https://opencv.
org/blog/what-is-computer-vision/
What is Computer Vision in 2025? A
Beginners Guide
Introduction
One field that has seen an extraordinary surge
in growth and innovation in recent decades
is Artificial Intelligence.
From humanoid robots like Sophia, capable of
mimicking human interactions, to renowned
models like ChatGPT, known for its ability to
comprehend and generate human-like text,
and even Amazon’s voice-controlled virtual
assistant, Alexa, integrated into Echo devices
and other products – AI is truly transforming
our world.
Table of Contents
Introduction
What is Computer Vision
History of Computer Vision
How does Computer Vision work
Key Features of Computer Vision
Computer Vision Tasks
How are Companies Leveraging Computer
Vision
What is Computer Vision?
Computer Vision, or CV for short, is a
subfield of Artificial Intelligence (AI) that
facilitates computers and machines to
analyze images and videos.
Just like humans, these intelligent systems
can make sense of visual data and extract
valuable information from it.
This capability of Computer Vision finds
applications across a wide array of
industries.
For instance, in healthcare, CV is
instrumental in the field of medical
imaging, aiding doctors and researchers in
diagnosing and understanding complex
medical conditions.
In the automotive industry, Computer
Vision plays a crucial role in enabling
autonomous vehicles to “see” their
surroundings, ensuring safe navigation on
the roads.
In recent years, Computer Vision has
made astonishing progress, which can be
attributed to two key factors:
Advancements in deep learning
and neural networks
Accessibility of vast amounts of
visual data.
These breakthroughs have propelled vision
systems from a mere 50% accuracy level to
an impressive 99% accuracy level in less
than a decade.
This remarkable improvement showcases
the incredible potential of Computer Vision
and its ability to push boundaries
continually.
What’s even more exciting is that the growth
of the Computer Vision market shows no signs
of slowing down. In fact, it’s projected to
reach a staggering $22.27 billion by the end
of 2023.
By 2028, it’s expected to skyrocket to an
astonishing $50.97 billion, growing at a
remarkable rate of 12.56% from 2023 to 2028.
The United States stands at the forefront of
this industry, with an estimated market value
of $8.3 billion.
History of Computer Vision
Highlights
1950s – Recording neural activity
1963 – Attempt at deriving 3D
representations from 2D images
1966 – Multiplayer neural networks
1979 – Necognitron – mimicking human
visual system
Forms of Computer Vision date back to the
50s. The pioneering work of
neurophysiologists David Hubel and Torsten
Wiesel in the 1950s and 1960s involved
presenting arrays of images to cats and
monkeys while recording neural activity. They
revealed fundamental principles of
early visual processing in the brain. Their
findings included the existence of neurons
selectively responsive to specific visual
features, hierarchical processing of
information from simple to complex features,
the concept of receptive fields, and
orientation sensitivity.
These discoveries set the stage for Computer
Vision development by inspiring algorithms
for edge detection, feature extraction, and
hierarchical processing.
Hubel and Wiesel’s research profoundly
impacted our understanding of visual
perception and the field of Computer Vision.
In the very same year, the first image digital
scanner was invented. The digital scanner
invented in 1959 was the VIDICON tube. It
aided in building modern Computer Vision by
converting optical images into electrical
signals, enabling digitizing visual information.
The VIDICON tube allowed for the capture
and processing of images by computers,
paving the way for Computer Vision
applications like object recognition and
pattern analysis.
This technology marked a foundational step in
the development of Computer Vision, which
has since become integral to various
industries and technologies, from facial
recognition to autonomous vehicles and
medical image analysis.
In 1963, Lawrence G. Roberts pioneered
Computer Vision with the “Blockworld”
program, an early attempt to derive 3D
representations from 2D images.
