Computer Vision Development Services
See more and do more. More than you could have expected.
We help businesses optimize their operations by integrating cutting-edge Computer Vision technology to automate tasks, improve accuracy, and enhance decision-making capabilities.
Companies see the unseen with Tensorway Computer Vision solutions, uncovering patterns and insights that were previously hidden. How?
Computer Vision (CV) enables computers to process and understand visual information, granting them the ability to perceive and respond to their surroundings like never before.
It allows for the automation of tasks such as image and video analysis, object detection and facial recognition. This improves efficiency, reduces costs and increases accuracy, allowing businesses to optimize their operations and gain a deeper understanding of their customers, products and operations.
Open new possibilities
Computer Vision tasks and use cases for business
Image generation
Computer Vision-based software is able to generate images based on prompts, and use other images as references. It can be used for smart product design, virtual try-on for e-commerce, and more.
Object detection
We are able to identify and locate objects within an image or video. As a result, organizations can use it to detect people on the streets, pathologies on medical images, cars on the road, etc.
Segmentation
Computer vision software can detect objects with pixel-wise accuracy, thus enabling applications for a variety of industries, such as detecting abnormalities in medical images, and much more.
Classification
By assigning a label or class to an image or video based on its content, we are able to re-identify people, car license plates, or add quality control to production with accuracy never seen before.
Healthcare
Computer Vision is transforming the medical field, from analyzing diagnostic images to aiding in remote consultations.
In surgical and interventional procedures
Computer Vision facilitates minimally invasive procedures, such as endoscopic surgery, by providing real-time information about the location and orientation of surgical instruments within the body.
By analyzing medical images
such as X-rays, CT scans, and MRIs, CV algorithms can help identify and diagnose a wide range of medical conditions
In telemedicine
Computer Vision technology assists in analyzing live video feeds of patients, allowing doctors to provide remote consultations.
Entertainment
Computer Vision is reshaping the entertainment industry, from motion capture and virtual reality to enhancing broadcasting and film production experiences.
In motion capture
CV technology facilitates tracking the movement of actors and translating it into computer-generated animation, which is used in movies, video games, and other forms of digital entertainment.
For Virtual and Augmented Reality
CV plays a crucial role in providing tracking and mapping of the real-world environment to create realistic virtual worlds.
In sports broadcasting
CV can be used to track players, balls, and other objects, providing real-time statistics and improved viewing experiences for audiences.
In Film and video production
CV aids in automatically editing and color-correcting footage and creating special effects, such as adding virtual backgrounds and objects to live-action scenes.
In Streaming platforms
CV is employed to analyze and understand the content of videos and images in streaming platforms, and recommend similar content to users.
Retail
The retail sector is harnessing Computer Vision to enhance customer experiences and optimize store operations.
In retail analytics
Computer vision aids in tracking customer behavior in physical stores, such as foot traffic, product interactions, and queue management. This information can be used to optimize store layouts, product displays, and staffing levels.
For Inventory management
Computer Vision facilitates automatically counting and tracking inventory in retail warehouses and stores.
Enabling autonomous shops
Computer Vision can be used to automatically identify and scan products at self-checkout kiosks, reducing the need for human cashiers.
In virtual fitting rooms
Computer Vision-based software can enable customers to try on clothes and accessories virtually, using their own images or 3D models.
In e-commerce business
we use CV to analyze images of products, providing better product search, tagging, and recommendation features.
For loss prevention
Computer Vision algorithms monitor CCTV footage in real-time to detect and prevent shoplifting, employee theft and other forms of losses.
Transportation
In the transportation sector, Computer Vision plays a crucial role, from the development of autonomous vehicles to the optimization of public transit systems.
In cargo and logistics
Computer Vision is employed to automate the sorting and handling of packages and cargo in warehouses and distribution centres, which can improve the efficiency and accuracy of logistics operations.
Enabling self-driving cars
Computer Vision provides the ability for cars to perceive and understand their environment and to make driving decisions.
