Image Segmentation Service

iMerit delivers image segmentation services that power Artificial Intelligence, Machine Learning, and data operation strategies.

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Image Segmentation Service
What is Image Segmentation

What is Image Segmentation?

Image segmentation is a branch of computer vision that focuses on the division of a digital image into multiple segments (regions or categories) according to its properties and features. It is a form of image processing. The main purpose of any segmentation algorithm is to simplify the image and make it more meaningful and easier to analyze. In image segmentation, every pixel in an image is labeled such that pixels with the same label share certain attributes and characteristics. Most modern image segmentation workflows use deep learning models such as convolutional neural networks to perform the segmentation.

The segments in segmented images can also be clustered using clustering algorithms such as k-means clustering. This allows unsupervised image classification to be applied to the individual segments.

common models and data processing

Most image segmentation models use some sort of digital image processing to standardize original image data prior to training on the GPU. This can involve various filtering steps such as converting a given image to grayscale, normalizing pixel values and pixel intensities, clipping pixels at some threshold value, partitioning the input image, applying segmentation masks, applying a gaussian blur, implementing Otsu’s method for image thresholding, drawing bounding boxes around regions of interest for object detection, smoothing discontinuities, and other optimizations.

Once the image data has been processed, a classifier model is trained to divide the entire image into different segments using the class labels that have been provided. Popular image segmentation algorithms include U-Net, Mask R-CNN, and encoder/decoder models, all of which can be found on GitHub implemented in Python and other languages along with tutorials on how to use them. The training proceeds over many iterations, while optimization is performed using an algorithm such as gradient descent. Many image segmentation methods follow this framework.

iMerit’s Image Segmentation Solution

iMerit provides various image segmentation techniques and services that cater to a client’s project needs. Common workflows include — semantic segmentation, instance segmentation, and panoptic segmentation. iMerit’s team works with the client to calibrate their quality and throughput requirements and builds custom processes to support client image analysis needs.

Semantic Segmentation
Semantic Segmentation

Semantic segmentation helps to train computer vision models that involve taking as input as some raw data like 2D images and assigning a label to every pixel in the image.

Instance Segmentation
Instance Segmentation

Instance segmentation helps to train the machine learning model at the instance level where multiple objects of the same class are assigned as one class but as separate instances.

Panoptic segmentation
Panoptic segmentation

Coupling instance and semantic segmentation, panoptic segmentation performs pattern recognition and helps to identify the pixels in images as belonging to a class and to identify what instances of that class they belong to.

Image Segmentation
Steps

iMerit subject matter experts will guide you through the process to develop a customized end-to-end workflow.

1
EXPERT CONSULTATION

Transformative, solution-based approach. Interdisciplinary image segmentation problem solving. Agility and responsiveness, Time-To-Value enhancers.

2
TRAINING

Targeted resources. Custom skilling. Focused and deep microlearning curriculum. Domain expertise. Rostering tools.

3
WORKFLOW CUSTOMIZATION

Alignment of image segmentation tools and processes. Structured Development Milestones. Two-step production and QA annotation workflows.

4
FEEDBACK CYCLE

Transparency via analytics. Real Time Monitoring and Service Delivery Insights. Edge case Insights. Dynamic Model Improvement.

5
EVALUATION

Assessment of deliverable. Appraisal of key metrics, quality control processes. Model reconsideration. Analysis of business outcome.

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Industries using Image Segmentation Service Today

Autonomous vehicle

Image segmentation in autonomous technology helps to train machine learning and edge detection models to identify pedestrians, other vehicles, lanes, and other objects of interest, allowing self-driving cars to operate safely.
Agriculture

Agricultural robots mostly depend on image segmentation for distinguishing between crops and weeds to perform weeding actions and increase crop health while reducing the number of chemicals used.
Medical AI

Medical image segmentation in medical imaging helps to detect tumors, abscesses, other MRI abnormalities and also helps to augment analysis performed by radiologists, greatly reducing the time required to run diagnostic tests
Geospatial Technology

Image segmentation in geospatial technology labels satellite images and maps the world from above which enables infrastructure planning, land cover analysis, humanitarian crisis maps, and environmental assessments
E-commerce

With changing technology, fashion retailers use image segmentation to automate inventory analysis within shops. It is also used to automatically collect product information, optimize store display, and verify shelf-share.

Image Segmentation services Case Studies

Image segmentation for leading automobile manufacturers

Image segmentation for leading automobile manufacturers

Client Profile: Leading global automobile manufacturer

Client data type: Street scene images

Challenge: Building a highly accurate dataset to help train algorithms to interpret their environment

Volume: Around 100,000 street images segmented

Outcome: Our team has annotated the elements in the images into predetermined classes of objects, ultimately dividing the image into semantically meaningful parts, to train the machine learning algorithm not just to ‘see’ but also to understand and interpret its environment and accuracy.

Semantic Segmentation for a large AV company

Client Profile: Large autonomous vehicle company

Client data type: 2D street scene images

Challenge: Building a training dataset that precisely identifies the different objects on a road scene for the AV to take real-time decisions and act accordingly when on the road. With more road scenes comes more complexity and more objects that need to be trained to the AV.

Volume: Around 100,000 images segmented

Outcome: The precise segmentation done by us was used in training the AV and then helped them in putting the vehicle in their testing environment. This takes the AV closer towards full autonomy.

Semantic Segmentation for a large AV company

GETTING STARTED WITH Image Segmentation

The need for speed in high-quality Image Segmentation has never been greater. iMerit combines the best predictive and automated annotation technology with world-class data annotation and subject matter experts to deliver the data you need to get to production, fast.

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Learn more about Image Segmentation

whitepaper

Data Annotation For Autonomous Vehicle Development

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post

Future of Autonomous Technology

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