It employed edge detection and hypothesis
testing to reconstruct 3D scenes from simple
block structures, setting the foundation for
key Computer Vision concepts. Roberts’ work
highlighted the importance of edge detection,
3D reconstruction, and hypothesis-driven
approaches, all central to modern Computer
Vision. Today, Computer Vision systems can
recognize and interpret diverse objects and
scenes, with applications in autonomous
vehicles, facial recognition, and medical
imaging, owing much to the foundational
principles set by Roberts in 1963.
In 1966, Marvin Minsky co-authored the book
“Perceptrons” highlighting the limitations of
single-layer neural networks in handling
complex, non-linear data impacting Computer
Vision. This work prompted a shift
towards multilayer neural networks and
renewed interest in the field. It influenced the
development of more advanced neural
network architectures and training
techniques, laying the foundation for modern
deep learning, which is now dominant in
Computer Vision and AI. Minsky’s research
illuminated the importance of overcoming
limitations in early AI models, shaping the
trajectory of Computer Vision research and
the broader field of artificial intelligence.
In 1979, Kunihiko Fukushima unveiled
the Neocognitron, a neural network design
that reshaped the landscape of Computer
Vision.
Neocognitron Architecture
This innovative architecture mimicked the
human visual system’s structure and function,
featuring layers of artificial neurons like S-cells
and C-cells. The Neocognitron excelled at local
feature extraction, detecting intricate patterns
and edges within images. Crucially, it
introduced translation invariance, enabling it
to recognize objects regardless of their
position or orientation—a pivotal concept still
in use today. Fukushima’s Neocognitron paved
the way for advanced neural networks,
notably Convolutional Neural Networks
(CNNs), which dominate modern Computer
Vision, powering applications from image
recognition to object detection.
How does Computer Vision work?
Computer Vision facilitates computers to
perceive and comprehend the visual world
much like humans do. It involves various
stages, beginning with capturing images or
video frames through cameras or sensors.
These raw visual inputs are then subjected to
preprocessing techniques designed to
enhance the overall quality and reliability of
the data. Let us take a quick look at the
different stages.
Feature Extraction
At the heart of Computer Vision lies a crucial
step known as Feature Extraction. During this
phase, the system scrutinizes the incoming
visual data to identify and isolate significant
visual elements, such as edges, shapes,
textures, and patterns. These features are
critical because they serve as the building
blocks for the subsequent stages of analysis.
To facilitate computer processing, these
identified features are translated into
numerical representations, effectively
converting the visual information into a
format that machines can comprehend and
manipulate more efficiently.
Object Detection
Moving forward in the process, object
detection and recognition play pivotal roles.
Once the features are extracted and
converted into numerical data, the system’s
algorithms work to identify and locate specific
objects or entities within the images. This
enables computers to not only detect the
presence of objects but also understand what
those objects are, a capability that finds
applications in fields ranging from
autonomous vehicles identifying pedestrians
to security systems recognizing intruders.
Image Classification
Image classification takes this level of
comprehension to even greater heights.
Traditional Image Classifier
Rather than merely recognizing individual
objects, image classification involves
categorizing entire images into predefined
classes or categories. This is where
Convolutional Neural Networks (CNNs) come
into play. CNNs are a specialized class of deep
learning models designed explicitly for image-
related tasks. They excel at learning complex
hierarchies of features, which allows them to
discern intricate patterns and make highly
accurate image classifications.
Object Tracking
Object tracking is a fundamental technique in
video analysis that plays a pivotal role. It
involves the ability to monitor and trace the
movement of objects as they traverse through
consecutive frames of a video. This might
seem like a straightforward task, but it’s an
essential component in a wide range of
applications, from surveillance and sports
analytics to robotics and beyond.
Semantic Segmentation
If we delve even deeper into the realm of
Computer Vision, we encounter a more
intricate and powerful concept known
as Semantic Segmentation.
This technique takes object analysis to a
whole new level by meticulously labeling each
and every pixel within an image with its
respective category. Imagine looking at a
photo and not only identifying objects but
also understanding the boundaries and
categories of each pixel within those objects.
