This is by far the greatest course that I had on my entire life about computers. I work with full flight simulators for pilot training and many things that I learnt here became so clear for me... We see many systems in a very superficial way due to those abstraction levels and with those classes I can see what's behind the scene, what's going on in a deeper way. Thank you, guys. Thank you very much for sharing all this knowledge and in a way so simple and easy to understand. You're the best!!! And I'm recommending the channel for everybody I know that likes computer science on any level of understanding!
Our university's robotics team is currently using OpenCV so our autonomous drone can see and navigate the world. Lots of theory, documentation reading, and pulled hairs come along with computer vision, that's for sure.
I really love this show, it's a great way to introduce concepts before having a full lecture at a college class, or to have a wide general idea of what the career path will include.
I am currently studying Imaging Science at RIT and this is the best explanation I have ever found. One of the greatest refreshers of what’s going on sense I got here.
For anyone who's interested, there's a (relatively) recent system called YOLO: You Only Look Once. Version 2 came out less than a year ago, if I remember right, and basically it uses computer vision techniques to classify many different objects in a scene in real-time video. As in, it's fast enough to fairly accurately detect and label many different objects in an arbitrary scene 24 times per second (24fps is a standard video frame rate). It's super interesting! :D
Only the current frame, also you use in general, downloaded pre-trained models for weights, so you can start detecting things easily, you can add if want new detection, but of course it takes a lot of work, real time HD video needs around the GPU power of the GTX 1080 Ti, depending on the complexity of the weights, but you can trans-code a video, with the labels added on them, and watch later the final results.
@buda20, Thanks for referring to the type of gpu required for this, which answered one of questions as I'm building my own workstation for deep cnn, video object detection, ... Can you recommend minimum hardware specs? Seems to me a capable system has always been taken for granted. Thanks
You're an absolutely brilliant communicator! I'm doing a computer vision specialization on Coursera with the University of Buffalo and your high level intuition just gave me oodles of excitement. I dream of one day developing my own algorithm for real time navigation for data constrained systems. Thanks, really, this was a fabulous primer video, and certainly one I'll show my best friends. ☺️
Mam very nice video, Mam please also made full course videos also with very easy explanation & cover only those maths which require for that course. Because your explanation is very simple
Awesome video! How exactly are these image processing softwares implemented - would it be a low-level programming language like C, a high-level like Python or would it even be at the hardware level?
Shame no one answered before. Anyway, can be both. Python can be used in production and in testing (Really good libraries for complex computer vision like convolutional neural networks, object segmentation and so on). However when really high control over performance is needed, be it memory or computing speed, a low level language like c or c++ are used. Hope that helped :D
Self driving cars often (also) use LIDAR, which has the great advantage of knowing distances, so the car is able to see in 3d. The (biggest) exception to this is Tesla, which decided that normal cameras work just fine, to which I say sure, but why not make it even better?
Good quality LIDAR sensors needed for automotive applications are expensive. One of Tesla's goals was to ship all of their cars with the hardware needed for automated driving, long before their cars were capable of automated driving. Shipping the initial cars without this feature but with LIDAR might have been too expensive to be feasible. Using this approach, Tesla collected millions of miles of consumer data about typical driving conditions. This treasure trove of data enables them to understand under what conditions what sensors fare poorly and gives a large test set to compare algorithms on. I don't know if Tesla made the right decision. They might have problems getting their system working in urban areas or other challenging environments without the more detailed data that LIDAR provides, but the low cost of Telsa's sensor package enabled them to use consumers to collect data for Tesla, and that is a significant advantage for Tesla's engineering design team.
Convolution just happened to pop out from nowhere. In case you are wondering, convolution is the operation that maps a set of values (also called N-tuple where N stands for the quantity of elements) to another set of values. Very simple example: _1,2,3,4_ is a 4-tuple _+1,+1,+2,+2_ is a simple convolution _2,3,5,6_ is a 4-tuple as result of applying the above convolution
Lol I just imagined this in the next patch of Windows: If(user.faceEmotion=frustrated){ restartNotice.Postpone(2) }; Not that they would ever do that though...
KinaTrax uses computer vision to record kinematic data on baseball pitchers. Biomarkers are no longer a requirement and data can be tracked accurately in game. Computer vision is revolutionizing the game!
