55
votes
Accepted
Is deep learning killing image processing/computer vision?
On the top of this answer, you can see a section of updated links, where artificial intelligence, machine intelligence, deep learning or and database machine learning progressively step of the grounds ...
- 29.9k
22
votes
Is deep learning killing image processing/computer vision?
First, there is nothing wrong with doing grad work in image processing or computer vision and using deep learning. Deep learning is not killing image processing and computer vision, it is merely the ...
- 4,911
16
votes
Accepted
Feature extraction for sound classification
By long shot it is doable - to what extend? You will see. This task of environmental sound classification is not very well studied. Also choice of machine learning paradigm is crucial - statistical ...
- 10.5k
14
votes
What Kind of Features Can I Extract from a Signal
Some Features:
Mean.
Variance.
Skewness.
Kurtosis.
Dominant 3 frequencies in the DFT.
Energy of the 3 dominant frequencies.
Max Value.
Min Value.
Median.
Total Variation.
Usually I'd compute them in ...
- 40.7k
13
votes
Is deep learning killing image processing/computer vision?
No Deep Learning isn't killing Image Processing. You need huge datasets and lots of computational resources to do deep learning. There are plenty of applications where it is desirable to be able to do ...
- 453
13
votes
Is deep learning killing image processing/computer vision?
Today we had a discussion with a friend of mine. It was a rainy day here in Munich, while a large portion of Europe was having a kind of sunny atmosphere. People were sharing photographs in social ...
- 5,207
10
votes
Accepted
When is a network called end-to-end training?
From feature extraction to learning the desired result, deep learning algorithms can act as full pipelines for solving tasks at hand. End-to-end learning usually refers to omitting any hand-crafted ...
- 5,207
9
votes
A Machine Learning Based Algorithm as an Alternative to the Matched Filter
Sure, you can learn the matched filter, as convolution with a filter is just a function applied to a signal, and e.g. Neural Networks (through the universal approximation theorem) are good function ...
- 26.3k
8
votes
Is deep learning killing image processing/computer vision?
The short answer is, No. DL can recognize a mug in a photo, but this doesn't kill signal processing in anyway. That said, your question is quite relevant in these troubled days. There is a nice panel ...
- 236
7
votes
Accepted
Apply Principal Component Analysis (PCA) for RGB Images
General Idea
The general idea of Principal Component Analysis (PCA) is as following (Intuition over formalism):
Given a set of points in space (Inner Product Space) find a set of vectors (Directions) ...
- 40.7k
7
votes
Accepted
A Machine Learning Based Algorithm as an Alternative to the Matched Filter
The idea is to have a simple experiment to see if we can get, for a known signal, a better results than the Matched Filter for time delay estimation.
Experiment Objective
Generate, using ML (DL) a ...
- 40.7k
6
votes
Accepted
K-means for 2D point clustering in python
It can be done very easily with the scikit-learn. Examples are easy to find on their website, i.e. here. In my opinion it is the best way to go.
Modified code example from the above link:
...
- 10.5k
6
votes
Is deep learning killing image processing/computer vision?
Data engineering is still used in machine learning to preprocess and select the data fed to DNNs to improve their learning time and their evaluation efficiency. Image processing (the stuff between ...
- 34k
6
votes
Python: Least Squares Support Vector Machine (LS-SVM)
There is a package called FukuML.
In their description (Version 0.4.1) they write:
Support Vector Machine
Primal Hard Margin Support Vector Machine Binary Classification Learning Algorithm
Dual ...
- 40.7k
6
votes
Detect a Frequency Change in a Step Wise Frequency Chirp
There are many way to tackle this:
Time - Frequency Analysis
Classic choice would be a spectrogram but probably a Fourier Synchrosqueezed Transform would do a better job (Have a look at even more ...
- 40.7k
6
votes
Accepted
Classic Signal Processing vs Deep Learning / Machine Learning (DNN / ML) Based Signal Processing
This is an open discussion.
