Computer Vision

Your one-stop-shop for Computer Vision applications using ML

We have a team of experienced and certified Machine Learning engineers who are ready to help you with your project.

Get in touch with us today to get started!

our work

We used Computer Vision
to Build a ‘Waste Classifier
App’ to Help Correctly
Deposit Waste

Our developers used machine learning algorithms to build a waste classifier app to help us deposit waste and recycle correctly. This machine learning model was designed, developed, and trained to correctly classify garbage while using an embedded user feedback feature to constantly improve performance.


The Process

To achieve this, our Machine Learning Engineers attached recycling bins with test devices and installed the app with trash cans to get the necessary pictures for training the model. They used the cloud to store the pictures while also deploying the working model to this cloud for ongoing improvements via user feedback and maintenance.

This user feedback feature helps the model produce the most accurate decisions, which is evident by its close to 95% accuracy rate. This machine learning model also successfully completes its objective of reducing as many human elements as possible.

The ‘RootTrash’ application is currently deployed internally at the Rootstrap offices and is now in our development phase for a full future release on iOS
and Android.


Machine Learning Phases

Some of the Computer Vision development services we provide include:

Image Segmentation

These computer vision systems have the capability to associate each pixel contained within images with a class label like a person, car, animal, flower, etc. It effectively uses computer vision algorithms to treat numerous objects from the same class as one single entity.  Image Segmentation uses specialized algorithms to analyze the visual content present in an image and identify certain features in it.

 

Object Detection

These computer vision applications are used for image recognition tasks to identify objects and their locations. To achieve this, these systems place a bounding box around the object(s) in question, as seen here.

 

How we help

The computer vision algorithms powering these systems effectively combine concepts from image classification and localization.

When presented with an image, object detection algorithms return bounding boxes around all objects of interest and assign a class to them.

Our computer vision services span a wide range of industries including HealthCare, Research, E-Commerce, Real Estate, and Tourism, to name a few.

Rootstrap is your one-stop shop for image segmentation projects, ranging from
effective and accurate data analysis to
high-performing end-user applications.

Here are some computer vision application examples
that we provide for these industries:

Recognize areas
in photos & aerial images

Recognize objects
in images

Healthcare
Lesion Detection

Area/volumen of
interest Detection

Classifications of
digital microscopy

Classify images
for e-commerce

Classify images
for touring

Computer Vision Development Services

With our Computer Vision Services, you get access to advanced computer vision algorithms that can process images and provide information derived from your chosen visual features.

Our Computer Vision Engineers develop innovative computer vision models and applications used for feature recognition, object classification, pattern recognition, segmentation, to name a few.

These state-of-the-art models are effective at addressing business challenges across a wide range of industries.

key partners

Our Machine Learning Engineers develop innovative applications by integrating with industry-leading systems such as AWS Computer Vision Services.

With access to high-performance computing and computer vision algorithms, our engineers utilize innovative machine learning models and neural networks to train computers to effectively detect issues and defects to ensure they don’t affect your operations.

Our Computer Vision Approach

Our approach is centered on improving customer experiences, unlocking new business opportunities, optimizing systems, and leveraging real-time data from visual systems to help derive effective business insights.

Our Computer Vision Development approach consists of the following phases:

PHASE 1
Data Acquisition

Collecting data is a crucial step in any Machine Learning project. The process, methodologies, and considerations, vary from project to project. Our team will guide you through the entire process, either by creating sophisticated IoT devices if and when needed, or by pointing us in the right direction if data needs to be acquired from a 3rd party.

PHASE 2
Data Cleaning and Augmentation

Cleaning data is key in machine learning algorithms. Machine learning data can be imbalanced. To combat this, we use augmentation. Data augmentation is the process of creating new data points from already existing data points. This can be done by adding noise, randomly permuting features, or artificially generating new data points.

PHASE 3
Ground Truth Preparation

During this phase, we have to measure and take into consideration many variables, such as data quality, data size, number of classes, and balance of the data. Here, we have to make sure that your data is representative of the real-world data you’ll be using your model on. It’s important here to ensure that data is clean and free of any errors.

PHASE 4
ML Training and Measure

There must be a well-defined task and metric in place when training a machine learning model. The model should be trained on a variety of data including edge cases. It’s imperative to monitor the training process to ensure that the model is learning and not overfitting. Hyperparameter tuning can be used to get the best performance from a model.

PHASE 5
Application layer

Application layers are used to connect the different components and microservices that compose an app, website, or prototype. To achieve this, we incorporate our tried and tested Scrum Methodologies. Our ability to run end-to-end projects from inception, all the way through to the machine learning processes that are used by millions of people on our apps, is what truly sets us apart from the rest.

What is Computer Vision

Computer vision is a service in AI that works to analyze content images and videos

Computer Vision Engineers train machines and systems to perform functionalities similar to human vision. Human sight has the ability to tell images and objects apart, their distance from them, their movement, and any discrepancies they may have.

A computer vision machine must perform these functions in a lot less time than human vision optimizing algorithms, data, and cameras. Because these computer vision systems have the ability to process and analyze at a much faster rate, they can quickly surpass the capabilities of human vision.

The industries in which computer vision is used are growing rapidly. These systems are currently being utilized in industries such as automotive, energy and utilities, manufacturing, to name a few. And while the market continues to grow,
it is forecasted to reach close to 50 billion
USD in 2022.

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