This level of granularity opens up a world of
advanced possibilities, particularly in the field
of autonomous navigation.
Semantic Segmentation
Autonomous navigation, such as that seen in
self-driving cars and drones, relies heavily on
semantic segmentation. It allows these
vehicles to detect and recognize objects and
have a detailed understanding of their
surroundings. This understanding is vital for
making real-time decisions and navigating
safely through complex environments.
But the capabilities of Computer Vision don’t
stop there. It has the ability to extract three-
dimensional information from two-
dimensional images, enabling the creation of
3D models and reconstructions. This feature
has applications in fields like architecture,
archaeology, and virtual reality, where the
conversion of 2D images into 3D
representations can provide invaluable
insights.
Moreover, Computer Vision can perform post-
processing tasks with remarkable precision. It
can count objects in an image or estimate
their sizes with incredible accuracy. Think
about the potential this holds in inventory
management, quality control in
manufacturing, or even in monitoring wildlife
populations in conservation efforts.
What makes Computer Vision even more
fascinating is its adaptability. Through the
power of machine learning, these systems can
learn and evolve over time. They can become
increasingly accurate and reliable as they
process more data and gain more experience.
This adaptability is what allows computer
vision to continually push the boundaries of
what’s possible in various industries and
applications.
Looking to get started with Computer Vision?
Check out our Free OpenCV Bootcamp.
Key Features of Computer Vision
In this section, we’ll delve into the key
features defining Computer Vision’s
fascinating realm.
Visual Perception
At its core, Computer Vision seeks to replicate
the human ability to perceive and
process visual information. It achieves this by
capturing and comprehending images or
video data from cameras and sensors. These
systems act as the digital eyes that enable
machines to “see” and make sense of their
environment.
Image Understanding
One of the pivotal functions of Computer
Vision is image understanding. Here,
sophisticated algorithms and models come
into play, working to dissect the content of
images or video frames. This process involves
recognizing a wide array of elements, from
objects and scenes to people, and
understanding their attributes and
relationships within the visual context.
Pattern Recognition
Pattern recognition is at the heart of many
Computer Vision tasks. Machines learn to
discern recurring patterns or features in visual
data. This encompasses the identification of
shapes, textures, colors, and various intricate
details that form the building blocks of our
visual world.
Machine Learning and Deep Learning
At the core of Computer Vision lies machine
learning and deep learning techniques. These
cutting-edge technologies, including
convolutional neural networks (CNNs),
facilitate Computer Vision systems to learn
and extract relevant features from visual data
automatically. They are the driving force
behind the remarkable advancements in this
field.
The practical applications of Computer Vision
span across a multitude of industries, making
it a transformative force in today’s world.
From healthcare’s critical medical image
analysis to the automotive sector’s quest for
autonomous driving, Computer Vision plays a
pivotal role. It assists in retail through product
recognition and recommendations, enhances
agriculture by monitoring crops and
predicting yields, strengthens security with
surveillance and facial recognition, and adds a
layer of immersive experiences in
entertainment via augmented and virtual
reality.
Multidisciplinary Character
Computer Vision is an exceptionally
interdisciplinary field. It draws knowledge and
inspiration from various disciplines, including
computer science, machine learning,
mathematics, neuroscience, psychology, and
physics. This amalgamation of insights from
various domains enables the creation of
systems capable of understanding and
interpreting visual data with remarkable
precision.
Computer Vision Tasks
Now let us explore some important Computer
Vision tasks.
Image Classification
At the core of Computer Vision lies image
classification, a fundamental task that
involves categorizing an input image into
predefined classes or categories. Picture a
system that can distinguish between a cat, a
dog, or neither, simply by analyzing an image.
This foundational capability is the bedrock for
various other Computer Vision applications,
paving the way for advanced visual
recognition.