Good video Anne.. i need your insight on something... am working on recognizing partial occluded license plate. can you contribute to my research. thanks
When my Windows laptop will be able to recognize I'm not in the mood for an update, only then I'll pull that sticky tape off my webcam. That also means I'll never get updates :(
Does anyone know the titles of all the books in the background of the videos? The only ones I can make out are "Ghost in the Wires" and "Linear Systems and Signals".
The orange one is *Robot Builder's Bonanza* . The others are way too blurry to read unless someone recognizes the spine design. (I've tried extracting frames from the downloaded video and enhancing them... no luck :C) I guess we could always ask CrashCourse themselves? ^^
Machine vision will match ours when we can shrink 1000s of processors each capable of 1000s of petaflops to the size of an eyeball connected to the equivalent of the human brain's vision center.
The clip of the tracking of the fingers, arms, and face of the guy reading from the book makes me think that some day soon there will be a presentation or something where they show a computer detecting sleight of hand in a magic trick. Would be a pretty neat way to show off the accuracy, anyway.
Even if something that is subjective is in the code, it’s still *in the code*, meaning that the computer is not making its own decisions. Only the programmer makes decisions based on subjective ideas.
The point is that the user's inputs to the program are no longer under the complete control of the user. Typing & clicking the same things might not get the same behavior/output, based on some ML classifier trying to interpret your mood/intentions/... more or less well. This *affective computing* approach is very different from regular UI design.
This channel is for the jitterbug learners for whom reading and super breakout compress to the same function. Grant is more for the laid-back learners who leave good questions unsaid.
Your computer will detect when you are happy and start a forced 10GB update to swipe off the smile on your face.
lol
@@Lecadre2 or you can upgrade to Linux
@@Renard-w5o Well, being incapable to run games it doesn't necessarily mean that is worse, ain't only videogames out there , ya know
Your Windows computer*
This is by far the greatest course that I had on my entire life about computers. I work with full flight simulators for pilot training and many things that I learnt here became so clear for me... We see many systems in a very superficial way due to those abstraction levels and with those classes I can see what's behind the scene, what's going on in a deeper way.
Thank you, guys. Thank you very much for sharing all this knowledge and in a way so simple and easy to understand. You're the best!!!
And I'm recommending the channel for everybody I know that likes computer science on any level of understanding!
This is probably the best explanation of computer vision I've ever seen in my life.
Our university's robotics team is currently using OpenCV so our autonomous drone can see and navigate the world. Lots of theory, documentation reading, and pulled hairs come along with computer vision, that's for sure.
Dude, I know what you feel I have been learning machine learning and most of the times it gets very frustrating.
"not to ask for updates if you are frustrated" LOL this course is so informative and entertaining at the same time. Very good job!
I really love this show, it's a great way to introduce concepts before having a full lecture at a college class, or to have a wide general idea of what the career path will include.
I am currently studying Imaging Science at RIT and this is the best explanation I have ever found. One of the greatest refreshers of what’s going on sense I got here.
For anyone who's interested, there's a (relatively) recent system called YOLO: You Only Look Once. Version 2 came out less than a year ago, if I remember right, and basically it uses computer vision techniques to classify many different objects in a scene in real-time video. As in, it's fast enough to fairly accurately detect and label many different objects in an arbitrary scene 24 times per second (24fps is a standard video frame rate). It's super interesting! :D
Do you know if it uses the information it got from previous frames?
Awesome naming for it!
Only the current frame, also you use in general, downloaded pre-trained models for weights, so you can start detecting things easily, you can add if want new detection, but of course it takes a lot of work, real time HD video needs around the GPU power of the GTX 1080 Ti, depending on the complexity of the weights, but you can trans-code a video, with the labels added on them, and watch later the final results.
@buda20,
Thanks for referring to the type of gpu required for this, which answered one of questions as I'm building my own workstation for deep cnn, video object detection, ...
Can you recommend minimum hardware specs? Seems to me a capable system has always been taken for granted.
Thanks
Yes! 😁
Facinating to get to this one in 2023 in the context of where things have gone since.
Great video! I'm taking a Computational Vision course right now. It was nice to know what you were talking about.
Tahsin Loqman May I have your email address . I am interested in this course
Ooo, speech recognition and synthesis! I'm super excited for next week now - I'm a computational linguist, so this is my jam. Can't wait!
Seems like a convoluted way to process images.
