Working on a new method to detect linear objects in images, I have developed a transformation that turns the images into a spooky tangled network. My theory is that the transformed image shows the main paths of spatial coherence.
Is this kind of analysis already done in other field of image processing or with another application?
This is the result with a satellite image of a forest area:
I have experience with image operations to identify features in images, like edges or straight lines, but I have never seen anything like this.
The method is based on several wavelet-transforms around each pixel, considering lines in all directions and selecting the angle with maximum convolution value. This operation results in two numbers per pixel: the maximum convolution and its corresponding angle, similar to the magnitude and phase of the Fourier analysis. In the above example, the convolution map is shown with its extreme values scaled to a gray map.
A college suggested to compare the transformation with a Sobel filter:
Sobel operator approximates the spatial derivatives of the image in each pixel. Using the classical 3x3 kernel, a first order approximation of the gradient vector is obtained that is typically used to highlight edges. My filter uses an arbitrary kernel size (32x32 in the above example) but does not approximate the spatial derivatives of any order. The image is convoluted against line kernels that can be interpreted as local Hough transforms. Selecting the kernel with maximum convolution value, the image is locally approximated as a line in the corresponding direction.
This hypothesis answers part of my question but the precise meaning of the paths present in the filtered image is still open. The paths seem to connect certain regions of the image, maybe spatially coherent or maybe just a visual artifact from the superposition of the local Hough transforms.