Object Detection
Moving beyond classification, object
detection adds another layer of complexity. It
identifies objects within an image and
precisely pinpoints their location by drawing
bounding boxes around them. Think of
autonomous vehicles identifying pedestrians
and other vehicles, security systems detecting
intruders, or retail applications tracking
products on store shelves. Object detection
empowers machines to navigate and interact
with the world more effectively.
Image Segmentation
Image segmentation is all about dissecting an
image into distinct regions or segments based
on shared characteristics like color, texture, or
shape. This technique aids in understanding
object boundaries and separating different
objects or regions within an image. In the
medical field, it helps segment organs or
tumors, while in robotics, it assists in
navigation and manipulation tasks.
Facial Recognition
Facial recognition is the art of identifying and
verifying individuals based on their facial
features. This technology has far-reaching
applications, from enhancing security through
authentication and access control to adding
fun filters in entertainment and aiding law
enforcement in identifying suspects from
surveillance footage.
Pose Estimation
Pose estimation determines the spatial
position and orientation of objects or body
parts within images or videos. For example,
it’s used in fitness tracking, gesture
recognition, and gaming, allowing machines
to understand the physical world and human
movement in detail.
Sample skeleton output of Pose Estimation
Scene Understanding
Scene understanding goes beyond object
recognition by extracting higher-level
information from visual data. It encompasses
recognizing the layout of a scene,
understanding relationships between objects,
and inferring the context of the environment.
This capability is crucial in robotics,
augmented reality, and smart cities for tasks
like navigation, context-aware information
overlay, and traffic management.
OCR
OCR, or Optical Character Recognition, is the
remarkable ability to recognize and extract
text from images or scanned documents. It
plays a pivotal role in digitizing printed or
handwritten text, making it searchable and
editable. Applications range from document
management to text translation and
accessibility tools for visually impaired
individuals.
Image Generation
While not strictly about recognition,
Computer Vision also facilitates image
generation and manipulation. Generative
models like GANs (Generative Adversarial
Networks) can create realistic images,
opening doors to artistic expression, content
generation, and data augmentation for
training machine learning models.
These are just some of the many Computer
Vision tasks, and numerous variations and
combinations exist to solve complex real-
world problems. Driven by advancements in
deep learning and neural networks, Computer
Vision enables machines to interpret and
interact with the visual world in sophisticated
ways.
How are companies leveraging Computer
Vision
In today’s rapidly evolving technological
landscape, businesses are increasingly turning
to Computer Vision to gain a competitive
edge. However, deploying Computer Vision
solutions often presents a significant
challenge, requiring extensive effort from
computer vision engineers, developers, and
data scientists. Let us look at how some of the
top companies are achieving this by
leveraging Computer Vision.
Intel
Intel Corporation, often referred to simply
as Intel, is a prominent American
multinational technology firm renowned for
its expertise in crafting semiconductor chips,
microprocessors, and various hardware
components for computers and electronic
devices. Established in 1968, Intel has been a
pivotal player in shaping the contemporary
computer industry, celebrated for its
pioneering advancements in CPU (Central
Processing Unit) technology. Intel’s processors
enjoy widespread adoption in personal
computers, servers, and various other
computing devices.
Intel offers a comprehensive suite of tools and
resources designed to assist businesses in
harnessing the power of Computer Vision. Let
us explore a few of them.
End-to-End AI Pipeline Software
One of the key hurdles in deploying Computer
Vision solutions is the complexity involved in
model development and deployment. Intel
recognizes this challenge and has developed
end-to-end AI pipeline software to streamline
the entire process. This software is equipped
with optimizations tailored for popular
frameworks like TensorFlow, PyTorch, and
scikit-learn, ensuring that vision engineers can
work efficiently and optimize performance.
Intel Distribution
For businesses seeking to simplify deployment
further, Intel provides the Intel Distribution of
OpenVINO toolkit. This powerful tool allows
teams to write AI solution code once and
deploy it virtually anywhere. What makes
OpenVINO particularly valuable is its open-
source nature, which enables you to avoid
vendor lock-in. This flexibility allows you to
build applications that seamlessly scale across
various hardware platforms, from edge
devices to the cloud.