Come up with a more efficient algorithm
I'll wait
I see what you did there.
@@dustinjames1268 Still, you've got to *recognize* that there's a *kernel* of truth to the criticism.
I've used Photoshop for years, it's really cool took take a look under the hood of image processing.
You're an absolutely brilliant communicator! I'm doing a computer vision specialization on Coursera with the University of Buffalo and your high level intuition just gave me oodles of excitement. I dream of one day developing my own algorithm for real time navigation for data constrained systems. Thanks, really, this was a fabulous primer video, and certainly one I'll show my best friends. ☺️
funny and clear! This series is the best.
The best online program, don't stop doin it!
I found the narrator very pleasant to listen to. Also the video was very good.
I *totally* understood all of this. Yeah, thats it...
Great lesson. I can't wait 'til next week. Thanks!
Wow, you did a great job of making something difficult easy to understand! This video was a great help!
I love computer vision with maths and all ❤
wonderfully explained
Can't wait for next week!
Just wondering where have you been 😊 Happy to see you again
Paused because I noticed the Ghost in The Wires book on your shelf. Bought this book after a Kevin Mitnick conference I saw last year :)
Love to see the passion this woman have for her job.
I lost my passion somewhere along the way.
Thanks a lot! It was a great introductory video to computer vision.
Way to go Carrie Anne!
Apart from face recognition, OCR is another nice field of research for 'teaching computers' to see !
The computer in the thumbnail looks like the one in Don't Hug Me I'm Scared Part 4. Which makes the topic even scarier.
First couple seconds of the Video, what a second that looks familiar, then realise it’s a footage of my hometown.
Mam very nice video,
Mam please also made full course videos also with very easy explanation & cover only those maths which require for that course.
Because your explanation is very simple
Awesome video! How exactly are these image processing softwares implemented - would it be a low-level programming language like C, a high-level like Python or would it even be at the hardware level?
Shame no one answered before. Anyway, can be both. Python can be used in production and in testing (Really good libraries for complex computer vision like convolutional neural networks, object segmentation and so on). However when really high control over performance is needed, be it memory or computing speed, a low level language like c or c++ are used. Hope that helped :D
@@TalSoikis Yeah awesome, thanks :D
This could be were Quantum computers shine. It can analyze all that data all at once basically seeing the bigger picture.
More useful than my whole semester CV course...
Self driving cars often (also) use LIDAR, which has the great advantage of knowing distances, so the car is able to see in 3d. The (biggest) exception to this is Tesla, which decided that normal cameras work just fine, to which I say sure, but why not make it even better?
Good quality LIDAR sensors needed for automotive applications are expensive. One of Tesla's goals was to ship all of their cars with the hardware needed for automated driving, long before their cars were capable of automated driving. Shipping the initial cars without this feature but with LIDAR might have been too expensive to be feasible.
Using this approach, Tesla collected millions of miles of consumer data about typical driving conditions. This treasure trove of data enables them to understand under what conditions what sensors fare poorly and gives a large test set to compare algorithms on.
I don't know if Tesla made the right decision. They might have problems getting their system working in urban areas or other challenging environments without the more detailed data that LIDAR provides, but the low cost of Telsa's sensor package enabled them to use consumers to collect data for Tesla, and that is a significant advantage for Tesla's engineering design team.
Very excellent explanation. Thanks for your videos. Please upload videos on machine learning and artificial intelligence.
Great video , very informative.
Best videos series ever about computer science,.,, Thank you..
CVision + Neural Network + Bad AI = me
by the way 5th
Lol. 😂
This is awesome
Convolution just happened to pop out from nowhere. In case you are wondering, convolution is the operation that maps a set of values (also called N-tuple where N stands for the quantity of elements) to another set of values.
Very simple example:
_1,2,3,4_ is a 4-tuple
_+1,+1,+2,+2_ is a simple convolution
_2,3,5,6_ is a 4-tuple as result of applying the above convolution
Wasn't AlexNet responsible for CNNs becoming a thing?
That's super cool^^ Thank you!!!
YOU ARE AMAZING!
thank you, this was helpful
At 5:52 you forgot to mention the bias value.
*connects a function generator to an oscilloscope in the background for some fun sciency atmosphere *
Extremely excellent. Thankyou.