In my opinion, any field we couldn't figure near optimal solution (And in most cases we only have Linear Optimal solution) using Deep Learning will replace classic methods ...
- 40.7k
6
votes
Accepted
Which Programming Language Should Be Used for Deep Learning (Deep Neural Network [DNN])?
The language choice depends on many factors.
For instance, are you after developing low level features of DNN or using existing building blocks?
Most advanced and popular Deep Neural Networks (DNN) ...
- 40.7k
6
votes
Accepted
Unsupervised Clustering of Images: Which Algorithms?
Unsupervised clustering of image data is tricky thing and requires adjusting the method to the content of the images set.
Assuming we're dealing with the MNIST data set we can do some nice things ...
- 40.7k
6
votes
Accepted
Building a Pipeline for Image Classification / Clustering Tasks with Features Extractor and Dimensionality Reduction (Example on MNIST Data)
Feature Extraction
There are many modern known features for images. Among them:
BRISK Feature.
FAST Feature.
Harris Feature.
KAZE Feature.
MSER Feature.
ORB Feature.
SIFT Feature.
SURF Feature.
LBP ...
- 40.7k
5
votes
What Kind of Features Can I Extract from a Signal
In addition to the features mentioned so far I would like to mention measures of complexity such as:
Shannon Entropy
LZ Complexity
Fractal Dimension
There are also Fourier Descriptors (as hinted ...
- 10.1k
5
votes
Is deep learning killing image processing/computer vision?
A thorough understanding of signal processing (along with linear algebra, vector calculus, mathematical statistics etc.) is imo indispensable for non-trivial work in the field of deep learning, ...
- 180
5
votes
Accepted
What Is "Description Vector" in Image Processing?
I think you have a matrix.
Each Row / Column is a descriptor vector of a point in the image.
Just like having features, let's say M features, and each point has M values corresponding to M features.
...
- 40.7k
5
votes
Accepted
Relationship between information retrieval and source separation in signal processing
There are, a few discrepancies that might be making a difference here. My suggestion would be to edit the question for clarity. There are quite a few assumptions that lead to non-straightforward ...
- 10.1k
5
votes
Converting speech audio to telephone audio
Band-pass filtering with cut-off frequencies of 300 Hz and 3400 Hz should result in a good approximation. Try with a Chebychev filter or order not more than 6.
Then you may need to downsample your ...
- 4,701
5
votes
Accepted
Minimize the Cost Function of Values of Vectors Based on Their Amplitude
Since there is no prior at the Vector level this is basically element wise problem.
Moreover, if we assume the noise to be White Noise with zero mean then the answer can be very simple.
Since the ...
- 40.7k
5
votes
Accepted
The Meaning of $ \mathbb{E} $ Operator in the Pix2Pix Loss Formula of a Neural Network / Convolutional Neural Network
The operator $ \mathbb{E} \left[ \cdot \right] $ is the Expectation Operator.
In the context above it means you run over all the pairs of x, y and average the ...
- 40.7k
5
votes
Accepted
Explain the Process of Spectral Pooling and Spectral Activation in the Context of CNN in Frequency Domain
Wouldn't this be equivalent to discarding all those frequencies in the input layer?
No, it won't for 2 facts:
Just like a CNN isn't a linear regression due to having non linear function in between. ...
- 40.7k
5
votes
Accepted
Image Segmentation Using Deep Learning
Well, I think the best way to tackle this question is a little background and a code as an example. I chose MATLAB for this example though PyTorch / Keras would probably be as easy.
This task requires ...
- 40.7k
5
votes
Accepted
Image Clustering Using Linear Discriminant Analysis (LDA) Compared to t-SNE / UMAP
The Linear Discriminant Analysis (LDA) (Also the Fisher's Linear Discriminant, which the LDA is a generalization of) is a method to find a projection plane to separate data by linear projection Matrix ...
- 40.7k
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