Computer vision enables computers to derive meanings from images and videos and enables companies to conduct complex tasks such as image classification, restoration, and object detection. Semantic segmentation is a method used in computer vision applications that helps computers to understand what objects are present in an image.
What is semantic segmentation?
Semantic segmentation is an image segmentation method that assigns every single pixel in an image belonging to an object. It is only dealing with pixels, classifies each pixel in an image with a class label such as dog, person, and cat.
What is the difference between semantic segmentation and instance segmentation?
Both are types of segmentation techniques. However:
- Semantic segmentation treats multiple objects that belong to the same class as a single entity. So, for instance, it aims to label all dogs in an image as “Dog”.
- Instance segmentation differentiates multiple instances of the same class. It assigns different labels such as “Dog 1”, “Dog 2”, etc. to each dog.
What are the applications of semantic segmentation?
Autonomous vehicles: Semantic segmentation can be beneficial for autonomous vehicles such as self-driving cars. It helps self-driving cars to understand the environment and identify the location of objects in visual data for safe driving. In doing so, it helps autonomous vehicles to decide which object is more important in the image.
Medical imaging: Semantic segmentation helps doctors to extract relevant information from X-ray scans and other medical images.
How does semantic segmentation work?
Semantic segmentation architecture mainly consists of an encoder and decoder network.
- Encoder takes image data as an input. It prepares image data for the usage of the decoder. It processes image data to extract statistical properties of the image such as the number of pixels. These features help to label and locate objects in a further step. It provides better classification accuracy for the decoder step.
- Decoder takes the output of the encoder to predict the location and size of each bounding box.
What are the methods for semantic segmentation?
- Fully convolutional networks: It is an architecture used in semantic segmentation. It uses a “fully convolutional” network to transform image pixels into pixel classes. FCN consists of convolution layers where the first layer takes out features from the input image.
- Skip connections: It is also known as “shortcut connections”. It mainly focuses on solving the degradation problem between the layers in the network. A layer in an image means the place of the objects at different levels, and the network represents relationships in a set of image data. Skip connections add the output of one layer in the network as an input for the next layers.
- U-Net: The shape of the architecture is in the form of U. That’s why it is called a U-Net. It is mainly used for biomedical image segmentation. It consists of two paths. The first path is called the contraction path (also known as the encoder). It captures the context in the image (relationship of the nearby pixels) and stores it for the use of a decoder. The second path is the expanding path, it is also known as a decoder. The main idea of U-Net is to achieve high-resolution images on the low-resolution image inputs.
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