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Classify images with pixel-wise annotation for computer vision to categorize each pixel in an image into a class or object.
It assists self-driving cars in seeing the world and reacting to things on the go. Through semantic segmentation visuals are divided into categories which include lanes on the road, other vehicles, road intersections, etc. It assists the car to safely navigate to arrive at its destination and also, take key measures like responding to unexpected occurrences which includes a pedestrian suddenly crossing the road or car braking all of a sudden.
Farmers use this technique for detecting crop infestations and automatic spraying of pesticides. It tells the farmer regarding the parts of the field that are infected and prone to risk so that the automated system is able to take action to get rid of the pest. The use of satellite imagery and drone recordings enables monitoring of urbanization and deforestation through image segmentation deep learning which assists in gathering precise information about these fields.
This technique is used quite often for enabling cameras to shift from portrait to landscape mode, adding or removing a filter for creating an affect. Social media apps like Instagram and TikTok utilize this technique for identifying cars, buildings, cattles, and other objects for selected filters or effects to be applied.
Common medical procedures including CT scans, X rays, MRIs are reliant on image analysis. Though this task has previously been in the hands of a medical professional, currently medical image segmentation models are attaining similar results. The analysis of image and surrounding the image with exact boundaries, this method assists in detecting anomalies and possible diagnoses.
Semantic segmentation is based on a deep learning architecture called image segmentation convolutional neural network (CNN) which is built for image segmentation. Given below is a brief synopsis of its working (from preparation to output).
Prior to the starting of the segmentation process, images are pre-processed to ensure they are made suitable for analysis. To ensure the input is consistent and optimal, the images are resized, noise is reduced, and color is normalized.
The next step involves passing the pre-processed images via a neural network for extracting the pertinent features that are necessary for the the particular use case. The images are analyzed and distinct patterns, shapes and textures are identified to differentiate different objects or regions.
This phase starts after the extraction of relevant features. It involves classifying every pixel and assigning a label or class. It enables machine learning models to distinguish between various objects.
Visualization of semantic segmentation results involves overlaying a segmentation mask to the image, highlighting a particular class or object of interest to assist in distinguishing the identified objects from the remaining image.
Semantic segmentation involves three key steps:-
Classification – This involves classifying a particular object in an image
Localization – This involves searching the object and drawing a bounding box around it.
Segmentation - This involves bundling the pixels in a localized image by creation of a segmentation mask.
In essence, semantic segmentation involves classification of a particular class of image and isolating it from the remaining image classes by overlaying it with a segmentation mask.
Semantic segmentation involves organizing data within images and videos into distinct categories. While image classification and object detection might inform us regarding the presence and location of particular objects, segmentation permits one to dive deeper in real time.
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