This is a good idea, I was pondering upon awhile ago. I drop some thoughts. A Linear(no activations) MLP with a single hidden layer performs the same or sometimes better than a Multi-layer model with ...

You should remove low frequency content of the diagram. Simply use moving average filter. find the optimum filter size that produces your desired result. Here is a matlab example code. t=0:0.001:1; ...

Compressed sensing does not assume any distribution of non-zero elements in the input vector (signal), so it makes no difference if your non-zero elements are near each other or located uniformly on ...

Histogram Equalization can help as well. It tries to have the same distribution of pixel values in both images.

The random does not play a role when averaging is used to remove the noise. The distribution of noise does. Averaging works when the mean of the noise is zero. The assumption is that averaging noise ...

Regarding your questions: Is there a good rule of thumb for determining the length $L$ of the sections (second variable for the function)? $L$ (or window argument) specifies a window function to ...

Simply resize the binarized image to a larger image but do not use any high order interpolation in resizing and instead use 'nearest neighbor'interpolation technique. In MATLAB: im2 = imresize(image, ...

If by feature you mean a group of pixels (like Goofy) I suggest to try SIFT + SVD. (http://weitz.de/sift/)

This is called curriculum learning , and not only this approach is possible but also it is shown to be quite effective. A few years ago I also came up with similar idea. Basically the idea is, instead ...

Each histogram has 9 orientation bins and 4 histograms are concatenated hence 36 bins. I am not sure about HOG, but I assume alike SIFT algorithm, the original authors come up with trial and error for ...

Assuming your image is a $K$ bits depth image, and the quantized version is a $3$ bits version, pixels of the difference image, represent $(K-3)$ least significant bits of pixels of the original image....

One suggestion could be using image derivative. To get rid of effects of different shading and light, instead of BW, use derivative of the image in step 1.

Fourier Transom of these two surely would be different, but remember Fourier gives a global representation(features) of the images and you need local features to compare those, it is like that you ...

You are asking how scale invariant property is achieved with DoG. In fact, it is not! Scale invariance is not achieved by DoG but only through G part of it, through Gaussian filtering and it has ...

I'd like to suggest Professor Hoff and Professor Mubarak's great video lectures which are freely accessible here and here.

To complete the list I'd like to add a couple of weblogs that I personally follow: 1.Aishack (covers basic to advanced algorithms, intuitively and practically) 2.Py Image Search A blog, specialized ...

Interesting question. So you are asking why we do not use FFT instead of commonly used filters (in your case Since function). In terms of computational complexity, FFT wins over digital filtering. ...

First, for removing shadows try to normilize your RGB image prior to converting it to HSV color model. For more information on this regard see : http://aishack.in/tutorials/normalized-rgb/ In case ...

Since you are having a sequence of images from a scene or an object on your phone, I suggest to use a fairly simple method called Structure from Motion1.

Simply do it for each color channel, I mean for Red,Green and blue, for each form a separate matrix and then concatenate those, to form a 3D color image. fs = 0.08; //FOR RED CHANNEL W = 256; H = ...

There are various methods for denoising image signals. With a preface about image sparsity I'm going to provide some matlab code. Images are sparse in nature, not in spatial domain, but in some ...

I'v have some experience with TI C55 and C6000 series DSPs, I'll give you some points, hope to be helpful. Programming each chip that came from a different company requires different softwares or ...