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Computational Photography course focuses on the impact of computation on the entire workflow of photography.  You will be introduced with the detailed discussion on the technical and computational aspects of forming pictures.

The capture and depiction of reality on a (mostly 2D) medium of images and the scientific, perceptual, and artistic principles behind image-making will be explored in the course. Especial emphasise will be given to the impact and role of computation that has changed the whole workflow of photography.

Other topics included are the relationship between pictorial techniques and the human visual system, new forms of cameras and imaging paradigms, and technical aspects of image capture and rendering.

Assessment

This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer needs to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.

Certification

Edukite courses are free to study. To successfully complete a course you must submit all the assignment of the course as part of the assessment. Upon successful completion of a course, you can choose to make your achievement formal by obtaining your Certificate at a cost of £49.

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Course Credit: Georgia Institute of Technology

Course Curriculum

Module: 01
1 M01 01 00:01:00
2 M01 01 about me 00:01:00
3 M01 01 overview 00:03:00
4 M01 01 overview structure 00:02:00
5 Requirements 00:03:00
6 Module 1 00:01:00
7 Module 2 00:01:00
8 Module 3 00:01:00
9 Module 4 00:01:00
10 Module 5 00:01:00
11 Module 6 00:01:00
12 Module 7 00:01:00
13 Module 8 00:01:00
14 What to Expect 00:03:00
15 M01 02 00:01:00
16 Lesson Objectives 00:02:00
17 What is Computational Photography 00:02:00
18 Computational Photography Combines 00:02:00
20 Limitations of Traditional Film Cameras 00:02:00
Module: 02
21 Computational Photography Enables Imaging 00:02:00
22 Elements of Computational Photography 00:04:00
23 Rays to Pixels 00:03:00
24 Summary 00:01:00
25 M01 03 00:01:00
26 Lesson Objectives 00:01:00
27 Recall Rays to Pixels 00:01:00
28 Novel Illumination 00:02:00
29 Novel Camera 00:01:00
30 Dual Photography 00:01:00
31 Reflective Properties of Ray of Light 00:01:00
32 Stanford Daul Photography 00:05:00
33 Summary 00:02:00
34 M01 04 00:01:00
35 Lesson Objectives 00:01:00
37 Dual Photo Pipeline 00:02:00
38 Taking Pictures 00:01:00
39 Matching to Warping 00:02:00
40 Detection and Matching 00:02:00
Module: 03
41 Warping 00:02:00
42 Fade Blend Cut 00:01:00
43 Five Steps to Make a Panorama 00:01:00
44 Summary 00:01:00
45 M01 05 00:01:00
46 Lesson Objectives 00:01:00
47 Cameras Processes 00:02:00
48 So why Study Cameras 00:01:00
49 Cameras are Everywhere 00:02:00
50 Sale of Cameras 00:02:00
51 Photos Taken 00:01:00
52 SLR to Camera Phone 00:02:00
53 Film to Digital 00:02:00
54 Comp Photo Benefits 00:02:00
55 Evolution of the Cameras p1 00:02:00
56 Evolution of the Cameras p2 00:02:00
57 Images in News 00:03:00
58 Kinds of Images 00:01:00
59 Evolution 00:02:00
60 Computer Vision and Computer Grpahics 00:02:00
Module: 04
61 Ultimate Camera 00:01:00
62 The Emerging Field 00:02:00
63 Summary 00:01:00
64 Representing an Image 00:01:00
65 Recall the Comp Photo Pipeline 00:02:00
66 Lesson Objectives 00:01:00
67 A Digital Image p1 00:03:00
68 A Digital Image p2 00:01:00
