The segmentation in image is used for object recognition, occlusion
boundary estimation within motion or stereo systems, image compression, image
editing, or image database look-up. The main motive of image segmentation is to
cluster pixels into salient image regions, i.e., regions corresponding to
individual surfaces, objects, or natural parts of objects.
What is Segmentation?
Image segmentation is the process of dividing an image into
different regions based on the characteristics of pixels to identify objects or
boundaries to simplify an image and more efficiently analyze it. It is
typically used to locate objects and boundaries in images to make it
recognizable to machine learning based AI models that need more precise
detection of objects in various scenarios.
Image segmentation is also used to track objects in a sequence of images
and to classify terrains, like petroleum reserves, in satellite images. Some
medical imaging applications of segmentation include the identification of
injured muscle, the measurement of bone and tissue, and the detection of
suspicious structures to aid radiologists while detection such
alignments.
IMAGE SEGMENTATION APPLICATIONS
Image segmentation helps define the relations between objects, as well as
the context of objects in an image. The applications include number plate
identification, face recognition, and satellite image analysis. Industries like
retail and fashion use image segmentation, for example, in
image-based searches. While self-driving cars use it to understand their
surroundings.
Objects Detection & Face Recognition
The most important applications of image segmentation involves
identifying the objects of a specific class in the digital image. Semantic
objects can be classified into classes like human faces, cars, buildings, or animals
in the single class.
Face detection: While detecting the object-class with
many applications, including biometrics and autofocus features in the digital
cameras. The machine leaning algorithms detect and verify the presence of the
facial features in different types of human faces.
Medical Imaging: For precise image detection, segmentation
plays a crucial role in medical imaging data. For an instance, radiologist may use
machine learning to augment analysis, by segmenting an image different organs,
tissue types, or disease symptoms reducing the diagnostic time.
Objects Tracking Moving Objects in Videos
Another applicationof image segmentation is
locating the moving object in video footage. Mainly used in security and
surveillance, traffic control, human-computer interaction, and video editing.
Autonomous Vehicles: Self-driving cars need to perceive and
understand their environment in order to drive safely. The relevant classes of
objects visible outside like other vehicles, buildings, and pedestrians. Semantic
segmentation enables self-driving cars to recognize which areas in an image are
safe to drive.
Face & Iris Recognition: To identify an individual in a frame
from a video source segmentation is used. This iris technology compares
selected facial features from an input image with faces in a database helps to
recognize the right person through their face and retina scan in the human
eyes.
Image Recognition in Retail
Apart from autonomous vehicles and medical imaging, image segmentation is
used in retail industry with an understanding of the layout of goods on the
shelf. It helps algorithms process product data on the real time basis to
detect whether goods are present or absent on the shelf. If a product is
absent, they can identify the cause, inform the merchandiser, and also alert
the corresponding part of the supply chain.
Cogito offers world-class image annotation services to provide the best
quality training data sets for machine learning or deep learning based AI
projects. It is also offering semantic segmentation image annotation to annotate the varied objects visible in the different scenarios. Working
with highly experienced annotators, it can produce large volume datasets with
flexibility and turnaround time.
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