Recent content by Avatrin

Hi Several attention mechanisms require trainable matrices and vectors. I have been trying to learn how to implement this in Tensorflow w/ Keras. Every implementation I see use the Dense layer from Keras, but I have a tendency to get lost trying to understand why and what they do afterwards...

Well, I've already skimmed through that one. It is about the algebraic Riccati equation, and does mention that it can be applied to the differential Riccati equation. However, I just don't know how. So, my best idea at the moment is just to use Euler's method. However, I wish I could find out...

Hi I need to solve an equation of the form $$\dot{X}(t) = FX(t) + X(t)F^T + B$$ All of these are matrices. I have an initial condition X(0)=X_0. However, I have no idea how to proceed. How can I make any progress?

Hi I am trying to learn optimal estimation by reading Gelbs Applied Optimal Estimation, and I am having hard time with finding \Gamma defined as the following: $$ \Gamma_k w_k = \int_{t_k}^{t_{k+1}} e^{F(t_{k+1} - \sigma)} G(\sigma) w(\sigma) d\sigma$$ Here F is a known matrix. So is G, and w...

Okay, thanks to both of you! I'll read through those books to see if they help. But, no, currently my interest is not specifically Kalman filtering; It's more the foundations I need to understand Kalman filtering. In other words, I want to understand the first few chapters of any book on the...

Currently, I am using Brown and Hwangs Random Signals and Applied Kalman Filtering. My background is mathematical; I have encountered measure theory in the context of integration theory. Moreover, I have taken courses in analysis and topology. Statistics and probability theory is something I...

Summary:: Random processes, autocovariance, ergodicity, Gauss-Markov etc Hi I am a person who resolutely prefers depth over breadth, and currently I am trying to learn more about random signals and Kalman filtering. However, the books I have found so far will mention and superficially...

Okay, here's what I do; I stary with noisy_image as described above. I use it with LIC to visualize a vector field and the output is output_image, which, yes, is an array containing floating point numbers between 0.0 and 1.0. Using this with figimage does give me the correct output in my...

Hmm, this actually didn't work. This is my current output having turned each element in my array to an integer: However, I am not opening any image; I am just doing this: img = Image.fromarray(np.array(output_image), 'LA') img.save('pillowwithint.png') The reason for this is that LIC starts...

Thank you! That makes a lot of sense. I guess I should try using int(floor(element*256)) for each element in the array to see if it gives me the output I want. Regarding the parameters. Yeah, figimage takes a lot more parameteres than I show, but I was alright with the default values (for...

The other day I was trying to visualize a field using line integral convolution. I thought I kept failing for days since Pillow was giving me outputs similar to this one (img = Image.fromarray(output_image, 'L')): I thought I was making some mistake until I tried Pyplot...

Hi, the above image is from the Line Integral Convolution paper by Cabral and Leedom. However, I am having a hard time implementing it, and I am quite certain I am misreading it. It is supposed to give me the distances of the lines like in the example below, but I am not sure how it can. First...

Those clearly use pillow... I am specifically asking for how to do this with OpenGL.

Alright, but this doesn't output an image. That's why I am asking for a tutorial or a book; I am not sure a forum post is going to tell me how to create images with OpenGL.

Hi I am learning how to do a line integral convolution with OpenGL given a vector field. So, as a first step, I need to learn how to create an nxn noise image. Are there any good tutorials/books I can use to learn how to do this?