69 Pixel 00:01:00
70 Characteristics of a Digital Image 00:05:00
71 Digital Image is a Function 00:03:00
72 Black and White Image 00:28:00
73 Digital Image Statistics 00:02:00
74 Color Digital Image An Example 00:02:00
75 Digital Image FormatsDigital Image Formats 00:02:00
76 Exercises to do on your own 00:07:00
77 Read and write images 00:01:00
78 Understand image formats 00:01:00
79 Summary 00:01:00
Module: 05
80 M02 02 00:02:00
81 Lesson Objectives 00:01:00
82 Recall Digital Image is A Function 00:01:00
83 Point Process 01:23:00
85 Pixel Operations 00:04:00
86 Alpha blending 00:02:00
87 Summary 00:01:00
88 Intro 00:01:00
89 Lesson Objectives 00:01:00
90 A bit about the setup 00:03:00
91 The final output is a pixel blend 00:01:00
92 Blending Pixels 00:02:00
93 Arithmetic Blend Modes 00:02:00
94 Advanced Modes 00:02:00
95 Dodge and Burn 00:01:00
96 Darken 00:01:00
97 Summary 00:01:00
98 M02 03 00:01:00
99 Lesson Objectives 00:01:00
100 Digital Image is a Function 00:01:00
Module: 06
101 From Pixel Point Ops to Groups of Pixels 00:04:00
102 Smoothing Process Over an Image Using Average 00:04:00
103 Smoothing Process for the Edge of an Image 00:03:00
104 Observations 00:03:00
105 Observations Continued 00:02:00
106 A Mathematical Representation for Smoothing 00:03:00
107 A Mathematical Representation for Smoothing 00:02:00
108 Special Case Median Filtering 00:02:00
109 Median Filtering for Smoothing Images 00:04:00
110 Median Filtering for Noise Removal 00:01:00
111 Summary 00:01:00
113 Image Processing 00:01:00
114 A Mathematical Representation for Smoothing 00:01:00
115 Cross Correlation Method 00:02:00
116 Example Box Filter 00:01:00
117 Example Gaussian Filter 00:02:00
118 Using Gaussian Filters for Smoothing Cont 00:01:00
119 Filtering by a Kernel 00:05:00
119 Convolution Method 00:02:00
120 Convolution vs Cross Correlation 00:03:00
Module: 07
121 Convolution vs Cross Correlation 00:03:00
122 Properties of Convolution 00:02:00
123 Linear Filters 00:03:00
125 M02 05 00:01:00
126 Lesson Objectives 00:01:00
127 Recall Convoltion and Cross Correlation 00:01:00
128 Using Filters to Find Features 00:02:00
129 Good Features to Match Between Images 00:04:00
130 Edges in Real Images 00:02:00
131 Recall Images As Functions 00:01:00
132 Edge Detection 00:03:00
133 Derivatives of F to Get Edges 05:02:00
134 Differential Operators for Images 00:01:00
135 Image Gradient 00:03:00
136 Definition Image Gradient 00:01:00
137 Differentiating an Image in X and Y 00:02:00
138 Gradient Images Cont 00:02:00
139 Visualizing Gradients 00:01:00
140 Summary 00:01:00
Module: 08
141 Intro 00:01:00
142 Lesson Objectives 00:01:00
143 Recall Differentiating an Image in X and Y 00:01:00
144 Derivative as a local product 00:02:00
145 Derivative using cross correlation 00:03:00
146 Computing discrete gradients 00:04:00
147 Various Kernal for Computing Gradients 00:02:00
148 Impact of Noise on Gradients 00:02:00
149 Impact of Noise on Gradients 00:02:00
150 Convolution and Gradients 00:02:00
151 Gradient to Edges 00:03:00
152 Canny Edge Detector 00:04:00
153 Summary 00:01:00
154 Intro 00:01:00
155 Lesson Objectives 00:01:00
156 Recall Context of Computational Photography 00:02:00
157 Pixels vs Rays 00:02:00
158 Evolution of the Camera 00:03:00
159 Single Lens Reflex Camera 00:03:00
160 Cameras Without Optics 00:04:00
