Image segmentation is an important basic operation of image processing. It has a direct effect on subsequent operations such as image analysis and scene understanding. The watershed algorithm is widely used to segment images because of its ability to obtain accurate boundaries. However

this algorithm tends to cause over-segmentation phenomena. Alternatively

segmentation algorithms specific to RGB images cannot radically solve the case wherein regions have similar colors and textures. To solve the over-segmentation problem in the watershed segmentation algorithm and overcome the limitation caused by using only RGB images

a marked watershed segmentation algorithm based on RGBD images is proposed. An RGBD image is composed of an RGB image and a depth image. By using depth information

we extract the geometry information of the object to assist the image segmentation process. For RGB images

the probability map of boundary is computed by the PB algorithm and used as the color gradient image in our algorithm. For depth images

a depth gradient operator and a normal gradient operator are defined to measure the differences in geometry information. We use the circular gradient operator with eight orientations. For each orientation

we divide the disk into two half disks along the diameter of the orientation and compute the gradient by using information from the two half disks. For the depth gradient

we obtain the weighted depths of Gaussian weight from the two half disks and consider the depth difference between the two weighted depths as the gradient. For normal gradient

we fit this normals to the points set in the two half disks and compute the angle between the two normal as its gradient. We select the maximum gradient among the different orientations as the final gradient. By fusing the depth gradient image

normal gradient image

and color gradient image

the marker image is extracted. Thereafter

the color gradient image is modified by the marker image using the minimum calibration technique

and the segmentation process is conducted by using the watershed algorithm. Our experiments are based on the NYU2 dataset provided by New York University. This dataset includes different indoor scenes and provides the labels of real segmentations. Results show that by extracting the markers and modifying the color gradient image

our algorithm can reduce the number of regions and solve the over-segmentation problem effectively. Furthermore

by using the geometry information of the object

our algorithm can obtain closer results to real labels. The accuracy of the segmentation is improved

particularly for regions with similar colors and textures. Specific to RGBD images

we propose a new watershed segmentation algorithm. Except the color gradient image obtained from the general RGB image

we also extract the depth gradient and normal gradient to assist in the image segmentation process. Furthermore

a marked watershed algorithm is used to obtain the final segmentation results. Results show that our algorithm reduces the number of regions and obtains improved segmentation accuracy

particularly in regions with similar colors and textures.