Thanks for the greate video!
thats really convoluted
Lol I just imagined this in the next patch of Windows:
If(user.faceEmotion=frustrated){
restartNotice.Postpone(2)
};
Not that they would ever do that though...
You guys rock!!!!
Great !!
I love convolutional neural networks
Carrie Anne you look so cute with your glasses on, you should keep them on for all your videos
Make a video on Mercury cycle! Please
you make it
KinaTrax uses computer vision to record kinematic data on baseball pitchers. Biomarkers are no longer a requirement and data can be tracked accurately in game. Computer vision is revolutionizing the game!
Good video Anne.. i need your insight on something... am working on recognizing partial occluded license plate. can you contribute to my research. thanks
Will you guys be uploading after 2 weeks from now on as you did with this video ?
When my Windows laptop will be able to recognize I'm not in the mood for an update, only then I'll pull that sticky tape off my webcam. That also means I'll never get updates :(
Nice👍
Can anybody recommend a minimum hardware requirements for computer vision/object detection?
Thanks
what a sweet world would be one that has computers capable of awareness of their surroundings
She said kernel so many times i can’t quit thinking about popcorn
Hey, i know that place! Sydney Olympic park!
Would you share a link for further reading?
My PC is already quite aware of it's suroundings. Usually there's me and there will be a hammer if computer starts to misbehave.
Where can i find the sources for this video???
very like this video
GREAT-VIDEO!!😁💻👀👂👍
Plz leave a link to The Origin of Everything, would love to check it out.
The link is in the description.
Yay Fei-Fei Li! Watch her TED talk too.
When I started watching this video, I did not expect it would actually help me with my physiology course. I finally understand receptive fields :-D
Big brother 😎
yay!
....are internet connected microwaves a real thing?
Anyone else watch these on 0.75 speed?
Does anyone know the titles of all the books in the background of the videos? The only ones I can make out are "Ghost in the Wires" and "Linear Systems and Signals".
The orange one is *Robot Builder's Bonanza* . The others are way too blurry to read unless someone recognizes the spine design.
(I've tried extracting frames from the downloaded video and enhancing them... no luck :C)
I guess we could always ask CrashCourse themselves? ^^
Is the guy in the middle the secret brother Dave?
Machine vision will match ours when we can shrink 1000s of processors each capable of 1000s of petaflops to the size of an eyeball connected to the equivalent of the human brain's vision center.
I would trade all my privacy just so Windows do not ask to install updates when I'm mad!
The clip of the tracking of the fingers, arms, and face of the guy reading from the book makes me think that some day soon there will be a presentation or something where they show a computer detecting sleight of hand in a magic trick. Would be a pretty neat way to show off the accuracy, anyway.
That Macintosh in the back needs some serious retrobright treatment.
Dang, you're right. I shall watch an 8BitGuy restoration video to soothe myself now.
Ah I knew there'd be an 8 Bit Guy fan around here somewhere :D
"Abstraction is the key to build complex systems"
I was 100% in until 80% of the video. Then, it was like...
So the government is watching me through my webcam?
我想字幕 Who stole the subtitles?
CCTV camera？
Designer is a Liverpool FC fan I see.
I suppose these are the same kernels used in Photoshop
How did you get 147 ? I can't understand.
-185-186-186+233+233+233 = 142
May be the presentation error, but still the concept is clear with the next example which equals to 1
So.... How do you play sudoku
isn't it upper left corner?
On our path to Judgement day.. hehe
No edge!
Amazon Go is an example
A computing device should never change behavior depending on highly subjective factors, it should only do what it is explicitely told to do.
If my computer can't lie then it's not really alive!
But then it's just a faster calculator :(
Even if something that is subjective is in the code, it’s still *in the code*, meaning that the computer is not making its own decisions. Only the programmer makes decisions based on subjective ideas.
The point is that the user's inputs to the program are no longer under the complete control of the user. Typing & clicking the same things might not get the same behavior/output, based on some ML classifier trying to interpret your mood/intentions/... more or less well.
This *affective computing* approach is very different from regular UI design.
Hear and speak you say? Well...
Cool, this is Tesla's expertise.
She spoiled the next video of 3blue1brown! He's litteraly in the middle of the image recognition by deep learning subject
This channel is for the jitterbug learners for whom reading and super breakout compress to the same function. Grant is more for the laid-back learners who leave good questions unsaid.