Module: 09
161 Cameras Without Optics 00:04:00
162 Camera Obscura 00:02:00
163 Pinhole Photograph 00:01:00
164 Pinhole Size and Image Quality 00:03:00
165 Light Diffracts 00:01:00
166 Effect of Pinhole Size 00:03:00
167 Replacing the Pinhole with a Lens 00:02:00
168 Geometrical Optics 00:01:00
169 Ray Tracing with Lenses 00:02:00
170 Summary 00:01:00
171 Intro 00:01:00
172 Lesson Objectives 00:01:00
173 Recall Ray Tracing With Lenses 00:02:00
174 Image Formation 00:03:00
175 Changes in Focal Length 00:02:00
176 Focussing 00:03:00
177 Field of View 00:03:00
178 Focal Length 00:01:00
179 Focal Length and Field of View 00:01:00
180 Sensor Sizes 00:01:00
Module: 10
181 Focal Length vs Viewpoint 00:02:00
182 Camera as a Window 00:01:00
183 A Camera Model 00:03:00
184 A Camera Model Perspective 00:01:00
185 Focal Length for Portraits 00:02:00
186 Summary 00:01:00
187 Intro 00:01:00
188 Lesson Objectives 00:01:00
189 Recall Focal Length vs Viewpoint 00:01:00
190 Exposure 00:02:00
191 Inside a Camera SLR 00:02:00
192 Shutter Speed 00:03:00
193 Aperture 00:03:00
194 Aperture Cont 00:01:00
195 Aperture Examples 00:03:00
196 Photographic Values of Aperture Shutter Focal 00:01:00
197 ISO 00:03:00
198 Exposure Triangle Examples 00:02:00
199 Aperture and Shutter 00:01:00
200 Exposure Triangle 00:01:00
Module: 11
201 Recap Exposure 00:02:00
202 Summary 00:01:00
203 Intro 00:01:00
204 Lesson Objectives 00:01:00
205 Recall Inside a Camera 00:01:00
206 Film vs Digital 00:05:00
207 Digital Converting Light to Data 00:03:00
208 Film Reaction Between Light and Chemicals 00:01:00
209 Digital Converting Light to Data p1 00:01:00
110 Digital Converting Light to Data p2 00:04:00
212 Bayer to RGB Demosaicing 00:01:00
113 Digital Converting Light to Data 00:01:00
114 CCD vs CMOS Sensors 00:03:00
215 Camera RAW File Format 00:03:00
216 Summary 00:01:00
217 M04 01 00:01:00
218 Lesson Objectives 00:01:00
219 Recall Images and Camera 00:01:00
220 Reconstructing a Signal 00:04:00
Module: 12
221 Octave 00:03:00
222 Reconstructing a Signal 00:02:00
223 A Fourier Transform 00:01:00
224 Frequency Domain of a Signal 00:03:00
225 Time Frequency and Frequency Spectra 00:01:00
226 Frequency Spectra 00:01:00
227 Convolution Theorem and the Fourier Transform 00:01:00
228 Freuency Spectra for Images 00:03:00
229 Freuency Spectra for Real Images 00:02:00
230 Fourier Transform Some Observations 00:02:00
231 Using the Frequency Spectra 00:01:00
132 Blurring and Frequencies 00:01:00
233 Summary 00:01:00
234 M04 02 00:01:00
235 Lesson Objectives 00:01:00
236 Recall Combine Merge Blend Images 00:01:00
237 Merging Two Images 00:02:00
238 Cross Fading Two Images 00:05:00
239 Cross Fading Window Size 00:01:00
240 Factors for Optimal Window Size 00:04:00
241 Frequency Spread Needs to be Modeled 00:03:00
242 Feathering 00:02:00
243 Summary 00:02:00
Module: 13
244 M04 03 00:01:00
245 Lesson Objectives 00:01:00
246 Optimal Window Size 00:01:00
247 Frequency Modeled 00:01:00
248 Pyramid Representation 00:04:00
249 Pyramid Representation of Images 00:01:00
250 Pyramid Representation of Images p2 00:02:00
251 Pyramid Representation of Images p3 00:03:00
252 Pyramid Representation of Images p4 00:01:00
253 Computing Gaussian and Laplacian Pyramids 00:01:00
254 Pyramid Blending 00:01:00
255 1 Blend 1 00:01:00
256 Pyramid Blending Process 00:02:00
