Top 3 Computer Vision Trends You Need to Know in 2019
Recent advances in computer vision are rapidly taking us towards a future we have long imagined.
A future where you don’t have to feed in or tell computers anything for a range of simple tasks – domestic and industrial. Computers have a vision of their own and can automatically initiate the task you want them to do. [Ex: FaceID by Apple]
Thanks to the advances in deep learning and abundance of visual data available today, computer vision market is one of the fastest growing innovative tech markets today – with a multitude of applications sprouting across multiple industries.
Adopting computer vision is a smart move forward in current dynamics. But don’t just dive in without doing your homework.
To help you with that, here are top computer vision trends that are currently driving value in the sector.
Deep learning algorithms have multiple advantages over traditional machine learning algorithms. First, they effectively reduce the need for frequent human intervention and thorough domain knowledge in training a model. Second, their workflow allows superior accuracy. And third, the more data you give them, the better results they produce, which is not typical with ML algorithms.
How is it relevant in regards to computer vision — Apart from doing a full picture recognition based on extracted features, they can also identify light and dark pixels, categorize lines and shapes to produce far more accurate results, far more efficiently.
For example, recently, Trantor team did a deep learning project for a leading electric appliance company to automate the extraction of data from electrical blueprints. Our solution provided 95% accuracy and reduced the operation cost for the task by 60%.
Another advantage with deep learning is that the more data (images/videos) you feed in, the better the results get. Many retailers are using these features to map customers’ interaction with products in-store so they can provide them a more personalized shopping experience.
Transfer learning has gained a lot of popularity in the recent past. Thanks to its extensive applications in the field of computer vision.
If you are not familiar, it essentially includes using data from one model to train a similar model. The data is fed in the form of layers. These layers are simply properties or constraints related to a specific task.
For instance, if you have a trained ML model A that identifies animals’ pictures, you can use it to train a model D that identifies dogs’ pictures. Or if you want to go a bit complex, then you can use the model D to train a model C that identifies cats’ pictures. In terms of data layers, training D would require adding a few additional layers to A, whereas, training C would require eliminating some dog-specific layers and adding some cat-specific layers to D.
Transfer learning makes things much easier for developers, as you don’t have to work from scratch to train an ML model. And for developers’ convenience, there are thousands of open source ML models, which they can customize to train new models with applications in a number of industries such as retail, healthcare, automobile, transportation, and so on.
Recommended Read: State of Machine Learning (AI) – Notes from DataHack Summit 2018
Point cloud is a set of data points in 3D space. Simply put, every point on the surface of an object has 3-dimensional coordinates (X, Y, Z), which is referred as point cloud.
Point cloud is a 3D machine vision-based technology, which provides an accurate representation of where an object is in the space. For this reason, it has multiple applications that involve object identification or object movement tracking.
Some of these applications are – monitoring of physical assets, scanning construction sites, scanning landscapes, mapping utility infrastructure, etc. These applications can come quite handy in solving real-world problems like urban planning, repair works, natural disaster management, and so on.
Visual content provides a firehose of information about the state of the world. And with today’s data technologies, we are able to observe, record, and act upon this information effectively. As a result, the world is changing rapidly, and for any organization, it is crucial to keep up the pace to stay state-of-the-art for itself and its client.
While we have discussed only major developments here, you shouldn’t limit your focus to these alone. We also encourage you to explore areas like mixed reality, edge computing, and semantic segmentation, which are gaining popularity across industries.