Intel Geti
Intel recognizes that AI model development is
not limited to coders alone. To bridge the gap
between domain experts and data scientists,
Intel has introduced Intel Geti, an open-
source, enterprise-class Computer Vision
platform. This innovative platform empowers
non-coders to collaborate effectively with
data scientists, speeding up the process of
building and training AI models.
Intel Geti
Hardware Portfolio for Diverse Needs
Intel understands that different Computer
Vision applications have varying hardware
requirements. To address this, they offer a
broad hardware portfolio that provides the
processing power needed for deploying
Computer Vision in diverse environments.
Whether you require AI models to run on
drones or other edge devices, Intel’s hardware
options have you covered.
Open Source Tools for Scalability
Intel’s commitment to open source extends to
its software tools. Developers and data
scientists can leverage open-source solutions
like the Intel Distribution of OpenVINO toolkit
to develop and optimize applications that can
seamlessly scale across a wide range of
heterogeneous devices. With just a few code
adjustments, you can adapt a Computer
Vision AI model trained on deep learning
accelerators to run efficiently on a drone or
any other platform.
Intel offers a comprehensive suite of
hardware and software tools that empower
businesses to harness the full potential of
Computer Vision, from simplifying model
development and deployment to providing a
diverse hardware portfolio and open-source
solutions. With Intel’s AI Computer Vision
platform, businesses can confidently navigate
every aspect of the AI pipeline, ultimately
driving performance and accelerating return
on investment.
Nvidia
Artificial Intelligence (AI) is ushering in a new
era of business transformation, but its rapid
integration presents significant challenges. For
enterprises, maintaining a secure and stable
software platform for AI is a complex task.
To address these concerns, NVIDIA has
introduced NVIDIA AI Enterprise. This cloud-
native software platform streamlines the
development and deployment of AI
applications, including generative AI,
Computer Vision, and speech AI. This platform
offers critical benefits for businesses relying
on AI, such as improved productivity, reduced
AI infrastructure costs, and a smooth
transition from pilot to production.
NVIDIA Maxine
NVIDIA AI Enterprise also constitutes
NVIDIA Maxine, exclusively for production
workflows.
In an era where virtual meetings have become
the norm, video conferencing quality has
taken center stage. NVIDIA Maxine, a cutting-
edge suite of GPU-accelerated AI
technologies, has stepped up to the plate to
transform communication through Computer
Vision.
Maxine is a comprehensive software library,
including AI solution workflows, frameworks,
pre-trained models, and infrastructure
optimization. Maxine is designed to enhance
audio and video quality in real-time, adding
augmented reality effects. It achieves
impressive results with standard microphone
and camera equipment and is deployable on-
premises, in the cloud, or at the edge.
Source
Let us explore how Maxine leverages
Computer Vision to revolutionize the video
conferencing experience.
One of Maxine’s standout features is its ability
to remove or replace backgrounds during
video calls effortlessly. Thanks to Computer
Vision, you can now join meetings from
virtually anywhere without the need for a
green screen. Whether you want to project a
professional image or add a touch of whimsy
with virtual backgrounds, Maxine makes it
possible. Let us look at some of the features
of Maxine.
Facial Enhancement: Maxine uses
Computer Vision for real-time facial
alignment and beautification, ensuring a
polished appearance on video calls.
Crystal-Clear Audio: Maxine excels in
audio enhancement, efficiently removing
background noise for pristine, noise-free
audio.
Gaze Correction: Maxine adjusts gaze
direction using Computer Vision,
simulating eye contact and enhancing
natural interaction.
Super-Resolution: Maxine employs AI to
upscale and enhance low-resolution
videos for sharper, detailed quality.
