watershed wiki

Skeleton extraction and watershed algorithm also belong to the morphological processing category, which are placed in the morphology sub-module.1. Skeleton ExtractionSkeleton extraction, also called binary image refinement. This algorithm can refine a connected area into a pixel width for feature extraction and target topology representation.The morphology submodule provides two functions for skeleton extraction, namely the skeletonize () function and

The watershed segmentation method is a mathematical morphology Segmentation Method Based on topological theory. Its basic idea is to regard the image as the topological landform of the survey, the gray value of each pixel in the image indicates the altitude of the point. Each local minimum value and its affected area are called a collection basin, while the border of the collection basin forms a watershed.

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This article mainly introduced the Python Digital image processing skeleton extraction and the watershed algorithm, now shares to everybody, also gives everybody to make a reference. Come and see it together. Skeleton extraction and watershed algorithm also belong to the morphological processing category, which are placed in the morphology sub-module. 1. Skeleton Extraction Skeleton extraction, also called

Close all;%%%step 1: Grayscale image, Color * * *RGB = Imread (' pears.png ');I = Rgb2gray (RGB);Figure;subplot (121)Imshow (I)%step 2: Using gradients to achieve image segmentation% using the Sobel operator for edge detection,Text (732,501, ' Image courtesy of Corel ', ' FontSize ', 7, ' HorizontalAlignment ', ' right ')hy = fspecial (' Sobel ');HX = Hy ';Iy = IMFilter (double (I), HY, ' replicate ');% implements the linear spatial filter function, a kind of image enhancement method using filte

Without looking at the watershed, the VC image processing book rarely introduces this algorithm, and cximage also finds related algorithms. Let's look at matlab and opencv. MATLAB encapsulates the watershed algorithm and does not look at the source code. opencv is too high to write, and the level difference is not added to opencv. I found the source code of several algorithms on the Internet. One is written

Python opencv watershed algorithm example, pythonopencv This article introduces the watershed algorithm example of python opencv and shares it with you as follows: Target Use the watershed algorithm to separate tagged Images Use the cv2.watershed () function () Principle: Gray images can be seen as topological p

Recently by the image cutting the whole weeping, in this thank old friend Zhou Yang to me on the watershed algorithm guidance! Originally intended to wait for color image cutting has a satisfactory result and then write this article, but considering that this step is also considered a stage, so intends to do a series of image cutting blog, so first write this article.That's a lot of nagging! First look at the effect:The effect is general, there are a

The watershed algorithm converts the edge of the image into a "mountain range" and transforms the homogeneous region into a "valley". First, the gradient of the grayscale image is computed, and then the continuous "perfusion" of the basin is initiated from the user-specified point (or the algorithm-obtained point) until the regions are connected together. The resulting tag allows the area to be merged together, and the merged region is segmented by ag

I. Concepts Basin hydrological simulation is a simulation of the hydrological process occurring on the basin, and a mathematical model is built and implemented on a computer. Consider a river basin as a system, know its input, and require its output. During calculation, the state of the system, that is, the hydrological process occurring on the watershed, should be simulated and calculated. The input is a rainfall process, excluding snow melting, wate

Recently by the image division of the whole weeping, here to thank my old friend Zhou Yang to me about the watershed algorithm guidance! Originally intended to wait for the color image segmentation has a satisfactory result and then write this article, but considering that this step is also considered a stage, so intends to do a series of image segmentation blog, so first write this article.That's a lot of nagging! First look at the effect:The effect

At the age of 23, when I graduated from college, the college students' career path began to go ups and downs. For example, in the middle of the world, a group of people who couldn't keep up with the pace will be dumped in several watershed ways. Although there may also be people after and after this age, the watershed that makes up the past and the future is clearly divided into seed 1 and seed 2 players.

Watershed algorithm is a mathematical morphology segmentation method based on topological theory, whose basic idea is to think of images as topological landforms on geodetic, the gray value of each pixel in the image is the elevation of the point, each local minimum and its affected area is called the basin, and the boundary of the basin is a watershed.The general watershed algorithm causes excessive segmen

　　SummaryDetermines the catchment area above a set of cells in a raster.　　Usage· The values for each watershed are taken from the source values in the input raster or from the feature pour point data. If the pour point is a raster dataset, the cell value is used. If the pour point is a point feature dataset, the value is obtained from the specified field.· If you pre-use the Snap pour Point tool to position the pour point to a cell with a large accumu

First, watershed algorithmWatershed transformation is a popular image processing algorithm for quickly segmenting images into homogeneous regions. The rationale behind it is to treat images as a map of the topological structure,The homogeneous area corresponds to a flat basin surrounded by steep edges.Implementation method:The result of watershed partitioning is obtained through the

First look at the effectDescriptionUsing the watershed algorithm to cut the image, set a marker image to achieve a better effect, but also to prevent excessive cutting.1, first of all, the threshold value of the two-value image corrosion, remove the small white area, the image of the foreground area. and the foreground region with 255 white Mark 2, the same to the threshold of the image expansion, and then the threshold and reverse. Get the background