 2 |
| Lab | : 0 |
| ECTS | : 2 |
The aim of this course is to introduce basic image processing techniques and teach how these techniques are used in quality control, classification, counterfeit detection and automation systems of food and dairy products.
Image processing fundamentals: Image types, resolution, color spaces Image preprocessing techniques: Denoising, histogram equalization Object recognition, segmentation, and edge detection Feature extraction and classification Color, shape, surface, and defect analysis in food products Image processing applications in dairy products (cheese, yogurt, milk foam, etc.) Vision systems for counterfeiting and quality control Industrial camera systems and automation Fundamentals of image processing with deep learning Practical examples: Using OpenCV and Python Literature reviews and current research examples
| 1. | Defines and classifies image processing techniques. |
| 2. | Performs visual quality analysis for food and dairy products. |
| 3. | Numerates product features through image processing. |
| 4. | Develops basic applications with Python and OpenCV. |
| 5. | Explains the place of image processing systems in quality control and automation. |
| 6. | Develops models to determine food fraud with image data. |
| 7. | Contributes to the decision-making process by interpreting the findings obtained with image data. |
| 1. | Digital Image Processing – Rafael C. Gonzalez & Richard E. Woods |
| 2. | Computer Vision Technology for Food Quality Evaluation – Da-Wen Sun |
| Type of Assessment | Count | Percent |
|---|---|---|
| Midterm Examination | 1 | %40 |
| Final Examination | 1 | %60 |
| Activities | Count | Preparation | Time | Total Work Load (hours) |
|---|---|---|---|---|
| Lecture - Theory | 14 | 0 | 2 | 28 |
| Individual Work | 14 | 1 | 0 | 14 |
| Midterm Examination | 1 | 4 | 1 | 5 |
| Final Examination | 1 | 4 | 1 | 5 |
| TOTAL WORKLOAD (hours) | 52 | |||
PÇ-1 | PÇ-2 | PÇ-3 | PÇ-4 | PÇ-5 | PÇ-6 | PÇ-7 | PÇ-8 | PÇ-9 | PÇ-10 | PÇ-11 | |
OÇ-1 | 5 | 5 | 5 | 5 | 4 | 5 | 4 | ||||
OÇ-2 | 5 | 5 | 5 | 5 | 4 | 5 | 4 | ||||
OÇ-3 | 5 | 5 | 5 | 5 | 4 | 5 | 4 | ||||
OÇ-4 | 5 | 5 | 5 | 5 | 4 | 5 | 4 | ||||
OÇ-5 | 5 | 5 | 5 | 5 | 4 | 5 | 4 | ||||
OÇ-6 | 5 | 5 | 5 | 5 | 4 | 5 | 4 | ||||
OÇ-7 | 5 | 5 | 5 | 5 | 4 | 5 | 4 | ||||