Best merit sewing machine in velachery - VS Enterprises
An image processing pipeline was designed for the thread
detection. The complete workflow is visualized in Figure 3.
At all times, knowledge about the desired thread pattern model
is available.
A. Two Partial Luminance Images
The composed camera RGB-image is converted to a singlechannel luminance image
L(x, y) = 0.3∗R(x, y)+ 0.59∗G(x, y)+ 0.11∗B(x, y). (1)
The conversion is based on the assumption, that the thread
appears either brighter or darker than the tissue background.
However, it is unknown in advance which appearance is given.
Therefore, two partial images are generated. The positive
image I+(x, y) only contains pixels brighter than the tissue
mean, whereas the negative image I−(x, y) only contains
pixels darker than the mean,
I+(x, y) = max(0, L(x, y) − m) (2)
I−(x, y) = max(0, m − L(x, y)) (3)
m = mean(L(x, y)). (4)
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By separation into partial images, the pixels representing
the thread will only be visible in one of them. They will
always appear as a bright structure. Next to the actual thread
pixels, there will also appear spurious pixels from noisy tissue
structures. They resemble thread-like structure parts and are
stochastically distributed.
B. Frangi Filtering
A Frangi filter [3] is applied on both partial images. The filter operation basically consists of a pixel-wise computation of
the Hessian-Matrix and a Gaussian-shaped smoothing kernel.
It is also known as a vesselness filter and was originally introduced in the context of medical image analysis to emphasize
pixels that are embedded in vessel-like structures. However,
an elongated and thin appearance is not only characteristic
for vessels inside the human body but also for the considered
thread within this work. It is therefore natural to adopt the
established methodology for the given task. The result are two
images, IF r+ and IF r−, with each pixel value containing the
probability that it is embedded in a thread-like structure. Best merit sewing machine in velachery
C. Selection of the Thread Image
Based on the supplied model prior, an approximate number
of expected thread pixels can be estimated. This expectation
can be turned into a thresholding operation, that is performed
on both images. Since one of the filtered images contains
both the thread as well as background noise, while the other
image only contains background noise, a robust detection of
the thread image is straight forward. The result is a single
binary image, wF r(x, y), with a pixel value of 1 indicating a
thread pixel.
D. Classification of Pure Thread Pixels
The previous step provides a mask for pixels that are
embedded in a thread-like, i.e. elongated and thin, structure.
Yet, pixels lying not directly on the thread but nearby
may be included. Therefore, the mask is refined using
an expectation maximization (EM) algorithm [1]. The
refinement is no longer performed on the luminance image,
but on the RGB-image. As initialization, the tissue RGB
values from the background removal step are taken for
the tissue mean and covariance values. The thread mean
and covariance values are derived from all pixels masked
by wF r. The iterative EM algorithm results in a single
binary image, wges(x, y), with a pixel value of 1 denoting a pure thread pixel.
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