There are numerous applications of image segmentation in real case scenarios. As you can see, the image segmentation creates a pixel-wise mask for each object of interest in the image that provides us more information about that object. However, to obtain any information about the shape of that object we need a different approach. By using the object detection, we are detecting a set of bounding boxes that correspones to each object in the image.
From this example, it is easy to understand why image segmentation is much more accurate than simple object detection. Each region is painted in different colors. On the left side, we can see the image of the road and on the right, we can see segmented image into several areas – road area, sidewalk area, pedestrian area, tree area, building area, and sky area. Let’s better understand image segmentation using the following example. You probably wonder what is the use of partitioning an image into several parts. Moreover, the accuracy of the segmentation step determines success and failure in further image analysis. It is one of the most important image processing tools because it helps us to extract the objects from the image for further analysis. Each of the pixels in a region is similar with respect to some property, such as color, intensity, location, or texture. Image segmentation is the process of partitioning a digital image into multiple segments by grouping together pixel regions with some predefined characteristics. Marker-based Watershed segmentation with the K means algorithm.Image segmentation with the Watershed algorithm in Python.Image segmentation with a Watershed algorithm.
Different techniques for image segmentation.Initially, we will give an overview of the segmentation and start with one of the most common region-based segmentation methods – the Watershed algorithm. This is an important step in many computer vision applications because it isolates the desired region from the image for further processing tasks.
#SHAREX IMAGE SEGMENTS HOW TO#
We will discuss how to segment an image into different regions. Highlights: In this post, we are going to cover one of the most important techniques in image processing.
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