257 Blend 2 00:01:00
258 00:01:00
Module: 14
259 M04 04 00:01:00
260 Lesson Objectives 00:01:00
261 Recall Combine Merge Blend Images 00:01:00
262 Cut Dont Blend 00:04:00
263 Finding the seams 00:02:00
264 Finding Seams 00:02:00
265 Extending Images 00:01:00
266 Editing Images 00:01:00
267 Editing Images 00:02:00
268 Seam Carving 00:01:00
269 Summary 00:01:00
270 Intro 00:01:00
271 Lesson Objectives 00:01:00
272 Recall Detection and Matching 00:02:00
273 Recall Detection and Matching 00:03:00
274 Finding Features 00:01:00
275 Characteristics of Good Features 00:03:00
276 Find Corners 00:03:00
277 Corner Detection The Basics 00:01:00
278 Corner Detection Mathematics 00:04:00
279 Corner Detection Mathematics p2 00:03:00
280 Eigenvalues 00:02:00
Module: 15
281 Harris Detector Algorithm 00:01:00
282 Properties of the Harris Detector 00:01:00
283 Scale Invariant Detectors 00:02:00
284 Invariant Local Features 00:01:00
285 Results 00:01:00
286 Summary 00:01:00
287 Introduction 00:01:00
288 Lesson Objectives 00:01:00
189 Corner Detection Mathematics 00:01:00
290 M Matrix 00:03:00
191 Recall Harris Corner Response Function 00:02:00
192 Harris Detector Step by Step 00:01:00
193 Harris Detector Workflow 00:02:00
194 Harris Detector Algorithm 00:01:00
195 Harris Detector Some Properties 1 00:02:00
296 Harris Detector Some Properties 2 00:01:00
297 Harris Detector Some Properties 3 00:02:00
298 Harris Detector Some Properties 4 00:01:00
299 Scale Invariant Detection 00:01:00
300 Scale Invariant Detection 2 00:01:00
Scale Invariant Detection 3 00:02:00
302 Scale Invariant Detection 4 00:01:00
Module: 16
303 Key Point Localization in Space 00:02:00
304 Scale Space Processed One Octave at a Time 00:01:00
305 Scale Invariant Detectors 00:01:00
306 Scale Invariant Detectors 2 00:01:00
307 SIFT 00:02:00
308 Feature Matching Demo 00:02:00
309 Summary 00:01:00
310 Intro 00:01:00
311 Lesson Objectives 00:01:00
312 Image Transformations 00:02:00
313 Parametric Global Warping 00:03:00
314 Parametric Global Warping Functions 00:03:00
315 Image Scaling 2D 00:01:00
316 2D Image Transformations 00:01:00
317 2D Rotation 00:02:00
318 2D Linear Transformations 00:02:00
319 Recall Images and Camera 00:01:00
320 Reconstructing a Signal 00:04:00
Module: 17
321 Basic 2D Transformation 00:03:00
322 Basic 2D Transformation p2 00:01:00
323 Affine Transformations 00:01:00
324 Projective Transformations 00:01:00
325 Recovering Transformations 00:01:00
326 2D Image Transformations 00:01:00
327 Example translation p1 00:01:00
328 example rotation p2 00:02:00
329 example shear p3 00:02:00
330 example warp using affine transform p4 00:03:00
331 example warp using perspective transform p5 00:02:00
331 Warping 00:03:00
333 Summary 00:01:00
334 M05 02 00:01:00
335 Lesson Objectives 00:01:00
336 Recall Image Transformations 00:01:00
337 Image Transformations vs Warping 00:01:00
338 Image Warping 00:03:00
339 Two Methods Forward Inverse 00:04:00
Module: 18
340 Minification 00:02:00
341 Recall Forward Warping 00:01:00
342 Recall Inverse Warping 00:01:00
343 Forward vs Inverse Warping 00:01:00
344 Mesh Based Warping 00:03:00
345 Image Morphing 00:01:00
346 Image Morphing Approaches 00:03:00
347 Feature Based Morphing 00:03:00
348 Demo Car Cheetah 00:02:00
349 Summary 00:01:00
350 Intro 00:01:00
351 Lesson Objectives 00:01:00
352 Review 5 Steps 00:01:00
