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Semantic Segmentation Services

Services We Deliver

Klatch's Semantic Image Segmentation Services

We support clients with all semantic segmentation needs, such as collecting and developing neural network training datasets for image processing systems and NLP solutions. Our goal is to enhance and annotate data precisely and securely, enabling your data projects to succeed.

Semantic Segmentation

Semantic segmentation helps train computer vision models, which includes taking raw data such as 2D pictures as input and giving a label to each pixel in the image.

Instance Segmentation

Instance segmentation aids in training the machine learning model at the instance level by assigning numerous items of the same class to the same class but as independent instances.

Panoptic Segmentation

Panoptic segmentation combines instance and semantic segmentation to perform pattern recognition and identify pixels in images belonging to a class and their instances.

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Industries we serve

Industries benefiting from our Semantic Segmentation solutions

Our Semantic Image Segmentation services are used across various sectors that use AI or computer vision based models. We cover all industries, from retail to institutions and healthcare, with the same degree of attention and quality.

Autonomous Transportation
Image segmentation can assist autonomous vehicles and transportation technologies in more precisely detecting distinct objects, signs, and obstacles on the road. As a result, it is an essential element in ensuring safety.
Healthcare
Medical image segmentation helps uncover MRI anomalies and enhances radiologists’ analysis, speeding up diagnostic procedures. Examine X-rays, MRIs, and other image-based tests to quickly determine the type of disease and the patient’s state.
Commerce
Image segmentation helps automate retail inventory analysis. Produce high-quality data and insights, such as customer footfall, to improve inventory planning, categorize products in-store, and learn about customers’ attitudes.
Geospatial Technology
Geospatial image segmentation identifies satellite images and maps of the Earth from above, allowing for infrastructure planning, land cover analyses, humanitarian crisis mapping, and environmental evaluations.
Agriculture
Large farmers and agriculture companies use image segmentation to differentiate crops and weeds. It helps them in weeding, monitoring, improving and protecting the health of crops or livestock.
Manufacturing & Robotics
Klatch provides clients with 2D or 3D cuboid image annotation to train their AI-powered robots and recognize products of different dimensions in automated warehouses, factories, and other facilities.

why choose us

Why Klatch is the right partner

Our semantic segmentation propels you from experimental R&D prototypes to operation, production-ready solutions. We understand that your data training process is dynamic, and we strive to be as agile and adaptable as you require.

Quality with Accuracy
Get the best-in-class quality services with the highest accuracy level, delivering excellent segmentation through multiple stages of auditing and reviewing labeled data.
Fully Scalable Workforce
Work with an expert-degree prolonged workforce to segment data as consistent with the call for a scalable answer with a turnaround time to satisfy the unique client’s needs.
Cost-Effective Pricing
Outsourcing image segmentation means that clients have access to cost-effective annotation services that help minimize project costs and help them with the highest efficiency.
Security with Privacy
We are certified and manage employees worldwide, all under strict NDA. We perform in monitored facilities for high-protection work, with strict protection protocols.

delivery

Semantic Segmentation process

Our specialists and technology enable your project with capacity control, real-time quality monitoring, secured access, and streamlined team collaboration.

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Expert Consultation
Dynamic, solution-oriented strategy. Problem-solving through entensive segmentation.
Training
Targeted resources. Skilled specialists. Focused and in-depth formative assessment program. Domain knowledge. Drafting tools.
Process Customization
Annotation tools and procedures must be in sync. Development milestones are structured on production processes and quality assurance in multiple phases.
Feedback Loop
Analytics focused on providing transparency. Insights on service delivery and real-time tracking of the project. Model enhancement using dynamic simulation.
Assessment
Deliverable assessment. Key metric assessment, as well as quality control procedures. Rethinking the model. Analyze business outcomes.

FAQ

Frequently asked questions about semantic segmentation

What is meant by semantic segmentation?

