Computer Vision Development Services
Make understanding images and videos easier: use AI to empower your business to see more
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Computer Vision applications allow computers to process images and videos in order to retrieve specific information, automate processes and make decisions. At Miquido, we use Computer Vision to train models to understand visual, high-dimensional data by feeding Machine Learning models with images. We offer AI solutions to get in-depth insights and provide you with comprehensive solutions.
Our Computer Vision development expertise
Image Classification
Labeling and categorising large image datasets can prove to be a difficult task. Image classification involves prediction of the class label for an object in the image, which allows us to automatically assign images to different categories. We can help you with assigning your imagery data to predefined classes and therefore automate the image classification process.
Object Detection
Computer Vision solutions can help you to automate tasks such as monitoring, security systems, etc. This is where Object Detection comes to the rescue: it uses image classification together with other computer vision tasks to identify and localise objects within digital content. It is more advanced than simple image classification, as it is able to classify multiple different types of objects in the image.
Content Indexing
Organising your content properly can save you a lot of valuable time and increase effectiveness. Video and image content indexing detects objects and automatically assigns their tags. Thanks to this feature, your business can speed up real-time video processing, and you can save your team the hassle of manually handling each piece of visual content.
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How can your business benefit from Computer Vision development services?
Computer Vision allows machines to observe the world and translate those observations into actionable findings. See how it might benefit you.
Elevate your processes
Save time and resources by making your business processes less prone to human errors! How do we achieve this? By limiting human involvement thanks to task automation, e.g. text extraction, image classification, product quality inspection on production lines in manufacturing, and much more.
Know your customer
Make sure your customers are who they say they are with the help of Computer Vision services. Verify your clients’ identities by using their photos, ID’s or passports to assess their identity and reduce the fraud risk to a minimum. This way, you can be certain that your business stays safe and secure.
Moderate your content
Keep your content organised and make sure your application is a pleasant experience for all users. Save time on manual content moderation – automate the process of detecting unwanted content, such as offensive gestures and explicit nudity in user-generated images or videos automatically (or semi-automatically).
Improve content discoverability
What’s the point of creating awesome content if no one’s going to find it? Let more users discover, share and interact your content with the aid of Computer Vision solutions. Advanced content searches by image, together with object tagging, allow users to discover similar content and products with ease.
Analyse video streams
Automate tasks related to analysing visual imagery that would take humans lots of time and harness the power of computer vision to examine video content in real time. By analysing and using visual sequences from multiple cameras and drones, you can use the collected footage for object detection and mapping.
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Where does Computer Vision excel?
development
delivered
conducted remotely
Computer Vision solutions at Miquido
Patent Office (GovTech)
OCR-based patent applications classification
AGH Solar Boat (with AGH Solar Boat team)
Real-time object detection and mapping using a video stream
I’ve worked for and owned other companies, and good work ethic can be rare. It’s inspiring to work with Miquido because of their impressive work ethic.
Miquido didn’t just go through the motions. They went above and beyond in being very thoughtful.
Their team differentiates itself through its management approach; their developers stay a long time and have more experience than other partners we’ve had.
Technology stack
Machine Learning
Python
Amazon SageMaker
TensorFlow
Keras
Pandas
Computer Vision
Amazon Rekognition
Vision AI
Cloud AutoML
Image Processing
OpenCV
Custom Computer Vision development
Want to know more about Computer Vision?
Not sure what Computer Vision is? That’s fine! We’ll be happy to walk you through the most common topics behind AI-based Computer Vision.
How can Computer Vision solve my business problems?
By processing photos and videos in real-time, Computer Vision can provide insights that are difficult to obtain through human observation alone.
Computer vision may automate routine tasks. For example, it monitors production lines for defects in manufacturing, ensuring that products meet quality standards. In retail, Computer Vision can track inventory levels and detect when products are out of stock.
Computer Vision can also improve safety in the workplace. In industries such as construction and mining, Computer Vision monitors worksites for safety hazards, such as workers needing to wear proper safety gear or equipment needing repair.
Furthermore, computer Vision may be used to enhance customer experiences. For example, in the healthcare industry, Computer Vision identifies guests and provides personalized services based on their preferences. Computer Vision analyses store customer behaviour in retail to provide a better shopping experience and increase sales.