353 Align Images Translate Warp 00:01:00
354 Bundle of Rays Contains All Views 00:03:00
355 Image reprojection 00:02:00
356 Recall Image Warping 00:01:00
357 Introducing Homography 00:02:00
358 Computing Homography 00:02:00
359 Solving for a Homography 00:01:00
360Warping into a shared coordinate space 00:01:00
Module: 19
361 Dealing with BAD matches 00:02:00
362 RANSAC 00:01:00
363 Building a panorama from two images 00:04:00
365 Warp example 00:01:00
366 Finding Panoramas 00:01:00
367 Summary 00:01:00
368 Intro 00:01:00
370 Dynamic Range in Real World 00:02:00
371 Dynamic Range 00:04:00
372 Limited Dynamic Range of Current Cameras 00:04:00
373 Relationship Between Image and Scene Brightness 00:05:00
374 Camera Calibration 00:02:00
375 Radiometric Calibration 00:03:00
376 Exposure Example 00:01:00
377 Series of Images 00:02:00
378 Response Curves 00:01:00
379 Response Curves p2 00:01:00
380 How to Compute 00:02:00
381 Radiance Map 00:01:00
382 Now to Display it 00:01:00
383 Tone Mapping 00:03:00
384 Summary 00:02:00
285 M05 05 00:01:00
286 Introduction 00:01:00
387 Depth (of a Scene) 00:02:00
388 Compute Depth Structure 00:03:00
389 Depth Ambiguity 00:01:00
390 Depth Cues 00:03:00
391 Trimensional 00:01:00
392 Depth Cues (Continued) 00:02:00
393 Estimating Depth Shape From One View 00:01:00
394 Stereo Vision 00:01:00
395 Why Stereo Vision 00:03:00
396 Why Stereo Vision (Continued) 00:01:00
397 Parallax 00:01:00
398 Depth Via Parallax 00:01:00
399 Stereo Photography and Stereo Viewers 00:01:00
400 Anaglyph 00:01:00
Module: 20
402 Making an Anaglyph 00:01:00
403 A Simple Stereo System 00:04:00
404 Stereo Disparity 00:02:00
405 Stereo Example 00:01:00
406 Computing Disparity 00:02:00
407 Recall A Simple Stereo System 00:01:00
408 No Matches 00:01:00
409 Effects of Patch Size 00:01:00
410 Some Well Known RGBD Cameras 00:03:00
411 Summary 00:02:00
412 M05 06 00:01:00
413 Lesson Objectives 00:01:00
414 Recall Panoramas 00:01:00
415 Photo Tourism Photo Synth 00:01:00
416 Photo Tourism 00:06:00
417 Photo Tourism Overview 00:01:00
418 Scene Reconstruction 00:01:00
419 Feature Detection 00:01:00
420 Pairwise Feature Matching 00:02:00
Module: 21
421 Correspondence Estimation 00:01:00
422 Structure From Motion 00:02:00
423 Incremental Structure From Motion 00:02:00
424 Photosynth Example 00:03:00
425 Google Maps Example 00:03:00
426 Google Streetview 00:04:00
427 Summary 00:02:00
428 All Intro 00:01:00
429 Intro 00:01:00
430 M05 07 outro 00:01:00
431 Intro 00:01:00
432 Outro 00:01:00
433 Intro 00:01:00
434 Outro 00:01:00
435 Introduction 00:01:00
436 Introduction 00:01:00
437 Recall A Digital Image 00:01:00
438 Video Images Over Time 00:03:00
339 Persistence of Vision 00:04:00
Module: 22
441 Feature Detection and Matching 00:01:00
442 Feature Tracking 00:04:00
443 Tracking, Registration in Video 00:01:00
444 Registration and Blending in Video 00:01:00
445 Summary 00:01:00
446 Introduction 00:01:00
447 Introduction 00:01:00
448 Recall Video is Images OVER Time 00:01:00
449 Video Textures 00:02:00
450 Video Clip to Video Textures p1 00:01:00
451 Video Clip to Video Textues p2 00:02:00
452 Similarity Metric -p1 00:01:00
453 Similarity Metric -p2 00:01:00
454 Finding Similar Frames -p1 00:02:00
455 Infinitely Long Video -Texture 00:01:00
456 Finding Similar Frames – p2 00:02:00
