THE WATERSHED SEGMENTATION 1 NADINE GARAISY

GENERAL DEFINITION 2 A drainage basin or watershed is an extent or an area of land where surface water from rain melting snow or ice converges to a single point at a lower elevation, usually the exit of the basin, where the waters join another waterbody, such as a river, lake, wetland, sea, or ocean

INTRODUCTION 3  The watershed concept was first applied by Beucher and Lantuejoul at 1979, they used it to segment images of bubbles and SEM metallographic pictures  The Watershed transformation is a powerful tool for image segmentation, it uses the region- based approach and searches for pixel and region similarities.

IMAGE REPRESENTATION 4

REMINDER-IMAGE GRADIENT 5 An image gradient is a directional change in the intensity or color in an image. Image gradients may be used to extract information from images.

IMAGE GRADIENT 6 an intensity image a gradient image in the x direction measuring horizontal change in intensity a gradient image in the y direction measuring vertical change in intensity

IMAGE GRADIENT 7

GEODESIC DISTANCE 8

GEODESIC ZONE OF INFLUENCE 9

GEODESIC SKELETON BY ZONES OF INFLUENCE 10

MINIMA AND MAXIMA 11

MINIMA AND MAXIMA 12

13 ASCENDING PATH

14 NON-ASCENDING PATH

THE WATERSHED TRANSFORMATION 15

16 THE WATERSHED TRANSFORMATION

17  THE WATERSHED TRANSFORMATION

BUILDING THE WATERSHED 18

BUILDING THE WATERSHED 19

BUILDING THE WATERSHED 20  Visual illustration Visual illustration

OVER-SEGMENTATION PROBLEM 21 Unfortunately, most times the real watershed transform of the gradient present many catchment basins, Each one corresponds to a minimum of the gradient that is produced by small variations, mainly due to noise.

OVER-SEGMENTATION: SOLUTION 22 The over-segmentation could be reduced by appropriate filtering, but the best results is obtained by marking the patterns to be segmented before preforming the watershed transformation of the gradient.

OVER-SEGMENTATION: SOLUTION FIRST: we mark each blob of protein of the original image (by extracting the minima of the image function) 23

OVER-SEGMENTATION: SOLUTION 24

HOMOTOPY MODIFICATION 25

OVER-SEGMENTATION: SOLUTION Now we look at the final result of the marking as a topographic surface, but in the flooding process instead of piercing the minima, we only make holes through the components of the marker set that we produced 26

OVER-SEGMENTATION: SOLUTION 27

OVER-SEGMENTATION: SOLUTION 28

OVERLAPING GRAINS OVERLAPING GRAINS 29  In some cases we have an image with overlapping figures, that we need to segment, in order to do that we need to point out the overlapping regions.  For example the figure here is a TEM (transmission electron microscopy) image of grains of silver nitrate scattered on a photographic plate.

30 OVERLAPING GRAINS OVERLAPING GRAINS To point out the overlapping regions we first threshold the initial image to a binary image with only two gray values

REMINDER: DISTANCE FUNCTION 31

32 OVERLAPING GRAINS OVERLAPING GRAINS By calculation the maxima of the distance function of the binary image we can provide the markers of the grains

33 OVERLAPING GRAINS OVERLAPING GRAINS

34 OVERLAPING GRAINS OVERLAPING GRAINS Finally after marking the background and calculation the gradient function we run the homotopy modification and the watershed construction are preformed

THE SEGMENTATION PARADIGM 35 I.Finding the markers and the segmentation. II.Performing a marker- controlled watershed with these two elements The segmentation process is divided into two steps:

WATERSHED TRANSFOTMATION PROCESS 36 Source: A gray scale image Step 1: Use the Gradient Magnitude as the Segmentation Function - The gradient is high at the borders of the objects and low (mostly) inside the objects. Step 2: Mark the foreground objects FROM -

37 Step 3: computing the opening-by- reconstruction of the image : Step 4: Following the opening with a closing can remove the dark spots and stem marks. Step 5: Calculate the regional maxima to obtain good foreground markers. WATERSHED TRANSFOTMATION PROCESS FROM -

38 Step 6: Superimpose the foreground marker image on the original image, Notice that the foreground markers in some objects go right up to the objects' edge cleaning the Step 7: cleaning the edges of the marker blobs and then shrinking them a bit Step 8: Compute Background Markers, Starting with thresholding operation WATERSHED TRANSFOTMATION PROCESS FROM -

39 Step 9: Compute Background Markers, using the watershed transform of the distance transform and then looking for the watershed ridge lines of the result Step 10: Visualize the Result, one of the techniques is to superimpose the foreground markers, background markers, and segmented object boundaries. WATERSHED TRANSFOTMATION PROCESS FROM -

WATERSHED TRANSFOTMATION PROCESS – ADVANCE OPTIONS 40 We can use transparency to superimpose this pseudo- color label matrix on top of the original intensity image. *Another useful visualization technique is to display the label matrix as a color image FROM -

ROAD SEGMENTATION 41  In this study they use the watershed algorithm among others to extract vehicle position on the road and possible obstacles ahead.  The algorithms have been tested on a small database representing different driving situations.

ROAD SEGMENTATION 42 The morphological gradient image The original road image

ROAD SEGMENTATION 43 Due to noise and inhomogeneities in the gradient image, the watershed will produce a lot of minima which leads to over-segmentation of the image

ROAD SEGMENTATION 44 We can enhance the watershed on the gradient image by modifying the gradient function by defining new markers which will be imposed as the new minima.

ROAD SEGMENTATION 45 The difference between watershed on simple gradient and watershed on the gradient after modifying using the regularized gradient

ROAD SEGMENTATION 46

ROAD SEGMENTATION 47

ROAD SEGMENTATION 48

ROAD SEGMENTATION 49  To obtain the road markers we do a simplification on the image using its gradient, the result is an image made of catchment basins tiles of constant gray values- this image is called the mosaic-image.  The gradient of this image will be null everywhere except on the divide lines where it will be equal to the absolute difference of the gray-tone values of the to catchment basins.

50 ROAD SEGMENTATION Watershed of the mosaic-image points out only the regions surrounded by higher contrast edges, and we can still extract a marker for the road

ROAD SEGMENTATION 51 The result – Road borders, corresponding to the watershed of the modified gradient image

LANE BY LANE ROAD SEGMENTATION 52 Original image Mosaic- image Watershed of mosaic- image Lanes markers enhancement Final result

POTENTIAL OBSTACLES DETECTION 53 The second part of this study was identifying obstacles on the road, but this detection is useless without a good definition of the nature of the obstacles, the problem in this part was distinguishing a dangerous obstacle from a light variation in intensity due, for instance to a shading. Black marker- the edges of the road White marker- obstacles-free zone

POTENTIAL OBSTACLES DETECTION 54 Difficulties in this segmentation:  false detection due to the shadows, because they are considered as obstacles, this can be solved if given complementary information by telemetry or stereovision

VISUAL EXAMPLES  Illustration of watershed road segmentation:  Road Detection Using Region Growing and Segmentation: 55

REFERENCES  THE WATERSHED TRANSFORMATION APPLIED TO IMAGE SEGMENTATION – S.Beucher  ROAD SEGMENTATION BY WATERSHEDS ALGORITHEMS – S.Beucher, M.Billodeau and X.Yu  USE OF WATERSHEDS IN CONTOUR DETECTION- S.Beucher and C.Lantuejoul  MATHWORKS.COM  WIKIPEDIA 56

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TOPOGRAPHIC MAP 58