Basically, Image Segmentation is an image processing technique. Significantly, it is an important part of object detection. In the case of object detection, it is required to partition an image. So, all the objects that the given image contains can be separated. Once we separate all parts of the image it becomes easier to analyze it.
Applications of Image Segmentation
The following list provides some of the applications where we can use image segmentation.
- In the case of object counting in an image, we need to first separate different parts of the image. After that, we can remove the background and then count the objects.
- Similarly, we can identify different types of objects.
- In the case of weed detection, we need to segment the image to remove the soil part and the crop part. Therefore, we can use image segmentation as a pre-processing step.
- Another significant application of image segmentation is medical diagnostics where it is used to analyze x-ray images.
- Further, we can use it in surveillance and aerial monitoring.
- Also, it has applications in robotics. For instance, a cleaning robot can identify objects in a scene. Similarly, a fruit and vegetable plucking robot needs to identify the desired object from the image.
- In the case of remote sensing, image segmentation can be used to detect environmental changes.
Image Segmentation using Python
The following list describes the python packages that we can use.
- Firstly, we use the OpenCV package to read and write an image.
- After that, we can use the pixellib package. Specifically, this package is available in python for image and video segmentation. In order to install this package, first, we need to install its dependencies. Therefore, we install tensorflow, and imgaug packages as the dependencies of pixellib.
Further, the pixellib package allows us to perform the following types of segmentation for an omage.
While the Semantic Segmentation segments the objects with the same pixel values with the same colormaps. In contrast, the Instance Segmentation detects the instances of the same objects. In order to do that, this method applies different color maps on the instances of different objects.