pouring — 通过在图像上“浇水”来分割图像。
pouring regards the input image as a “mountain range.”
Larger gray values correspond to mountain peaks, while smaller gray
values correspond to valley bottoms. pouring segments
the input image in several steps. First, the local maxima are
extracted, i.e., pixels which either alone or in the form of an
extended plateau have larger gray values than their immediate
neighbors (in 4-neighborhood). In the next step, the maxima thus
found are the starting points for an expansion until “valley
bottoms” are reached. The expansion is done as long as there are
chains of pixels in which the gray value becomes smaller (like water
running downhill from the maxima in all directions). Again, the
4-neighborhood is used, but with a weaker condition (smaller or
equal). This means that points at valley bottoms may belong to
more than one maximum. These areas are at first not assigned to a
region, but rather are split among all competing segments in the
last step. The split is done by a uniform expansion of all involved
segments, until all ambiguous pixels were assigned. The parameter
Mode determines which steps are executed. The following
values are possible:
This is the normal mode of operation. All steps of the segmentation are performed. The regions are assigned to maxima, and overlapping regions are split.
The segmentation only extracts the local maxima of the input image. No corresponding regions are extracted.
The segmentation extracts the local maxima of the input image and the corresponding regions, which are uniquely determined. Areas that were assigned to more than one maximum are not split.
In order to prevent the algorithm from splitting a uniform
background that is different from the rest of the image, the
parameters MinGray and MaxGray determine gray
value thresholds for regions in the image that should be regarded as
background. All parts of the image having a gray value smaller
than MinGray or larger than MaxGray are
disregarded for the extraction of the maxima as well as for the
assignment of regions. For a complete segmentation of the image,
MinGray = 0 and MaxGray = 255 should be
selected. MinGray < MaxGray
must be observed.
Image (输入对象) singlechannelimage → object (byte)
输入图像。
Regions (输出对象) region-array → object
Segmented regions.
Mode (输入控制) string → (string)
Mode of operation.
默认值： 'all'
值列表： 'all', 'maxima', 'regions'
MinGray (输入控制) integer → (integer)
All gray values smaller than this threshold are disregarded.
默认值： 0
建议值： 0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110
值范围：
0
≤
MinGray
≤
254 (lin)
最小增量： 1
建议增量： 10
MaxGray (输入控制) integer → (integer)
All gray values larger than this threshold are disregarded.
默认值： 255
建议值： 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 255
值范围：
1
≤
MaxGray
≤
255 (lin)
最小增量： 1
建议增量： 10
限制：
MaxGray > MinGray
* Segment a filtered image read_image(Image,'particle') mean_image(Image,Mean,11,11) pouring(Mean,Seg,'all',0,255) dev_display(Mean) dev_set_colored(12) dev_display(Seg) * Segment an image while masking the dark background read_image(Image,'particle') mean_image(Image,ImageMean,15,15) pouring(Mean,Seg,'all',90,255) dev_display(Mean) dev_set_colored(12) dev_display(Seg)
Let be the number of pixels in the input image and be the number of found segments, where the enclosing rectangle of the segment contains pixels. Furthermore, let be the number of chords in segment . Then the runtime complexity is
pouring usually returns the value 2 (
H_MSG_TRUE)
。如有必要，则抛出异常。
binomial_filter,
gauss_filter,
smooth_image,
mean_image
watersheds,
local_max,
watersheds_threshold,
watersheds_marker
histo_2dim,
expand_region,
expand_gray,
expand_gray_ref
基础