Page 198 - Ai_C10_Flipbook
P. 198
Face Filters
Face filters in Computer Vision are fun and interactive tools that uses algorithm
to detect and track facial features, such as the eyes, nose, mouth, and overall
face shape. This process is called facial landmark detection. These filters then
overlay digital effects or objects on a person's face in real time, such as hats,
glasses, or funny faces. They are commonly used in social media apps like
Instagram and Snapchat, video conferencing platforms, and Augmented Reality
(AR) applications.
Google's Search by Image
While most people use Google to search for information
by typing text, there’s an interesting feature called
Search by Image that allows you to search using an
image instead of words. This feature relies on Computer
Vision, a technology that enables computers to interpret
and analyse images.
When you upload an image to Google Search, the system
uses Computer Vision Algorithms to analyse the image. It
extracts key features such as shapes, colours, textures, and
patterns. This process is similar to how the human brain
identifies objects by recognising visual characteristics.
Next, Google compares these extracted features to its massive database of images collected from across the
Internet. Using advanced algorithms, such as image similarity search and pattern recognition, it identifies
matching or similar images.
Once the analysis is complete, Google displays search results that are related to the uploaded image. These results
may include:
• Information about the object or scene in the image
• Similar images
• Websites containing the image or related content
Computer Vision in Retail Person
Person
In the world of shopping, both online and in physical stores,
Person Person
Computer Vision is being used more and more to improve Person
Person
the shopping experience and make businesses more efficient.
Retailers are using this technology to understand customer
behaviour, optimise store layouts, and provide better services.
It helps the retailers to have:
• Improved store layout: By analysing where customers spend the most time, retailers can modify store layouts
to boost sales and enhance the shopping experience.
• Better inventory management: Retailers can track product engagement to determine which items attract
more attention and which do not, optimising stock levels. Additionally, Computer Vision can assess shelf space
usage to identify inefficient configurations and recommend improved product placements.
• Enhanced customer experience: Computer Vision facilitates personalised recommendations and streamlines
the shopping process, making the overall experience smoother and more enjoyable for customers.
196 Artificial Intelligence Play (Ver 1.0)-X