Semantic segmentation is the field of computer vision that focuses on segmenting a digital image into several segments (regions or categories) based on its qualities and features. It is an instance of image processing. The main goal of any segmentation algorithm is to simplify the image and make it more understandable and easy to examine. Every pixel in an image is labeled in semantic segmentation so that pixels with the same label share certain qualities and properties. Recent image segmentation procedures employ deep learning models such as convolutional neural networks to achieve the segmentation.

What is semantic segmentation example?

5 semantic segmentation examples are:

Autonomous transportation and technology: Semantic segmentation is helpful for autonomous transportation and technologies like self-driving cars and drones. Autonomous vehicles, for instance, can identify accessible terrain.
Agriculture: Large farmers and agriculture companies use image segmentation to differentiate crops and weeds. It helps them in weeding, monitoring, improving and protecting the health of crops or livestock.
Medical AI: Medical image segmentation helps uncover MRI anomalies and enhances radiologists’ analysis, speeding up diagnostic procedures. Examine X-rays, MRIs, and other image-based tests to quickly determine the type of disease and the patient’s state.
Retail and E-Commerce: Image segmentation helps automate retail inventory analysis. Produce high-quality data and insights, such as customer footfall, to improve inventory planning, categorize products in-store, and learn about customers’ attitudes.
Manufacturing and Robotics: Klatch provides clients with 2D or 3D cuboid image annotation to train their AI-powered robots and recognize products of different dimensions in automated warehouses, factories, and other facilities.

What are the types of image segmentation?

3 types of image segmentation are:

Semantic Segmentation: Semantic segmentation helps train computer vision models, which includes taking raw data such as 2D pictures as input and giving a label to each pixel in the image.
Instance Segmentation: Instance segmentation aids in training the machine learning model at the instance level by assigning numerous items of the same class to the same class but as independent instances.
Panoptic Segmentation: Panoptic segmentation combines instance and semantic segmentation to perform pattern recognition and identify pixels in images belonging to a class and their instances.

Klatch Technologies caters to all kinds of semantic image segmentation or data labeling requirements for any project size.

Is outsourcing semantic segmentation projects safe?

We assure your data protection by combining streamlined semantic segmentation operations and cross-quality checks. The results are supplied in a secure manner tailored to your specific requirements. Our annotation teams adhere to a stringent nondisclosure agreement.

Image segmentation can be done in-house until there is too much data to handle. When things get hard, you’ll need specialists and the correct tools to label them accurately. For example, if you are developing a medical product, you will need a subject matter expert to classify objects found on an MRI report. Klatch data specialists can annotate datasets in various processes.

What is semantic segmentation in deep learning?

Semantic segmentation in deep learning is an innovation in image segmentation using convolutional neural networks. Semantic image segmentation segments a digital image based on its quality and attributes. It is an example of image processing. Segmentation algorithms aim to simplify images and make them easier to analyze. In semantic segmentation, each pixel in an image is labeled so that pixels with the same label share attributes.

What is the difference between semantic segmentation and instance segmentation?

The difference between instance and semantic segmentation is:

Semantic Segmentation: Semantic segmentation helps train computer vision models, which includes taking raw data such as 2D pictures as input and giving a label to each pixel in the image.
Instance Segmentation: Instance segmentation aids in training the machine learning model at the instance level by assigning numerous items of the same class to the same class but as independent instances.

Klatch Technologies offers semantic image segmentation services to prepare and provide various relevant, high-quality, and unique datasets.

Why do we do semantic segmentation?

We do semantic segmentation by Structural, Stochastic and Hybrid segmentation techniques, as in:

Threshold segmentation
Edge-based segmentation
Region-based segmentation
Clustering based segmentation
Watershed segmentation
Artificial Neural Network based segmentation
Partial Differential Equation based segmentation

What location are you in?

Our corporate headquarters are in India. We have various offshoring centers in Asia and Africa. Our client support teams are available 24 hours a day, 7 days a week, and keep them up to date on the project’s status.