Object       segmentation

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Our expertise in deep learning object segmentation

OpenCV.ai specializes in empowering machines to interpret and understand the visual world, transforming images and video into actionable insights. By integrating the various image segmentation methods provided by OpenCV.ai, you can unlock new perspectives from your data, enabling smarter decisions and innovative business solutions.
01
Instance segmentation

Instance segmentation pinpoints every single item in your images and videos, with perfect detail. With it, you receive effortless inventory control without manual counting, personalized marketing campaigns based on what your customers are wearing, and higher accuracy in identifying product defects.

02
Semantic segmentation

Implement semantic image segmentation to understand the contents of the scene or video. Semantic segmentation will help you optimize production lines, ensure flawless quality control, train robots to navigate complex environments, and more.

03
Panoptic segmentation

Let us augment your object segmentation model with panoptic algorithms that can see both distinct objects and amorphous areas (sky, road) with pinpoint accuracy. This allows you to train autonomous vehicles, optimize crop yields and monitor field health, or segment tumors, organs, and other structures in medical scans.

04
Graph-based segmentation

Embed efficient graph-based image segmentation algorithms to free up your human resources and gain a tangible edge in your industry.
The technology is proven to easily analyze complex machine parts, segment cells with high accuracy in medical research and disease diagnosis, logistics and supply chain, retail, and other industries.

05
Region-based segmentation

This technique focuses on the spatial distribution of image elements and provides valuable insights and benefits to many business verticals.
You can use it to maintain high production quality, analyze online customer behavior for personalized recommendations, improve object recognition in security footage, and automate other visual tasks.

06
Object segmentation-based CV solutions

Image and video object segmentation forms the foundation of many complex solutions that our vetted team delivers to our clients. Open up new opportunities and growth directions for your business with our wide range of computer vision services — from real-time image segmentation and object detection to AI image classification, object tracking, facial recognition, and pose estimation.

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Not sure what image segmentation algorithms are best suited for your needs?
Let OpenCV.ai's consultants and engineers guide you through the dense jungle of computer vision and build a solution that delivers maximum business value.
opencv.ai in numbers
How OpenCV.ai paved its way in computer vision
We can assure our customers of our expertise in computer vision and our eagerness to tackle any visual AI challenge their business faces. The OpenCV.ai team is skilled in building object-based segmentation solutions and ready to help businesses with its visual innovation initiatives.
60 Countries where our solutions are used
6 Fortune500 Clients
80+ Successfully Executed Computer Vision Solutions
50+ Professionals in-house
50+ Professionals in-house60 Countries where our solutions are used80+ Successfully Executed Computer Vision Solutions6 Fortune500 Clients
Our Process

How we build object segmentation models

Building image segmentation deep learning models can be a challenging endeavor without the right process. At OpenCV.ai, we’ve developed a streamlined system for turning your ideas into reality.
01
Interview
  • Get to know you, your business and your customers
  • Understand your pain points and desired outcomes
  • Find out what problems you face and what features you need
02
Planning
  • Analyze the information we collect
  • Recommend the most efficient solution based on your needs, specifications, and requirements
  • Document everything from hardware requirements to project timelines
03
Data preparation
  • Create a database of images extracted from multiple sources
  • Ensure data is structured, relevant, and of high quality
04
Model development
  • Train the model with clean data
  • Develop the solution
  • Make adjustments to the project as needed
05
Deployment
  • Launch the solution or integrate it into your existing systems
  • Facilitate a seamless launch to increase its value from the outset
06
Continuous support
  • Provide expert assistance after the solution’s launch
  • Make updates and changes as required
Our case studies

Success stories

Hundreds completed projects and counting.
With years of experience providing real-time object segmentation development services, we have learned a lot. We approach each project with respect, enthusiasm, and a commitment to your satisfaction. These case studies are a small sample of the results we have delivered to our clients:
AI in Tennis
ai in construction
ai in retail
Portfolio
Book
Management

Meet our team

Behind the scenes, our team of passionate engineers and data scientists is constantly pushing the boundaries of object segmentation technologies. Masterminds crafting the algorithms, our developers deliver solutions that work best for your specific needs and identify objects with the highest accuracy.
CEO
ANNA
KOGAN
CTO
Tatiana
Khanova
Chief Scientist and President
Dr. Gary
Bradski
our approach

Why choose OpenCV.ai for your object detection and segmentation project?

