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Where machine vision needs help from machine learning
Published on Aug 02, 201110532 Views
I'll describe where computer vision needs advances from computer science and machine learning. This talk will cover where computer vision works well: finding cars and faces, operating in controlled e
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Chapter list
Where machine vision needs help from machine learning00:00
Outline - 100:06
The Taiyuan University of Technology Computer Center staff, and me (1987) - 100:40
The Taiyuan University of Technology Computer Center staff, and me (1987) - 200:57
Me and my wife, riding from the Foreigners’ Cafeteria01:02
Me in my office at the Computer Center01:10
VISION01:23
Goal of computer vision01:35
Some particular goals of computer vision02:02
Companies and applications02:42
COGNEX02:57
POSEIDON03:26
Natatorium03:41
Saved by a computer lifeguard03:44
Mobil Eye03:58
1998 Journal publication04:42
Sony EyeToy, 200305:01
Microsoft Kinect, 201005:13
identix06:04
Google Image Search, Google Goggles - 106:20
Google Image Search, Google Goggles - 206:33
The Computer Vision Industry07:07
Games and Gesture Recognition07:17
Industrial automation and inspection07:21
Object Recognition for Mobile Devices07:24
Three-dimensional modeling07:52
Some particular goals of computer vision07:58
Outline - 209:16
Outline - 309:30
What makes computer vision hard? - 109:33
What makes computer vision hard? - 209:39
What makes computer vision hard? - 309:47
intra - class variation10:12
Object recognition issues10:36
Computer vision features over time11:21
What everyone looked like back then11:32
Features11:40
Objects11:46
Computer vision research results, 198611:57
Computer vision research results, 199212:37
Back to the present ...12:57
What has allowed us to make progress? - 113:15
What has allowed us to make progress? - 214:22
CVPR 2003 Tutorial - 114:29
CVPR 2003 Tutorial - 214:49
Invariant Local Features15:09
Hand under two different lighting conditions16:27
SIFT vector formation - 117:06
SIFT vector formation - 217:30
Feature stability to noise19:04
Feature stability to affine change19:38
Distinctiveness of features20:00
Figure 1220:34
Figure 1321:15
Building a Panorama - 121:39
Building a Panorama - 221:45
These feature point detectors and descriptors are the most important recent advance in computer vision and graphics21:56
S5ll another use for SIFT features22:28
Extarcting words22:50
Visual words23:16
Object recognition using visual words23:24
Now this starts to look like a learning theory problem24:15
What else do we want, to make progress?24:47
My poll of the top researchers in computer vision24:59
“How do you think computer science can best help computer vision?” - 125:21
“How do you think computer science can best help computer vision?” - 225:27
“How do you think computer science can best help computer vision?” - 325:36
Nearest neighbor search in high dimensions25:51
Fast Approximate Nearest Neighbors With Automatic Algorithm Configuration26:25
Comparison of different algorithms26:34
Additional structure present in NN problems for computer vision27:30
Another NN search problem, with structure: non-local means denoising28:31
Non-local means denoising algorithm28:52
An approx nearest-neighbor algo. that takes image spatial structure into account - 130:03
An approx nearest-neighbor algo. that takes image spatial structure into account - 230:20
An approx nearest-neighbor algo. that takes image spatial structure into account - 330:26
An approx nearest-neighbor algo. that takes image spatial structure into account - 430:36
An approx nearest-neighbor algo. that takes image spatial structure into account - 530:48
An approx nearest-neighbor algo. that takes image spatial structure into account - 630:58
An approx nearest-neighbor algo. that takes image spatial structure into account - 731:02
An approx nearest-neighbor algo. that takes image spatial structure into account - 831:30
An approx nearest-neighbor algo. that takes image spatial structure into account - 931:44
Problem - 134:11
Another commonly expressed need: help in scaling up algorithms34:27
Problem - 235:06
Outline - 435:11
Priors on images35:30
Removing camera shake - 136:42
Removing camera shake - 237:02
Close-up - 137:17
Close-up - 237:18
Close-up - 337:20
Image formation process37:29
Multiple possible solutions - 138:04
Multiple possible solutions - 238:08
Multiple possible solutions - 338:23
Multiple possible solutions - 438:30
Multiple possible solutions - 538:49
Is each of the images that follow sharp or blurred? 38:51
Picture - 138:57
Picture - 239:02
Picture - 339:04
Natural image statistics39:19
Blury images have different statistics40:05
Parametric distribution40:18
Three sources of information - 140:26
Three sources of information - 240:28
Three sources of information - 340:34
Three sources of information - 440:39
Bayesian estimate of latent image, x, and blur kernel, b.40:45
Original photograph41:23
Our output41:32
Matlab's deconvblind41:43
Close-up of garland41:48
A stronger prior might help us deblur this image42:06
Problem - 342:22
Texture Synthesis by Non-parametric Sampling42:30
Algorithm43:15
Picture - 443:27
Picture - 543:34
Picture - 643:38
Problem - 443:53
Special case of an image prior44:14
Some methods of approximate inference in MRF’s45:28
MRF/CRF wishes - 145:55
Input image46:10
MRF/CRF wishes - 246:57
Image Segmentation with Bounding Box Prior47:31
Other constraints48:37
Problem - 548:46
Outline - 549:01
Compressed sensing - 149:06
Compressed sensing - 249:15
Problem - 649:33
Large, noisy datasets49:45
Problem - 750:48
Shai Avidan50:51
Blind vision50:55
Problem - 850:57
me51:55
Continuous to discrete representations51:58
Problem - 952:52
Deva Ramanan52:59
Evaluate easily over a powerset of all segmentations53:01
Problem - 1053:24
Alyosha Efros53:28
Efros and Hoiem comments53:31
Really, we’d like another breakthrough...54:02
David Lowe - 154:18
David Lowe - 254:23
Most references are in citation list of this manuscript54:45
Computer vision academic culture54:57
A computer graphics application of nearest ‐ neighbor finding in high dimensions - 156:12
A computer graphics application of nearest ‐ neighbor finding in high dimensions - 256:17
The image database56:40
Obtaining semantically coherent themes56:46
Image representation57:15
Basic camera motions57:40
Scene matching with camera view transformations: Translation - 157:41
Scene matching with camera view transformations: Translation - 257:48
Scene matching with camera view transformations: Translation - 358:03
Scene matching with camera view transformations: Translation - 458:14
Scene matching with camera view transformations: Translation - 558:20
Scene matching with camera view transformations: Translation - 658:29
Scene matching with camera view transformations: Camera rotation - 158:55
Scene matching with camera view transformations: Camera rotation - 259:03
Scene matching with camera view transformations: Camera rotation - 359:07
Scene matching with camera view transformations: Camera rotation - 459:08
Scene matching with camera view transformations: Camera rotation - 559:09
More “infinite” images – camera translation59:11
Video - 159:19
Video - 259:47
Video - 301:00:08