457 Preserving Dynamics with Transitions 00:01:00
458 Fading and Blending in Video 1 00:02:00
459 Not Just Fade Blend, but Cut 1 00:02:00
460 Video Portraits 00:02:00
Module: 23
461 Video Sprites 00:02:00
462 Clipets, Cinemagraphs 00:01:00
463 Summary 00:01:00
464 Introduction 00:01:00
466 Stabilized Video Example 00:02:00
467 Video Stabilization 00:01:00
468 YouTube Enhancement Suite 00:02:00
469 Video Stabilization Types 00:01:00
470 Recall Evolution of the Camera 00:01:00
471 Video Stabilization Types 00:03:00
472 Post Process Video Stabilization 00:01:00
473 Stable, Virtual Camera 00:02:00
474 Stabilization by Cropping 00:01:00
475 Post Process Video Stabilization 00:02:00
476 Motion Models 00:03:00
477 Similarity Model Over Time 00:01:00
478 Smoothing Camera Paths 00:02:00
479 Path Smoothing Demo 00:01:00
480 Re Synthesize New Path 00:01:00
Module: 24
481 YouTube Example 00:01:00
482 Recall CCD vs CMOS Sensors 00:02:00
483 Types of Electronic Shutters 00:01:00
484 Global Shutter Model 00:02:00
485 Rolling Shutter Wobble 00:02:00
486 Adaptive Shake Auto Crop 00:01:00
487 Alternative Stabilizer 00:01:00
488 Summary 00:01:00
489 Introduction 00:01:00
490 Lesson Objectives 00:01:00
491 Panoramic Video Textures 00:02:00
492 Recall Panoramas 00:01:00
193 Recall Video Texture 00:01:00
494 Video Registration 00:03:00
495 Video Textures of Dynamic Regions 00:01:00
496 Not Just Fade Blend, but Cut 00:02:00
497 Examples 00:01:00
498 Summary 00:01:00
500 Lesson Overview 00:01:00
Module: 25
501 Recall Photography (Light Rays) 00:02:00
502 Pinhole Camera and a Light Field 00:04:00
503 Parameterizing the Light Field 00:03:00
504 The Plenoptic Function 00:01:00
505 Light Fields (7 D) 00:02:00
506 Light Fields (4 D) 00:03:00
407 Visualization of a Light Field 00:02:00
508 Capture a Light Field, Store and Render 00:01:00
509 Light Field via a Pinhole Camera 00:02:00
510 Single Lens System 2 00:03:00
512 History of Light Field Camera 00:03:00
513 Light Field Examples 00:01:00
514 Summary 00:02:00
515 Intro 00:01:00
516 Lesson Objectives 00:01:00
517 Recall Computational Photography 00:02:00
518 Controlled Illumination p1 00:01:00
519 Lightstage 00:05:00
520 3D Scanning on Mobile Phone 00:02:00
Module: 26
521 Controlled Illumination 2 00:01:00
523 Projector Calibration with 1 Pixel Sensor 00:04:00
524 Light that is Aware of Obstructions 00:02:00
525 Programable Headlights 00:05:00
526 Room Alive 00:03:00
527 Summary 00:01:00
528 Introduction 00:01:00
529 Lesson Objectives 00:01:00
530 Recall Epsilon Photography 00:03:00
531 Coded Photography 00:02:00
532 Epsilon vs Coded Photography 00:03:00
533 Coded Photography 00:01:00
534 Lens and Defocus 00:03:00
535 Depth and Defocus 00:02:00
356 Depth and Defocus Challenges 00:02:00
537 Possible Approaches 00:02:00
538 Defocus as a local convolution 00:02:00
539 Coded Aperture 00:03:00
540 Benefits of Coded Aperture 00:01:00
Module: 27
541 Depth Estimation 00:02:00
542 Comparison Conventional Aperture Result 00:02:00
543 Coded Aperture 00:01:00
544 Flutter Shutter Camera 00:03:00
545 Traditional Camera Box Filter 00:01:00
546 Flutter Shutter Coded Filter 00:04:00
547 Different Codes 00:01:00
548 Summary 00:02:00
549 Have fun computing with photographs! 00:01:00
Assessment
Submit Your Assignment 00:00:00
Certification 00:00:00

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