In traffic management
CV can be used to analyze traffic patterns and optimize traffic flow, helping to reduce congestion and improve safety.
In public transportation
Computer Vision is utilized to monitor public transportation systems, track the location and status of vehicles and improve scheduling and routing.
As a part of Advanced Driver-Assistance Systems (ADAS)
Computer Vision is implemented to provide drivers with real-time information such as lane departure warnings, traffic sign recognition and collision avoidance.
Finance
The financial world is leveraging Computer Vision for efficient document processing, enhanced fraud detection, and more.
In document scanning and processing
Computer Vision can be used to automatically extract information from financial documents, such as invoices and receipts, which can reduce the need for manual data entry and improve.
In credit analysis
Computer Vision is applied to analyze images of financial documents, such as tax returns, pay stubs, and bank statements, to assess the creditworthiness of borrowers and to make lending decisions.
For fraud detection
Computer Vision is employed to analyze images of ID documents and banknotes, to detect and prevent fraudulent activities, such as money laundering, identity theft, and counterfeit detection.
In risk management software
Computer Vision is implemented to analyze large amounts of data and to identify patterns and trends that can help financial institutions to manage risks and to make better investment decisions.
In trading
CV can be used to analyze real-time financial data, such as stock prices, to make predictions and identify patterns, which can help traders to make more informed decisions and improve their trading performance.
Using NLP with Computer Vision for powerful results
Computer Vision and Natural Language Processing (NLP) are often used together to solve complex problems that involve both visual and textual data.
For example, Computer Vision is able to process images and extract visual features, while NLP can be used to process text and extract semantic information. As a result, we get new and innovative apps that can understand and respond to images and videos in the same way as humans do.
Case studies: NLP and CV used together in business
Combining these technologies can help businesses gain insights from visual and textual data, automate processes, and improve customer engagement and satisfaction.
Key stages of Computer Vision development
Our Computer Vision Software Development Process
1
Problem definition
The first step is to clearly define the problem that the Computer Vision system is intended to solve. This includes identifying the specific tasks that the system needs to perform, as well as any constraints or requirements that must be met.
2
Data collection and processing
In order to train and evaluate a Computer Vision system, a large dataset of images or videos is needed. We find a pretrained model to collect data from a variety of sources.
3
Approach or Model selection
At this stage, we have to decide the best development approach or model that will be used to solve the problem. This decision will be based on the problem's complexity, available data, and resources.
6
Model deployment
Once the model has been developed and evaluated, it can be deployed in the real world. This may involve integrating the model into an existing application or building a new application around the model.
5
Model evaluation
Once the model has been trained, it is important to evaluate its performance to ensure that it is performing well on the task at hand. This typically involves using a set of test data that was not used during training and comparing the model's predictions to the ground truth.
4
Model development
After pre-processing the data, the next step is to develop the Computer Vision model. This typically involves selecting a model architecture, such as a convolutional neural network (CNN), and then training the model using the pre-processed data.
1
Understand your business requirements
Our team analyzes your business and identifies your needs to provide a software solution that can solve your business problems.
2
Data collection and preparation
Collect and prepare the data that will be used to train the model. This involves sourcing and collecting data, as well as cleaning and preprocessing it.
3
Model design and architecture
This step involves selecting the appropriate type of model, such as a convolutional neural network or a recurrent neural network, and determining the number and size of the model's layers.
4
Model training
By training the Deep Learning model on a large and diverse dataset, we can ensure that the model is able to generalize well and perform accurately on a wide range of inputs.
5
Model evaluation
We evaluate the model's performance on a separate dataset to identify any weaknesses or issues with the model and make necessary improvements.
6
Model deployment
If the model performs well during evaluation, it can then be deployed in a production environment, where it can be used to make predictions or decisions based on real-world data.
What's next?
After the deployment, the system should be maintained and updated regularly, by monitoring its performance, updating the model with new data and fixing bugs.
