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; Lecture
 
; Lecture
: Wednesday 9:50, M-I
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: Wednesday 9:50, M-IX
 
; Excercises
 
; Excercises
: Monday 18:10, F1-248
+
: Monday 17:20, I-H3
  
 
=== Grading ===
 
=== Grading ===
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* Final writen exam: 50p
 
* Final writen exam: 50p
 
* Excercises: 50p ''(with minimum of 30p)''
 
* Excercises: 50p ''(with minimum of 30p)''
** 4 home assignments: 4x10p (2 from CG part and 2 from IP)
+
** 2 home assignments (projects): 2x20p (1 from CG part and 1 from IP part)
 
** Attendance: 10x1p
 
** Attendance: 10x1p
 
                                        
 
                                        
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----
 
----
 +
== Lectures ==
  
 +
[http://www.sccg.sk/~madaras/students.html CG slides ]
 +
*3.11. Snimanie [https://docs.google.com/presentation/d/1mxQZt2V2fwaNZlZ2c4QVOXlJunyBBbGRfIv68yjBZXQ/edit?usp=sharing slides]
 +
*10.11. Predspracovanie [https://docs.google.com/presentation/d/1JHeZS8IlN41tR_7KbLc_6KDX_qKnLIBN_I6mb82mlyw/edit?usp=sharing slides]
 +
*24.11. Segmentacia [https://docs.google.com/presentation/d/1Rgos4gePcXCjhmhwu-DUto18yKvQrUifJZnnr8k2-ms/edit?usp=sharing slides]
 +
*1.12. Image classification and Pattern recognition [https://docs.google.com/presentation/d/1uXw1ATBRvj6P2426PE0Mejpb2TksVrsu-xOiJkWTIL0/edit?usp=sharing slides]
 +
*8.12. Deep learning for computer vision, online cez Teams [https://docs.google.com/presentation/d/1aZ0iLF1DM8TtB_J6rGyWVRGHP7EhJiTeQeWlV3z7pVE/edit?usp=sharing slides]
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*15.12. Exam, online cez Teams
  
= Labs - CG =
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== Labs ==
  
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* Slides & project assignment: [https://github.com/danasko/zpgso GitHub]
 +
<!---
 
Guide: Adam Riečický
 
Guide: Adam Riečický
  
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[https://docs.google.com/presentation/d/14luAuPU26NjKMbQRf7leVxUwQcGh0ZLBmvQ4uR4Kerc/edit?usp=sharing Slides #03]
 
[https://docs.google.com/presentation/d/14luAuPU26NjKMbQRf7leVxUwQcGh0ZLBmvQ4uR4Kerc/edit?usp=sharing Slides #03]
<!----
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[https://docs.google.com/presentation/d/1IXT5HbH1q7LhgGfYJkI5khzezfBqzTT1jnp3d15VWGY/edit#slide=id.p Slides #04]
 
[https://docs.google.com/presentation/d/1IXT5HbH1q7LhgGfYJkI5khzezfBqzTT1jnp3d15VWGY/edit#slide=id.p Slides #04]
  
[https://docs.google.com/presentation/d/1y2rWsXCrkgj68UuLU-ELztN57ofnoie7oG5DmgUJXKI/edit#slide=id.g29c1b949f2_0_0 Slides #05]---->
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[https://docs.google.com/presentation/d/1y2rWsXCrkgj68UuLU-ELztN57ofnoie7oG5DmgUJXKI/edit#slide=id.g29c1b949f2_0_0 Slides #05]
  
 
== Project ==
 
== Project ==
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=== Stage 2 (10p) ===
 
=== Stage 2 (10p) ===
''Deadline 13.12.2020 23:59''
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''Deadline 22.12.2020 23:59''
  
 
Add transformation controls to your tool.
 
Add transformation controls to your tool.
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=== Stage 3 (7p) ===
 
=== Stage 3 (7p) ===
''Deadline 13.1.2021 23:59''
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''Deadline 27.1.2021 23:59''
  
 
Enhance existing visualization tool by implementing Blinn-Phong Lightning Model
 
Enhance existing visualization tool by implementing Blinn-Phong Lightning Model
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: [https://dai.fmph.uniba.sk/upload/b/bd/Zpgso_pr2_scr1.png screen 1]
 
: [https://dai.fmph.uniba.sk/upload/b/bd/Zpgso_pr2_scr1.png screen 1]
 
: [https://dai.fmph.uniba.sk/upload/7/71/Zpgso_pr2_scr2.png screen 2]
 
: [https://dai.fmph.uniba.sk/upload/7/71/Zpgso_pr2_scr2.png screen 2]
 +
 +
=== Bonus Points ===
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''Submit with Stage 3''
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 +
Extend your visualization options by the following features to earn bonus points. Attention: Your point total (including bonus points) will not exceed 25 points for exercises from computer graphics part.
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; Material Properties (1p)
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: Expose material properties ka, kd, ks, shininess, and color to GUI. Re-render image when the user changes these properties.
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 +
; Lighting Model Selection (2p)
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: Implement a switch that allows the selection between two lighting models (Phong vs Blin-Phong). The difference should be minimal but it is a proof of concept.
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 +
; Light Types (3p)
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: Add possibility to select the light type (point light, sun/directional light) - the GUI option should also change from "light direction" to "light position".
 +
--->

Verzia zo dňa a času 11:23, 8. december 2021

Fundamentals of Computer Graphics and Image Processing 1-AIN-301

Course information sheet >

Lecture
Wednesday 9:50, M-IX
Excercises
Monday 17:20, I-H3

Grading

You can get 100 points (p) during semester, where 1pt = 1% of final grade

  • Final writen exam: 50p
  • Excercises: 50p (with minimum of 30p)
    • 2 home assignments (projects): 2x20p (1 from CG part and 1 from IP part)
    • Attendance: 10x1p

Materials



Lectures

CG slides

  • 3.11. Snimanie slides
  • 10.11. Predspracovanie slides
  • 24.11. Segmentacia slides
  • 1.12. Image classification and Pattern recognition slides
  • 8.12. Deep learning for computer vision, online cez Teams slides
  • 15.12. Exam, online cez Teams

Labs

  • Slides & project assignment: GitHub