Gesture and Emotion Recognition:
Maxine recognizes gestures and emotions
through Computer Vision, fostering
interactive experiences.
Speech Enhancement: Maxine reduces
echo and eliminates background noise,
ensuring crystal-clear speech in virtual
meetings.
Language Translation: Maxine offers real-
time language translation for seamless
communication in international meetings.
By providing a comprehensive ecosystem for
AI development and deployment, NVIDIA
empowers businesses to unlock the full
potential of AI.
Qualcomm
Qualcomm’s Vision Intelligence Platform is
reshaping the landscape of Computer Vision
in both consumer and enterprise IoT domains.
This powerful platform seamlessly combines
image processing with advanced Artificial
Intelligence (AI) capabilities, elevating the
performance of smart camera products across
a spectrum of IoT devices. From enterprise
and security cameras to industrial and home
monitoring cameras, Qualcomm’s platform is
a driving force behind the integration of on-
device vision AI in applications spanning
security, retail, manufacturing, logistics, and
more.
One example is the iOnRoad application,
which earned recognition with a CES Award
for Design and Engineering. This accolade
from the Consumer Electronics Association
(CEA) underscores the platform’s innovative
use of Computer Vision technology. CV
harnesses video input and high-speed
computation to identify shapes within a given
field of view. In the case of iOnRoad, CV is
ingeniously combined with a mobile phone
camera to detect nearby objects precisely.
Here are a few technical highlights of
Qualcomm’s Vision Intelligence Platform that
further illustrate its capabilities.
Here are a few technical highlights of
Qualcomm’s Vision Intelligence Platform that
further illustrate its capabilities.
FastCV for Snapdragon: This platform
leverages FastCV. This robust tool
enhances image processing and machine
learning capabilities, thereby making
Snapdragon processors even more adept
at handling complex Computer Vision
tasks.
Qualcomm’s commitment to excellence is
evident in the 10-15% overall
performance increase, ensuring seamless
and efficient operation of smart camera
products.
Image conversion speed is crucial in
Computer Vision applications.
Qualcomm’s platform excels in this aspect
by offering a 30% increase in the
conversion speed of YUV420 images to
RGB format.
Beyond the technical marvels, Qualcomm’s
Vision Intelligence Platform brings substantial
business advantages to the table:
Qualcomm’s Vision Intelligence Platform
offers easy integration for Computer
Vision, making it accessible and
uncomplicated.
It extends Computer Vision capabilities to
sub-1GHz processors, expanding
possibilities for middle-tier devices.
The platform revolutionizes IoT devices
with advanced image processing and AI,
simplifying integration and transforming
industries.
Meta
Meta, formerly Facebook, is leveraging
Computer Vision across its platforms and
products to create more immersive
experiences and enhance user safety. Here’s a
concise breakdown of how Meta leverages
Computer Vision.
Content Moderation: Meta uses
Computer Vision to identify and remove
prohibited content from its platforms
automatically.
Image Recognition: Computer Vision tags
individuals in photos and videos for easier
photo tagging.
Augmented Reality (AR): CV overlays
digital objects onto the real world for
immersive AR experiences.
Ad Targeting: It analyzes visual content for
relevant ad targeting.
Accessibility: CV generates alt text for
images to aid visually impaired users.
Marketplace and Shopping: It categorizes
and suggests listings in Meta Marketplace.
Virtual Reality (VR): CV enables hand
tracking in VR environments.
Safety Features: It detects self-harming
content and provides support resources.
Language Translation: Computer Vision
translates text within images to break
language barriers.
Enhanced Video Understanding: CV
improves video recommendations by
analyzing video content.
Earlier this year, Meta took a significant stride
in the realm of Computer Vision by
introducing FACET (FAirness in Computer
Vision EvaluaTion), setting a benchmark in AI.
This innovative tool is designed to evaluate
the fairness of AI models when it comes to
classifying and detecting objects and
individuals in photos and videos.
