10 votes
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Universal bases (dictionary) for image compression

This is a great and interesting question. There are 2 ways to look at it, empirically and analytically. But before we start, a major detail is that when dealing with images we mainly talk about the ...
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10 votes
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Number of dimensions? Color image vs gray scale image? Colour video vs gray scale video? Especially in the context of MATLAB

Color images are usually modeled as a vector valued function of 2D: $$ I : \mathbb{R}^{2} \to \mathbb{R}^{3} $$ Namely for 2D coordinates input it outputs 3 values (RGB). Hence images are 2D functions....
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6 votes
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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 ...
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6 votes
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When to Use Composite Filters and When to Use Separable Filters?

Image Processing Context In classic Image Processing the filters used are known. Hence being separable is a property of a given filter which is suitable to the task. In this context, separability only ...
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5 votes
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Understanding the Bilateral Filter (Image Filtering)

There are some good resources on our site: Understanding the Bilateral Filter - Neighbors and Sigma. How to Validate Bilateral Filter Implementation? What Is the Bilateral Filter Category: LPF, HPF, ...
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5 votes
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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 ...
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5 votes
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Rectangle Segments of Image (Rectangle Super Pixels) per Pixel

One answer for your question would be limiting Super Pixels to rectangle forms. It requires changing the code of a Super Pixel algorithm to constraint the shape of the Super Pixel. Another approach ...
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5 votes

Universal bases (dictionary) for image compression

As a complement to the neat answer by @Royi, I would add that "sparsity" is originally a heuristic principle in science, that applies well to many interesting really world data and problems. ...
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4 votes

Slicing an Image into Tiles According to Content

Under the following assumptions (Written for the vertical lines, same for the horizontal): The background is uniform. There at least a gap of 2 background pixels between the closest objects on ...
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4 votes

Image Standardization for Image Classification (Machine / Deep Learning)

I will display image standardization using MATLAB: ...
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4 votes

Detecting bullet holes using Python with camera or sensors

I can't really comment on the machine vision part, other than any question that asks "How do I do <some signal processing task> in <some language>" is fairly naive. The way you ...
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4 votes

Number of dimensions? Color image vs gray scale image? Colour video vs gray scale video? Especially in the context of MATLAB

My answer became quite long. So normally a digital signal is 1D, a digital image is 2D, a video is 3D. But it can get complicated. Long introduction on mathematics for the start. In mathematics, the ...
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3 votes
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Comparing distribution of vectors with different length?

From what you've said, you have two sample sets: $$x_n, n = 0 \ldots N-1$$ and $$y_m, m = 0 \ldots M-1$$ where $M \ne N$ and you want to compare the distributions of the underlying processes. Do you ...
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3 votes

Alternative to support vector machine?

I think that the best option, probably, would be Random Forests or any other Boosting / Bagging method based on decision trees. I would probably start with SK Learn Random Forests. As more advanced ...
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2 votes

Edge Detection Convolution Intuition

If it helps, try a simpler version. Just look at two neighbouring pixels in a row. First example: There is 10 and 10. Difference between them is 0, no difference, no edge. Second example: In the ...
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2 votes

Number of dimensions? Color image vs gray scale image? Colour video vs gray scale video? Especially in the context of MATLAB

Depends what "dimension" means, but I'll say 2D, and interpret in context of convolutions. A convolution operates on 1D: (channels, time) 2D: ...
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2 votes

How to perform Spatial derivative calculation?

What you should do instead to calculate $I_x$: $$ \begin{align} I_{x}(x,y)&=\frac{\partial I(x,y)}{\partial x}\\ &=I(x+1,y)-I(x-1,y)\\ &= \begin{bmatrix}5& 7 & 11\\ 9 & 12 &...
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2 votes

Fixing distortion in photographic reproductions

I answered a related question in SO some time ago. Briefly, you can work your setup through a standard camera calibration procedure, and you have the option of "freezing" the focal length to ...
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1 vote
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Fixing distortion in photographic reproductions

In a general setting, multiple observations of a calibration target are needed because otherwise the focal length parameter(s) and principle point parameters are coupled so you would not be able to ...
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1 vote

Slicing an Image into Tiles According to Content

Thanks to user Ash for giving me a key insight: Measuring the HSL (hue, saturation, and lightness) of the pixels can help detect where the gaps are. If I take one horizontal row of pixels that goes ...
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1 vote

Universal bases (dictionary) for image compression

Families of basis functions that are roughly sinusoidal are efficient for images I think because they can efficiently encode an edge at any spatial location. In images it is typical to have edges ...
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1 vote
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If a camera optical axis is perpendicular to a flat surface, does the area covered by a pixel is the same for all pixels?

For an ideal pinhole camera model, your geometric reasoning is correct: the projected pixel area on an orthogonal flat world-surface will be independent of sensor pixel position on the flat image-...
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1 vote

What transformations to go from top-down fisheye to occupancy (ground texture)

Allright well, @TimeWescott was essentially correct about what I had to do, though the theory took some investigation and thought before I realized what the parts to the puzzle are, and the inherent ...
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1 vote
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Optical-flow visualizations explained

Yes, as your linked page shows: the idea is that the pixels move from $(x,y)$ in the first from to $(x+\Delta x, y + \Delta y)$ in the second. I believe the image you quote is just connecting a ...
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1 vote

When to Use Composite Filters and When to Use Separable Filters?

For any given problem definition, there's a filter that -- if you ignore execution time and hardware expense -- is "best"*. In general, that "best" filter isn't separable. ...
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1 vote

Understanding the Bilateral Filter (Image Filtering)

A simple filter (convolution) replace value of a pixel (for simplicity assume its the pixel positioned at the center of kernel) with a weighted sum of its neighbors and the weights are constant ( the ...
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  • 1,306
1 vote

Projecting a 3D point onto Multiple Cameras in the Same Projective Frame

It appears that my problem was normalization. The mentioned Algorithm for calculating the cameras makes use of the epipoles $e'$ and $e''$ (i.e., the images of the first camera's focal point in the ...
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1 vote

Help or suggestions with Line detection in microscopy images

Instead of using a homegrown amalgamation of algorithms, I suggest you look in the scientific literature for existing solutions. People have solved the same problem over and over again, there exist ...
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1 vote

How are line pairs and pixels related in image resolution?

This answer is to be read along with Fat32's answer. The whole electrooptical imaging thing is complicated, and there's always more details to stumble over. In this case, I think the following three ...
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1 vote

Counting vechicles in an image

This is a relatively easy method using modern Computer Vision, namely, Deep Learning. The easiest way would be using transfer learning one a model which was trained on face detection. The reason to ...
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