At OpenCV.ai, we're not just revolutionizing computer vision we're redefining it. Our computer vision consultants and developers have extensive experience working with organizations ranging from agile startups to established enterprises. Here are just a few of the reasons you should trust our computer vision AI company:
Advanced tech stack
We develop and deploy proven and cutting-edge algorithms and technologies for accurate, reliable, and fast object and human image segmentation. In addition, our team is constantly refining advanced algorithms to stay at the forefront of the field and provide you with the best quality of service.
Integration & compatibility
Solutions are designed to integrate seamlessly with your existing systems and workflows. Whether you're using a custom platform or a popular cloud service, we deliver a solution that fits your needs. We ensure compatibility with multiple data formats and platforms to minimize disruption to your operations.
Quality data pre-processing
The OpenCV.ai team uses only the latest data preprocessing methods and techniques to ensure that your data is well optimized for the segmentation process. The process includes noise reduction, image correction, and data augmentation to improve the accuracy and performance of your models.
High customization
OpenCV.ai offers tailored solutions and image segmentation techniques for our clients with the flexibility that they need to stay competitive in their industries. Our experience of working in different verticals gives us an insight into business-specific tech solutions and the ability to fulfill all your CV wishes.
Certified software engineers
Our development team consists of highly skilled and certified professionals. They have in-depth knowledge and expertise in various image segmentation methods to develop robust, scalable, and efficient computer vision solutions that meet the highest industry standards.
High data protection
Your privacy is our priority. Industry-leading encryption protects your information, while granular access controls ensure that only authorized personnel see your data. Rest assured that your valuable information is protected throughout our computer vision process.
Tech stack we use

Object segmentation platforms we work with

Our talented team of experts has years of experience in developing and optimizing some of the most high-performance neural networks, engines, and 3D object segmentation models. In our work, we utilize the following platforms and technologies:

Tools

Pytorch
TensorFlow
OpenCV
CUDA/cuDNN
Core ML
TensorRT
CVAT
JAX
ONNX
OpenVINO
Deepstream SDK
TFLite

Hardware

ARM
RISC V
Jetson
NVIDIA GPU
Raspberry Pi
Intel / AMD CPU
Intel Myriad X

Questions?
Answers.

Are you limited to the OpenCV library?

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Despite our close connection with the OpenCV library, our team at OpenCV.ai doesn't limit ourselves to just this library. We provide object segmentation services to assist our clients in enhancing their businesses through visual intelligence.

What is the price of object segmentation solutions?

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The cost of your object segmentation solution can vary widely depending on the complexity of the project, the level of customization required, and your unique requirements. Contact our team for a personalized quote and to agree on the most cost-effective solution that meets your needs and requirements.

What is human pose estimation in gaming?

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Human pose estimation is a computer vision technique that involves detecting and localizing human figures in images or videos, and then estimating the person's pose by identifying key points on the body, such as the joints of the limbs and the head. This technology makes it possible to understand human posture, movement, and actions.

What is the difference between object detection and object segmentation?

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Object detection involves identifying and locating objects within an image and drawing bounding boxes around them. In contrast, object segmentation takes this a step further by not only detecting objects but also outlining their precise shapes, boundaries, and forms within the image.

What is object segmentation in computer vision?

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Object segmentation in computer vision is the process of dividing an image into segments to identify specific objects or areas of interest. Each segment represents a set of pixels that are closely related and share similar characteristics, which allows for simplified and more efficient image analysis.

What is 3D segmentation?

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3D segmentation is an advanced form of object segmentation that applies to three-dimensional data, such as medical scans or 3D models.

What is image segmentation and why is it important?

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Image segmentation is the technique of dividing an image into multiple segments or objects.
It separates objects from the background and from each other based on color, texture, or other features. This makes complex images easier to analyze. By understanding what's in an image, computers can perform tasks such as self-driving cars that navigate roads, medical imaging tools that identify tumors, and more.

What are the different types of image segmentation techniques?

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There are several types of image segmentation techniques, including thresholding, edge detection, instance and semantic segmentation, clustering, watershed, and machine learning-based methods such as convolutional neural networks (CNNs). Each technique has its strengths and is suitable for different types of images and segmentation tasks. Check out our services above to see which one is more appropriate for your business and will provide more value.

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