FACET in action
FACET is built upon a vast dataset comprising
32,000 images featuring 50,000 individuals,
annotated by vision engineers. These images
span various demographic attributes,
occupations, and activities. The goal is to
delve deep into the potential biases that
might exist within AI models.
One of Meta’s key objectives is to encourage
the broader research community to leverage
FACET to scrutinize the fairness of vision and
multimodal AI tasks. By doing so, developers
can gain valuable insights into any biases
present in their AI models and work towards
mitigating them.
Meta’s introduction of the FACET benchmark
represents a huge stride toward fostering
transparent fairness evaluation.
Sony
Sony Semiconductor is at the forefront of
revolutionizing Computer Vision. Their
approach involves leveraging the power of
raw data and pixels right at the source, in
order to send only the most relevant
information to AI systems upstream. This
innovative technique, reminiscent of the
Internet of Things (IoT) model, alleviates the
burden on internet bandwidth and reduces
the strain on GPUs, traditionally responsible
for image processing.
Sony’s vision for the future is clear – they aim
to go beyond merely analyzing full images and
instead delve into the granularity of individual
pixels within cameras themselves. This is
made possible with Aitrios, Sony’s full-stack
AI solution for enterprises, comprising an AI
camera, a machine-learning model, and a
suite of development tools.
Mark Hanson, Vice President of Technology
and Business Innovation at Sony
Semiconductor, emphasizes the importance of
accurate data over aesthetically pleasing data
for AI applications. He points out that
interpreting individual pixels plays a pivotal
role in this endeavor. Let us explore some of
the stages in Sony Stack.
Highlights
1. Sony stack – using logic chips to
optimize pixel structures
2. Detecting objects once sensors
capture the image
3. Processing image data
4. Data flows into larger trained models
within cloud services
The heart of this breakthrough is the Sony
stack, which is equipped with AI cameras
known as IMX500 and IMX501, which
process data differently to cater to AI
needs. Sony employs logic chips that
optimize pixel structures, enhancing their
sensitivity by allowing more light to be
exposed. These logic chips also handle AI
computations, eliminating the need for
data to traverse through bus structures to
GPUs or CPUs.
As soon as the sensor captures an image,
it undergoes processing within
milliseconds. The output can manifest as
detecting objects like people, animals, or
human poses, conveyed as text strings or
metadata.
Aitrios incorporates core technology that
facilitates AI models, including cutting-
edge TinyML for deep learning on
microcontrollers at the edge. Sony takes it
a step further by enabling direct
integration of image-collecting sensors
with cloud models. This integration, akin
to how 5G cells and various sensors feed
data into cloud services, is part of a
collaboration with Microsoft. These
sensors are poised to become endpoints
for processing imaging data right at the
edge.
The processed data can seamlessly flow
into larger trained models within cloud
services like Azure, offering access to
custom or synthetic datasets for AI
training models. An intuitive Aitrios
console serves as the interface for
managing camera technology. It handles
tasks such as searching for cameras,
downloading firmware, managing
updates, and deploying AI models from
the marketplace to the cameras.
AITRIOS
The applications of Sony’s Aitrios technology
are diverse and promising. In retail settings, it
can be employed to determine product
availability on shelves, optimize customer
traffic flow, and identify areas vulnerable to
theft, thereby enhancing security.
Sony’s Aitrios represents a remarkable leap
forward in Vision technology. This innovative
approach conserves bandwidth and
empowers AI systems with more accurate and
granular information by analyzing data and
pixels at the edge, with applications spanning
various industries.
Conclusion
In this read, we’ve looked at what Computer
Vision is, its mechanics, some common CV
tasks, and how companies like Sony and
Qualcomm are implementing it. This read
shed light on the significance of Computer
Vision in AI. Continued advancements in
Computer Vision will undoubtedly play an
integral role in a wide range of industries,
offering numerous opportunities for
innovation and growth. Stay tuned! More
insightful readings are coming your way.
