thanks for your posts
because pattern recognition is not so easy as you might think. Seriously.
i dont think that this is easy. but i want to deal with it. in my study we have 2 courses which go in this direction, one of them this semester. so now i´m collecting information about it to have a base for my exercises
I could provide you my bachelor thesis, if you like.
yes, this would be great
For preparing the image, make it grayscale and try bit-extraction on the first two high-level bits. Then use a Sobel and/or Laplace filter to extract candidates for contours and use a 4-way or 8-way contour finding algorithm to skeletize the image.
Then, geometric shapes can be easily detected with the Hough-Transformation, that is faster than creating a filter kernel with a circle and convoluting the image. In the fourier domain, convolution is faster, but this is much more complicated.
I processed some images of cells and/or bacteria, a histogramm equalization is almost all the time very beneficial, because it brings some details, which are hidden in low-contrast areas, up front.
i´ll do so. until now i´ve played around with some filters and adjustments (using ImageJ) to get a black/withe-image of the bacterias
