First, the circular option relates to the treatment of the borders of the image. Then, standard image kernels can be any $[r,c]$ matrix. If either $r$ or $c$ is equal to $1$, then this is a very flat $2D$ filter, that acts only across one direction: across lines if horizontal, across columns if vertical (with the transpose).
Filtering is a linear operation: ...
If I'm not mistaken, a column vector will filter the image across its columns, treating each row independently of the others. Likewise, a row vector will filter across rows, treating all columns the same.
edit: Regarding an example - consider the simple image [1,1,1;0,0,0;-1,-1,-1]. It's constant along its rows (i.e., all the columns are the same) and a ...
in my opinion
your question seems based on a combination of thoughtful reflection and possibly, pre Alzheimer’s cognitive biases. ;)
There was an article in the WSJ not too long ago , I’m outside the paywall so I can’t provide a link, that looked at IBM’s efforts using Watson at major Cancer research centers.
The conclusion was that Watson wasn’t ...
Could you please share the part of your code where you include Laplacian inside bounding boxes? Many thanks.
Regarding to your question, there is a paper reviewing focus methods:
Pertuz, Said, Domenec Puig, and Miguel Angel Garcia. "Analysis of focus measure operators for shape-from-focus." Pattern Recognition 46.5 (2013): 1415-1432.
Maybe in your case is ...
For your application, image segmentation would be more useful than bounding boxes that contain also background. Other useful keywords: instance-aware image segmentation, instance segmentation.
Figure 1. Instance segmentation example image from Mask R-CNN, by Karol Majek. Bounding boxes are also shown.
Examples of implementations using some version of Yolo:
This is a go at the first suggested extension of my previous answer.
Ideal circularly symmetric band-limiting filters
We construct an orthogonal bank of four filters bandlimited to inside a circle of radius $\omega_c$ on the frequency plane. The impulse responses of these filters can be linearly combined to form directional edge detection kernels. An ...
For a metric of the blurredeness, you are using a laplacian which gives you usable limits of the object which are not included in most of the edge of the objects square. If you walk lines of pixels inwards from the edges of the detected zone, omitting the photo edges, when you cross a black zone from your laplacian, it means that you have transitioned inside ...
Let me present the following Diagram:
So, both Deblurring and Deconvolution are operations within the family of Image Restoration (Which is a subset of Inverse Problem set).
Basically we build the Image Restoration set by different Degradation Models.
The one related to the question are:
Linear Degradation Model
Namely, the degradation is made by a Linear ...
Here is what I would try on the source image:
Split your image into 5x5 pixel blocks (maybe 3 maybe 7, who knows?)
Create output image one fifth (third, seventh, ??) size
For each block
For each color channel
Find best fit plane
Measure RMS of (pixel value-plane value)
Set output pixel to RMS(R,G,B)
In blurry/plain areas the ...
In the context of image processing (and machine vision as well), blurring is an operation that reduces the sharpness of an image by some lowpass filtering applied on it.
There are different causes of blurring such as lens blur, motion blur, or just LSI (linear shift invariant) lowpass filtering.
Deblurring refers to any restoration performed on the image ...