Image Segmentation: The marvel easing the new normal

The year 2020 witnessed an array of accelerated lifestyle changes. Those changes brought with them a lot of technological advancements in disguise. One such being the rise in the use of video conferencing applications and software. As indicated in the AppAnnie report.

“Microsoft Teams and Google Hangouts Meet both jumped in the rankings, with Zoom Cloud Meetings the undisputed winner; it saw large numbers of downloads in the US, UK and across Europe. According to App Annie, during the record-breaking week Zoom was downloaded 14 times more than its 2019 Q4 weekly average in the U.S.; 20 times more in the UK; 22 times more in France; and a staggering 55 times more in Italy. (The higher numbers are in areas that began lockdowns earlier.)

During the same period, Hangouts Meet saw 30 times the weekly level of downloads compared to the last quarter of 2019 in the U.S., while Teams saw an 11-fold increase..”

With the increased number of users and rise in the competition. The software providers mainly, Microsoft, Google, Cisco and the newest Zoom tried various improvements and add on features to their application to lure the customers.

The virtual background

The most prominent and widely used feature being “The virtual background”. The rapid growth of video conferencing led to increased expectations for this technology to meet high-quality standards. This is why, during a video conference, you must appear flawless. But what if you get an unexpected call and the background of the room is a little cluttered? This is an excellent example of how a virtual background for video conferencing can be a useful tool in assisting you to present yourself in the most professional manner.

If you are still not familiar with the technology. Head over to this video to quickly get an overview.

But, hey what is the technology that drives these flawless background changers. Hmm, you guessed it right Deep Learning, Augmented Reality stuff sort of. But diving deeper, these flawless changing backgrounds would still have been a futuristic dream if not for computer vision-enabled image segmentation.

Seems like jargon, Ohh wait…

What is Image Segmentation?

Image segmentation is a computer vision technique used to get a granular lever idea of what’s in an image at the pixel level, unlike image recognition where labels are assigned to the entire image or object detection, which tries to localize images by drawing a bounding box around them.

With a high-level idea of Image segmentation, let’s understand how are virtual background and image segmentation related?

By the virtue of image segmentation, it is possible to get a pixel level idea of the foreground and the background in a visual. Which is being used to mask out the not required details. In our case the background. With the foreground in perspective, it can be optimized to be presented.

The masked out background is then replaced with the images based on user choice or upon preference variable image effects like blurring iare applied on the segmented background.

Seems interesting, isn’t it?

Allow me to take you on a tour of different use cases where the human race is benefitting from a concept as simple as Image Segmentation.

Portrait mode:

A report by The manifest highlights,

“Images remain the most popular type of content: Around three-quarters of Generation Zers (77%), millennials (77%), and Generation Xers (72%), along with 52% of baby boomers, prefer posting images on social media.”

If social media is being discussed, Display picture or the way Genz prefers, DP is an essential part of your social identity. Understanding this a lot of feature improvements have been done in the camera application of smartphones. One of the prominent one’s and also a favourite of mine is the portrait mode.

Unnecessary details (out-of-focus) are blurred out and the subject in focus is highlighted to give what professionals say, the bokeh effect, in no time. The engine driving this instant Likes gaining machine in non-other than image segmentation. Focused element pixels are treated as one entity and masked out. Gaussian blur is applied on the non-masked pixel and Voila!! You get the perfect DP.

Social Media Stories

Just in process of writing this, we found an article, suggesting the change of background without the use of a green screen. And not limiting the content creators to a well-equipped studio or a setup. You can now post stories without the fear of any clutter in the background and connect with your audience.

Fascinating isn’t it? Now you know what computer vision technology is enabling you to remain flawless even in a cluttered environment before your audience.

Visual Image Search

Did you spot a marvellous chair? That aroused a craving in you to buy it. But you lack the words to describe it on the search platform of your favourite eCommerce website.

And it is nowhere to be found, and down goes your eagerness to experience the comfort of owning it

.

Hey, Image segmentation does more than just changing backgrounds and hiding your insecurities regarding the appearance of your surroundings. It can be employed to extract relevant information from an image of a product. Which can then be matched against the product images in the database to deliver a functionality named Visual Image Search.

