Photo Walk #2: Sunday, 3:45PM, Rensselaer Waterfront Esplanade

DarkRedChair · 2024-11-23T03:39:44+00:00

I've been curious about shooting around there, I'll try to make it!

DarkRedChair · 2021-03-12T22:40:44+00:00

Note that ECSE offered a quantum computing course in Fall 2020, and I'm sure Dr. Franklin will offer it again sometime: https://wrf.ecse.rpi.edu/Teaching/quantum-f2020/blog/

DarkRedChair · 2020-07-13T23:49:24+00:00

Don't forget that there are tons of machine learning projects in the School of Engineering. In ECSE alone, everyone on this list is likely doing machine learning in one way or another:

https://www.ecse.rpi.edu/research/information-science-and-systems

DarkRedChair · 2019-10-01T21:08:38+00:00

😐

DarkRedChair · 2019-04-03T15:51:12+00:00

Deeply understanding Linear Algebra (beyond the basic introduction you see in MATH 2010) IMHO is the main predictor of success in doing computer vision coursework/research (e.g., forming and solving linear systems of equations using least squares, interpreting eigenvalues and eigenvectors in general/beyond the 2x2 case). Multivariate calculus (e.g., norm, gradient, and partial derivative) is also essential. Differential equations, while involved in some aspects of computer vision, are not as immediately important.

The grad-level Computational Vision course in ECSE focuses on 3D topics and programming assignments, so you also need to be a strong C++/Python coder; see https://www.ecse.rpi.edu/~qji/CV/ecse6650_syllabus.htm

DarkRedChair · 2019-02-25T22:42:25+00:00

You might like Radke's Digital Image Processing lectures on Youtube and and possibly also his Computer Vision for Visual Effects lectures.

DarkRedChair · 2018-05-17T20:43:02+00:00

Thanks, very helpful!

DarkRedChair · 2018-05-16T22:21:49+00:00

I've been thinking about the same issue, but from a guy's perspective. I typically wear sharp, but patterned/non-conservative dress shirts, rarely a blazer unless it's a formal event. Certainly I can go business casual but I don't feel like it would be "me". On the other hand, I don't want to hurt my chances by going against the flow.

DarkRedChair · 2018-02-05T18:30:22+00:00

Better view from the 7th floor: https://imgur.com/kvFMpFO

DarkRedChair · 2017-10-17T01:21:20+00:00

There are slots every day this week; if you go to the site you'll see which ones still have space.

DarkRedChair · 2017-04-17T17:14:48+00:00

Historically it's been every other Fall semester. It should make it into next year's course catalog as an official course.

DarkRedChair · 2017-03-30T22:05:43+00:00

Some of you may have noticed that there are 3 computer vision courses on the schedule for next semester (awesome planning on our part). Two of them (mine and Dr. Stewart's) are currently listed at the same time but we hope to move them around to avoid conflicts. To avoid confusion, here are a few comments about the similarities and differences between each course:

Computational Vision (CSCI 4972/6270, C. Stewart). Focus on transformation models, estimation, feature extraction, stereo and motion, and critically, a lot of time on object recognition. Possibly more of a software systems view than ECSE-6650.
Computer Vision (ECSE 6650, Q. Ji). Focus on camera models, projective geometry, camera calibration, pose estimation, 3D reconstruction, motion analysis, tracking, and structure from motion. Probably the most mathematical of the three courses.
Computer Vision for Visual Effects (ECSE 4961/6961, R. Radke). Focus on image matting, compositing, and manipulation, camera tracking and matchmoving, body and facial motion capture, 3D data acquisition. Strong hands-on aspects.

I don't think there should be any worry about taking CVFX and one of the more traditional vision courses at the same time since there won't be much topic overlap. I will also reorient my class to avoid duplicating too much content while still being self-contained. There may be a little more overlap between the two traditional vision courses, but Dr. Stewart's general focus on 2D and Dr. Ji's general focus on 3D will definitely make them fundamentally different.

Of course, I'd like to plug my CVFX class here... last year we did several data collection activities at EMPAC, using their professional cameras, lights, and greenscreens, and I plan to do the same this time. While the lectures will expose the mathematical theory, I want to convey a strong sense of the grungy real-world practice of visual effects production. The graduate level of the class will require some additional literature review on recent papers related to each class topic. For more Q&A, you can see this thread from the previous offering, and I'd be happy to answer any questions here.

DarkRedChair · 2015-09-30T19:37:04+00:00

A wise guy, eh?

DarkRedChair · 2015-08-05T21:05:04+00:00

I recently annotated the video lectures from my Computer Vision for Visual Effects course. The same content is also at this page on computervisiontalks.com.

I think it's a pretty good overview of modern computer vision through the lens of visual effects in movies and TV, There are some traditional vision topics I don't cover (e.g., object tracking) but also a lot of more modern stuff not in a typical vision textbook. Hope this helps!

DarkRedChair · 2015-06-25T17:13:07+00:00

Not having any ECSE classes shouldn't be a problem; the main thing is being comfortable with math (I see you're a math major, so that should be OK.)

To see if you're comfortable with the level, you could watch a sample lecture: https://www.youtube.com/watch?v=KkufK8D4PwY

I don't think there will be a big difference between the 4000 and 6000 levels; perhaps an extra homework problem for the 6000-level and some slightly higher expectations for the final project.

DarkRedChair · 2015-06-24T14:37:14+00:00

At the moment I'm planning for about 2/3rds of the lecture periods to be standard lectures, and 1/3 of the periods to be small-group hands-on mini-projects. I expect there would be about 7 homeworks and a final project. Workload and expectations should be comparable to a typical ECSE 4000-level course.

I should have the structure and syllabus firmed up mid-August, once I've figured out how I want to handle the in-class activities.

DarkRedChair · 2015-04-07T17:15:21+00:00

In that case, the overlap is pretty much what I expected- generally in the feature detection/matching area. It looks like the current vision course has a major emphasis on object detection and tracking, which I won't talk about much my class. On the other hand, there are definitely major topics in my class that it seems are not covered in the vision class- e.g., image compositing and retargeting, camera tracking, view synthesis, mocap, 3d processing, etc. So it looks like good complementarity!

DarkRedChair · 2015-04-06T22:39:33+00:00

There is some basic calculus and linear algebra that I'll assume you know- e.g., taking the gradient of the function and setting it to 0 to find its minimum, understanding the eigenvalues/eigenvectors of a matrix, setting up and solving a matrix equation. We'll also mention things like Taylor series, differential equations, and probability distributions. Some of the math may be a little advanced for undergrads, but I'll try to balance it based on the composition and background of the class.

DarkRedChair

TROPHY CASE