Month: July 2019

Categories
Polygon

Understanding Semantic Segmentation

Understanding Semantic Segmentation

Development of most of the Computer Vision projects involves inferring knowledge from an image. Semantic Segmentation plays a key role in image analysis and helps a machine to recognize objects in an image. It is one of the high-level tasks that help to understand the complete scene in images.

What is Semantic Segmentation?

Semantic segmentation is the task of associating every pixel of an image with a corresponding class label. It is a type of pixel-level image classification which not just marks an image with labels and bounding box parameters but also gives a high-resolution image as the output.

Applications of Semantic Segmentation

Semantic segmentation has been successfully applied to tasks that involve segmentation of images. Below are a few key applications of Semantic Segmentation:

Autonomous Vehicles

Semantic Segmentation helps in equipping self-driving cars with the needed perception to understand the constantly changing environments and plan & execute the tasks with utmost precision.

It helps the autonomous vehicles to know exactly where other vehicles are and the free spaces on roads in addition to traffic signals and lane markings to safely navigate through the roads.

Medical Imaging

In the field of medical imaging, Semantic Segmentation helps to identify pixels of lesions or organs from CT or MRI images which in turn helps to understand the shapes and volumes of the organs. This aids the radiologists to perform better analysis and run diagnostic tests without delay.

Geo-sensing

Multi-class semantic segmentation helps to monitor land areas for deforestation and urbanization. It helps to recognize the type of land cover at the pixel level in satellite images. It also aids in city planning, road monitoring, and traffic management through detection of roads and buildings.

Precision Agriculture

Semantic Segmentation can help farming robots by assisting them in triggering weeding actions in real-time. It helps the robots to identify the right time for spraying out herbicides in the fields thereby reducing the need for manual monitoring.

Semantic Segmentation thus plays a crucial role in understanding images through the classification of each pixel in images. Pixel-level classification helps to achieve detection and delineation of objects thus enabling computers to perform accurate image analysis.

Categories
Annotation

Image Annotation Types

Image Annotation Types

Image Annotation helps to build accurate object detection models mainly used for Computer Vision projects. In this blog, we will take a look at what is Image Annotation and its types.

What is Image Annotation?

Simply put, Image Annotation is the labeling of an image either in the form of one label for the entire image or numerous labels for every object in an image. It is a marking tool that highlights an object or content in an image sketching around it.

Types of Image Annotation

Let’s take a look at some of the common Image Annotation types used for Computer Vision projects;

Bounding Box

Bounding Box annotation involves drawing a box around objects within an image. The image is marked as per the custom requirements of data-scientists and mostly involves drawing a box as close to the edges of the objects as possible.

Being one of the most commonly used image annotation techniques, it plays a major role in training autonomous vehicle models by tagging entities like vehicles, cyclists, pedestrians, and other obstacles within a traffic image.

Cuboid Annotation

Similar to bounding boxes, cuboid annotation involves sketching a box around objects in an image. Bounding boxes helps to depict the length & width of an object whereas cuboid annotation highlights the objects in 3D, depicting the length, width, and depth of the objects.

Cuboid Annotation is mainly used in the field of construction and building structure where it is used to get the accurate dimension of objects. It is also used for annotating medical images mainly in the field of radiation imaging.

Line Annotation

Line Annotation is used for training Machine Learning models to detect boundaries and lanes by drawing lines on roads or streets. The most common application of Line Annotation is in annotating sidewalks and road lanes for training the autonomous vehicles to stay in one lane without veering and to understand the boundaries.

Landmark Annotation

Also known as Dot Annotation, it helps to identify the dissimilarities between objects and count the miniature objects in an image. It is used for detecting distant objects in satellite images, identifying the different poses of athletes and predict the motion of the pedestrians for training self-driving cars.

Semantic Segmentation

Semantic Segmentation is also called as pixel-level labeling and is more precise and specific. The other types of image annotation mainly dealt with outlining the outer edges of an object whereas Semantic Segmentation involves labeling every pixel in a given image.

It helps to divide an image into multiple segments thus helping to depict an image in a more meaningful way. This type of image annotation is used for training autonomous driving models, classifying visible terrain in satellite images, analyzing medical images and in industrial inspection.

Polygonal Segmentation

Being one of the smartest and fastest ways to annotate objects for Machine Learning, Polygonal Segmentation helps to detect the boundaries of an object with optimum precision. It helps in the accurate estimation of the shape and size of the objects mainly captured from distant cameras.

Polygonal Segmentation is used for more detailed recognition of objects such as detecting street signs, facial features and logos. These are some of the common image annotation types used for training machine learning models. If you are planning to outsource your image annotation workload, contact Data Labeler, the one-stop-shop for all your data labeling needs.