Computer Vision Consulting ─ a Reliable Way to Achieve More in the Market
In the coming years, computer vision technologies will change the usual production, education, ways of doing business, and many other processes beyond recognition. According to experts, the global CV market will grow to $19 billion in market revenue by 2027.
Rapid growth is driven by the adoption of process automation and the demand for robotic systems with visual control. But you need to assemble a team of specialists with the necessary technical skills, which are being hunted by the world’s leading companies. This requires time, money, a clear strategy, and an understanding of how to mitigate potential risks. Given the complexity of the tasks, the best choice is professional computer vision consulting.
In this article, Axisbits shares tips on how to choose a reliable partner based on real-world success stories. Learn how to approach digital transformation and overcome key challenges.
Fields of Computer Vision
Computer vision is an advanced computer area of expertise based on reproducing human vision and allowing a machine to identify objects more clearly in images and videos.
The vast field of AI tech includes a set of techniques that enable the computer to extract the information it needs. This is one of the areas of machine learning with integrated concepts.
With advances in deep learning and neural networks, there has been a big leap forward and CV has succeeded in surpassing humans in tasks related to object detection and tagging.
Project Ideas for Computer Vision
In order to teach a computer to “see” properly, it is necessary to collect a lot of data and to identify objects precisely enough to highlight similar traits and signs. These ideas are implemented in different ways.
Successful examples of computer vision use can be seen in the industrial, medical, sports, aerospace, financial, advertising, and other sectors, leading to an increase in demand as well as in the number of related applications. Below are some of the most exciting cases.
Human pose analysis
In a nutshell, Posenet is an open-source model that uses computer vision to determine the pose of a person or object in an image/video.
Pose analysis is performed by identifying, locating, and tracking the coordinates of key points of the human skeleton. The solution is applied for:
- activity recognition for sports analysis or real-time surveillance systems;
- augmented reality purposes;
- machine learning;
- animation and video games.
And these data sets can help you develop a similar model of your own:
Image transformation via GAN
Faceapp is a tool that transforms an input image using filters. In training, Generative adversarial networks, known as GANs, use two neural networks playing against each other to generate new data based on the distribution of the training data. Here are datasets to help you create something similar:
Technology can be used to track people in a room or area in real time to see if they are complying with norms.
Violation detection is determined when the object goes beyond the bounding rectangle. To make the instruments more accurate, you can use transfer learning methods. There are also various pre-trained object detection models like YOLO and Mask R-CNN.
Creation of 3D models out of 2D images
Deep Mind researchers have come up with an AI system that transforms 2D images into 3D visuals. Known as the generative query network, it can perceive images from different angles.
Nvidia has developed an architecture that can predict the 3D properties of an image. Facebook AI offers a similar tool known as the 3D Photo feature.
And here are some related data sets:
Medical image analysis
Images produced by CT and X-ray equipment can be conveniently converted into interactive 3D models for very visual, simple yet in-depth interpretations of patient details.
According to the research of the central Radiology department in Wuhan, deep learning methods can be most efficiently employed to distinguish between Covid-19 and regular pneumonia. Data sets by Kaggle will come in more than handy for that.
How to Evaluate Computer Vision Development Companies
- Explore the quality of services, experience, and security standards of the development companies.
- Consider reviews of service providers on websites, LinkedIn accounts, rating platforms (Clutch, Manifest, Goodfirms).
- Consider the size and recruitment opportunities as medium to large suppliers are more likely to have all the resources needed.
- Pay attention to equipment and infrastructure in hand.
- Ensure compliance with ISO, PCI, HIPAA standards, and the effectiveness of the security policy.
- Ask for a portfolio of successfully completed projects.
You should also check out the savviness of software engineers:
- 3-5 years of experience with C/C ++, Python, Java, .NET;
- Extensive experience in AI and machine learning, big data, cloud technologies, DevOps, and quality control;
- Minimum tech stack: OpenCV, VisionWorks, PyTorch, TensorFlow, Caffe, SimpleCV;
- Proven skills in vision projects development, training, testing, and model optimization;
- Experience with methods of classification, detection, and segmentation of objects;
- Knowledge in OCR; NLP; CNN;
- Understanding of supervised, unsupervised learning, and reinforcement learning.
You may be surprised, but this is exactly the profile of Axisbits – we have engineers ready to take on the most daring project ideas for computer vision.
Despite impressive progress, the world is still far from solving all CV problems. Especially when it comes to beginner computer vision projects. But the trend is promising, which may be the reason why so many companies go for computer vision consulting.
If you decide to radically transform your business processes for the better, contact our specialists. Axisbits is a computer vision development company that has sufficient expertise to provide a quality solution of any complexity and purpose.