TÜRKÇE
ENGLISH
Information
| Unit | |
| Code | SD0015 |
| Name | Image Processing |
| Term | 2018-2019 Academic Year |
| Term | Fall and Spring |
| Duration (T+A) | 2-0 (T-A) (17 Week) |
| ECTS | 3 ECTS |
| National Credit | 2 National Credit |
| Teaching Language | Türkçe |
| Level | Belirsiz |
| Label | NFE Non-Field Elective Courses (University) UCC University Common Course |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Prof. Dr. NAZIM AKSAKER |
| Course Instructor |
Prof. Dr. NAZIM AKSAKER
(Güz)
(A Group)
(Ins. in Charge)
Prof. Dr. NAZIM AKSAKER (Bahar) (A Group) (Ins. in Charge) |
Course Goal / Objective
In this course, it is aimed to acquire the knowledge and skills of the images used in Astronomy, Remote Sensing and Geographic Information Systems with the help of IDL program processing techniques.
Course Content
Fourier analysis of images, astronomical image analysis, analysis of remote sensing images, use of HUBBLE data archive, use of meteosat data archive, use of Landsat data archive, usage of image data, image, raster and vector data types, command line operations, input / output operations, Use of modis data archive, Image processing techniques in sports encounters, Image processing methods of autonomic tools, Artificial intelligence applications in image processing
Course Precondition
Resources
Notes
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | It distinguishes image, photo, raster and vector data types. |
| LO02 | Algorithm is applied by programming. |
| LO03 | Connects between input / output operations. |
| LO04 | Apply image filtering techniques |
| LO05 | Applying fourier analysis to images |
| LO06 | The visual differential equation applies. |
| LO07 | Distinguish astronomical images, apply basic image processing methods. |
| LO08 | It uses astronomical data archives. |
| LO09 | Applies the classification techniques of educated / uneducated. |
| LO10 | Learning levels are determined. |
| LO11 | He knows the astronomical satellites and uses his archives. |
| LO12 | Recognize remote sensing satellites, |
| LO13 | Learn the image processing methods used by autonomous tools. |
| LO14 | Learn image processing techniques used in sports encounters. |
| LO15 | It uses artificial intelligence technique in image processing. |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Raster, vector data definitions, data formats (FITS, HDF5, GEOTYPE etc.) | Literature review on the subject | |
| 2 | Command-line operations, basic programming logic, algorithms, simple programs. | Literature review on the subject | |
| 3 | Making images visible, analyzing pixels, showing results, getting output. | Literature review on the subject | |
| 4 | Filters (nearest neighbors filter, bin filter etc.), application of interpolations by image. | Literature review on the subject | |
| 5 | Applying the image fast fourier transform. | Literature review on the subject | |
| 6 | Solution of first and second order differential equations by computer programming methods; Finish differences account; Runga-Kutta method; Newton-Raphson method; Bernoulli Equations; Difference method. | Literature review on the subject | |
| 7 | Practical Explanation of Conversion of Photometric Observations to Standard System; Atmospheric Mass, Heliocentric Time Correction, Reduction of Observations in UBV Filter System, First Order Atmospheric Damping Coefficients, Second Order Atmospheric Damping Coefficients, Instrumental Conversion Coefficients (Photometric Scale Factors), Reduction Applications. Conversion to Standard System. | Literature review on the subject | |
| 8 | Mid-Term Exam | Literature review on the subject | |
| 9 | Implementation of techniques for the identification of Pixellar objects | Literature review on the subject | |
| 10 | Determination of learning levels | ||
| 11 | Analyzes of astronomical observations of Hubble, SPITZER, JWST, FITS. | Literature review on the subject | |
| 12 | Analysis of HDF5 format data of meteorological and ground-based satellites. | Literature review on the subject | |
| 13 | Recognition of traffic applications and learning techniques | Literature review on the subject | |
| 14 | Identification of decision making systems and techniques used in sports encounters. | Literature review on the subject | |
| 15 | Application of classification techniques by artificial intelligence learning method in mass data, Examination of methods of producing satellite data. | Literature review on the subject | |
| 16 | Term Exams | ||
| 17 | Term Exams |
Assessment (Exam) Methods and Criteria
| Assessment Type | Midterm / Year Impact | End of Term / End of Year Impact |
|---|---|---|
| 1. Midterm Exam | 100 | 40 |
| General Assessment | ||
| Midterm / Year Total | 100 | 40 |
| 1. Final Exam | - | 60 |
| Grand Total | - | 100 |