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Introduction to Image Segmentation

Introduction to Image Segmentation

Introduction to Image Segmentation

Computer vision is a subfield of artificial intelligence, that gives us the ability to process digital images and videos to extract meaningful information using machine learning and deep learning. Image Classification, object detection, object tracking, and image segmentation are some of the processes we can perform on digital images using computer vision. We will discuss image segmentation in this article. In computer vision, image segmentation is the best  choice when we are looking to differentiate between objects with the highest degree of accuracy.

What is Image Segmentation?

In addition to detecting the objects present in an image, Segmentation aims to determine where and how the detected objects are placed in the picture.

To tell the model what we are looking for, we annotate the image by drawing a polygon around every object in the image at a pixel level and labeling it. The resulting model then easily detects and labels objects in images.  In the example image below, the segmentation model  detects objects such as the wall, clock, laptop, people, and sofa in the picture.

Figure 1. (A) segmented image (B) original image

Types of Image Segmentation

Figure 2. (A) original image (B) semantic segmentation (C) instance segmentation (D) panoptic segmentation

1. Semantic segmentation

In Semantic Segmentation objects are only classified into predetermined classes, that’s to say if we have several of the same objects in the picture, all of them will be labeled as a class and there will be  no clear distinction between them.

2. Instance segmentation

In this type of segmentation we differentiate between similar objects. Instance segmentation, recognizes each object in the image separately, and generates a mask for each object. This means that the model first focuses on the object type and then individually identifies each object’s instance.

3. Panoptic segmentation

Panoptic segmentation is a combination of Semantic and Instance segmentation, where each instance of an object is individually detected and labeled.

Applications of Image Segmentation

Image segmentation has important applications in various industries like Medical Imaging, Self-Driving Cars, Satellite Imaging, industrial inspection, etc.

1. Medical Imaging

Image segmentation can be used for tasks such as cancer cell segmentation or locating tumors and other pathologies. Image segmentation helps  speed up diagnoses.

Figure 3. (A) Bone Tumor segmentation [3] (B) Coronary artery segmentation [4] (C) Pathology segmentation [5]

2. Self-Driving Cars

Self-driving cars have to identify roads, pedestrians, traffic signs, etc. to be able to execute appropriate commands and safely navigate to their destination. Image segmentation is therefore a key part of the software behind self-driving cars.

Figure4. Using Segmentation to give a Self-Driving car the ability to see [6]

3. Satellite Imaging

Image segmentation can precisely identify forests, mines, bodies of water, etc. in satellite images and track their changes over time.

Figure 5. Segmentation of satellite Earth imagery [7]

4. industrial inspection

Inspection and maintenance of industrial equipment is a very difficult and time-consuming task. Implementing Image Segmentation in the process can make failure detection significantly faster and safer.

Figure 6. Railroad track components inspection using segmentation [8]
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