Which takes in an image and returns the purchasing details. So that you can satisfy your cravings.

Self Driving Cars

Are you fascinated by the idea of driverless cars? Companies like Tesla, Volvo, BMW are investing a lot of money into technologies that help build these self-driving cars. Don’t you want to know what major technologies fuel innovations like this? Yes, Artificial Intelligence is a word you have been hearing about on an on. But on a granular level, self-driving cars rely heavily on computer vision algorithms like Image and Instance segmentation.

Self-driving cars require a pixel-perfect understanding of their surroundings. As a result, image segmentation is used to identify lanes, people on the road, traffic signals, barriers, road curvatures,

people and animals crossing the road, the vehicles in front and around and other important information.

Virtual make up

Do you want to get that fabulous look for the cute date or the classy look for the sales pitch?

Well, Image Segmentation got your back. With companies like Lakme, Loreal, Maybelline introducing virtual try on and virtual makeup platforms. You get to experiment with different looks and makeup in a matter of minutes. Before getting that final splendid look.

With the help of Image Segmentation, facial features are masked like eyes, lips, cheek etc. and a combination of AR and Deep Learning algorithms do the rest of the trick.

Medical Imaging and diagnostics

Semantic segmentation image annotation can be used to annotate various types of medical images such as CT Scan, MRI, and X-rays of various parts or organs of the human body. Semantic segmentation aids in highlighting or annotating the portion of a body organ that is only affected by the disease.

When used in real life as an AI model, semantic segmentation can provide true insight into medical images in order to predict similar diseases. As a result, semantic segmentation may provide the best medical imaging datasets for deep learning or machine learning AI models in healthcare.

Leverage Labellerr

The use cases are marvellous but one bottleneck remains. The high quality labelled dataset to train your deep learning models to adhere to any one of the above-mentioned use-cases.

Are you encountering a similar problem? Training a Computer vision model requires a voluminous amount of high quality labelled data. Which in turn needs a sophisticated enterprise-scale, cloud-based annotation system. Where you can plug your data stream from the cloud/ on-prem based databases and get labelled data with confidence scores for training the computer vision-enabled image segmentation models and accounting for a cent per cent data privacy.

If yes. I got your back. Let me introduce you to a one-stop solution to all your machine learning needs. Labellerr

Labellerr is a data-annotation platform that provides simple, clear and easy to use UI with seamless UX to perform annotation on different types of data catering to a wide array of industries, Retail, Health care, E-commerce, Hospitality, Businesses to name a few.

For your, AI needs Labellerr provides pre-trained models and products that can deliver Plug n-Play APIs with basic functionality to reach clients and can be modified on request easily and swiftly.

Leverage the auto-label feature on Labellerr to annotate your data with 10x speed and save crucial man-hours.

Follow along with this video to get an idea of how to perform segmentation annotation on Labellerr.

Automated ML experience on Labellerr

Experience the truly automated machine learning experience with Labellerr’s complete ML suite. Just plug in your data via a range of connectors as FTP, Local storage, Google Drive, AWS S3, Azure Blob etc.

Allow our inbuilt Auto ML feature to suggest annotations based on your requirement. Leverage the Auto-label feature to annotate your data with 10x speed and save crucial man-hours. Get a list of confidence scores of the assigned labels and verify only those with a low score.

Connect Labellerr to the Cloud-Based Compute service of your choice with assured data privacy and train your machine learning models on the go. Without the hassle of downloading the data, Service-specific data conversion formats, Data Leakage to name a few.

Labellerr’s community service initiative

As a part of our community service initiative, we have created a GitHub repository, wherein we list the implementations and walkthrough guides of tools, technologies, state of the art algorithms catering to the latest developments in the field of Deep Learning and do our bit in building a strong community of deep learning enthusiasts.

Head over to our blog where we regularly write about the industrial and corporate use-cases of Deep Learning. The recent advancements in the field and how the current industry is accepting them, building over them in pursuit of solutions that were once deemed unachievable.

Connect with us

If interested, you can get your hands dirty with our precoded computer vision notebooks as part of our community service initiative.

Have any other use case in mind. Visit our website and mention your use case in brief and our customer engineer will contact you and help you prepare the plan and get you running on a trial with us to validate.