Why Image Semantic Segmentation Using Deep Learning: Applications?

Semantic segmentation is the process of image annotation techniques in which the images are annotated with full pixel-wise annotation to make the objects recognizable for computer vision based machine learning algorithm training.

Semantic segmentation helps to classify, localize, detect and segment multiple types of objects in the image belongs to a single class. Semantic segmentation in deep learning algorithms helps to classify each pixel in the image from a predefined set of classes.

Image Semantic Segmentation Using Deep Learning

In the context of deep learning, semantic segmentation is dividing a visual input into segments to simplify image analysis. Segmentation means objects or parts of objects, and comprise sets of pixels, or “super-pixels”.

Image segmentation sorts pixels into larger components, eliminating the requirement to consider individual pixels as units of observation. And the image analysis process is done into three stages Classification, Object detection and Segmentation.

Semantic segmentation architecture with deep learning is the concept of broadly thought of as an encoder network followed by a decoder network. As a pert of decoding system, it has different approaches to employ the various mechanism listed below.

  • Region-Based Semantic Segmentation
  • Fully Convolutional Network-Based Semantic Segmentation
  • Weakly Supervised Semantic Segmentation

And doing the semantic segmentation with fully convolution network is critical, as implementing the most popular architecture for semantic segmentation – Fully-Convolutional Net (FCN) need a right algorithm and complete knowledge of FCN architecture to implement the same precisely and develop a right model.

Semantic Image Segmentation Applications

Image semantic segmentation using deep learning is applicable in various types of object detection like face detection, medical imaging and machine vision into different types of AI or Ml based model developments like self-driving cars and aerial based geo sensing applications for autonomous monitoring of farm fields.

Semantic Segmentation Deep Learning for Autonomous Vehicles

Self-driving cars autonomous flaying object like drones can benefit from automated segmentation. For example, self-driving cars can detect drivable regions using the semantic segmentation for deep learning of objects classification and segmentation.

Segmentation with Deep Learning for Satellite Image Analysis

Aerial imagery captured from drones or satellites are analysis with semantic segmentation to monitor the field’s land through automated land mapping for better farm production and crop yield at less cost and human efforts at the same time analyzing the historical data to train the model for improving its prediction accuracy.

The semantic segmentation are used by AI development companies to build the accurate model for right output. Cogito is one of the companies providing the semantic image segmentation service to annotate the different types of objects in the images making the computer vision based machine learning process feasible.

It is also providing the image annotation service for different industries like healthcare, retail, automotive and robotics with best level of quality. It is using all types of image annotation techniques including semantic segmentation for different types of models. Cogito is also specialized in text annotation and video annotation with semantic segmentation video annotation system to create the training data for AI model.

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