How is Machine Learning Applied to Computer Vision?
Machine learning is applied in Computer Vision in various ways.
Image classification is one of the most common applications of machine learning. For example, a machine learning algorithm can be trained to recognize different types of animals in images. Image classification is used in facial recognition, object recognition, and medical diagnosis.
Another important application of machine learning is object detection. A machine learning algorithm can be trained to detect different types of vehicles on the road. Object detection is used in self-driving cars, surveillance, and industrial automation.
Machine learning is also used for image segmentation. It is the process of dividing an image into different regions or segments. Image segmentation identifies other objects within an image and can be used in many applications, such as medical imaging, robotics, and augmented reality.
Furthermore, machine learning may be used for object tracking. Object tracking may be used in video surveillance, sports analysis, and traffic monitoring.
Where is Computer Vision used?
In healthcare, Computer Vision is used for medical imaging and diagnosis. Computer Vision algorithms can analyse medical images, such as X-rays and CT scans, and identify diseases. This can help doctors to develop more effective treatment plans. Computer Vision is also used for patient monitoring and to detect vital signs and track patient movements.
Computer Vision is used for fraud detection and security in the financial industry. Algorithms analyse images of checks and credit cards to identify suspicious activity and prevent fraud. Additionally, Computer Vision is used for facial recognition and biometric authentication, which can improve security in banking and financial transactions.
It is also worth mentioning that computer Vision is used in the entertainment industry for virtual reality and augmented reality experiences. Computer Vision algorithms can track user movements and create immersive experiences that enhance the user’s experience.
These are just a few examples of the many use cases of Computer Vision across various industries. However, it is worth remembering that you can also use the possibilities of computer vision in entirely different sectors or cases.. The sky’s the limit.
What are the main types of Computer Vision?
- Image segmentation: It involves dividing an image into distinct regions or segments based on colour, texture, or other visual features. This can be useful for various applications, from identifying objects in a scene to isolating a particular feature for further analysis.
- Object detection: Computer Vision may be used to identify and locate specific objects within an image or video stream. This type of Computer Vision is used in many applications, such as autonomous vehicles, robotics, and surveillance systems.
- Facial recognition: Facial recognition involves identifying and verifying a person’s identity based on their facial features. It can be used for access control, identity verification, and real-time tracking of people.
- Edge detection: It is a type of Computer Vision that involves detecting the edges of objects in an image or video stream. This can be useful for various applications, such as object recognition, tracking, and image enhancement.
- Pattern detection: It involves identifying patterns within an image or video stream and can be helpful for tasks such as object recognition, image retrieval, and video analysis.
- Image classification: It is used to categorise images based on their content, such as identifying whether an image contains a person, an animal, or an object. This is a fundamental task in Computer Vision and is used in many applications, such as image search and content-based image retrieval.
- Feature matching: It is a type of Computer Vision that involves finding similarities between images based on visual features. This can be useful for tasks such as image retrieval, object recognition, and 3D reconstruction.
Our Computer Vision development services across the industries
Healthcare is an industry which Computer Vision solutions are truly helping to revolutionise. Speed up the diagnostics process and assist radiologists with scan interpretations with medical image segmentation and analysis. Extraction of image features and automated detection of altered areas can be used for all contemporary imaging modalities like CT, MRI, PET and SPECT, as well as 3D model building.
Search content by image and provide efficient recommendations based on visual characteristics. Low-quality images displayed on your website reflect poorly on your business; image quality control using CV models may be trained to detect and remove, for example, blurred photos that don’t meet required standards. You can also optimise images for the Internet, remove duplicates, and match image content description with the product.
Make your entertainment application both fun and secure for your users, and reduce the workload for your team at the same time. You can make use of facial recognition and tagging on social media with picture metadata, which is based on detecting individual features and matching them with a facial database. For user-generated image quality control, you can automate detecting nudity and banned content before it is visible to the public.
Fintech is an industry that thrives on competitiveness, which is why harnessing the most of the available tech solutions is crucial to succeed. Automate client identity verification and make it available 24/7, make mobile payment more secure using facial recognition and authentication of the right person, use imagery of properties and cars to accelerate your insurance underwriting and quotation process, and much more!