Computer vision technology is also changing in the industry, making consumers’ lives not only easier, but more interesting. As an emerging field, computer vision has received extensive publicity and considerable investment. The total investment 120 million U.S. dollars in the North American computer vision software market. While the Chinese market surged to 3.9 billion U.S. dollars. Let’s take a look at the most promising and interesting technologies. That have made the computer vision software development market so fast.
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Progress in deep learning
Deep learning is popular due to its advantages in providing accurate results. Although traditional machine learning algorithms can be very complex, their core is still very simple. Their training requires a lot of domain expertise (which is expensive), manual intervention is require when errors occur. The finally, machine learning is only good at train tasks.
On the other hand, deep learning algorithms understand the current task through a network of neurons, and these neurons map the task to a conceptual hierarchy. All these algorithms can be done by themselves. In the context of computer vision technology, this means first identifying light and dark areas, then classifying lines, then performing shape recognition, and then performing comprehensive image recognition.
When you provide more data to deep learning algorithms. Their performance will be better, which is not a typical feature of machine learning algorithms. This is great news for computer vision. Not only does it allow more pictures and videos to be used for deep learning algorithm training, but it also eases a lot of work related to annotating and labeling data.
A gourmet candy retailer called Lolli & Pops uses facial recognition technology to identify shoppers who often visit the store. As a result, store employees can personalize the shopping experience by presenting personalized product recommendations and occasional loyalty discounts. Special treatment can instill brand loyalty and transform occasional shoppers into ordinary shoppers. Both are good for business.
Computer vision technology in rise of edge computing
Machines connected to the Internet and the cloud can learn from the data collected throughout the network and adjust accordingly to optimize system performance. However, connection to the Internet and the cloud is not always guarantee.
This technologies attached to physical machines, such as gas turbines, jet engines, or MRI scanners. It allows processing and analysis where the data is collected, rather than processing in the cloud or data center. Edge computing cannot replace the cloud. It only allows the machine to act independently on new data insights when needed. In other words, machines at the edge can learn and adjust based on their own experience, and have nothing to do with large networks.
Now, devices can be place in areas with poor or non-existent network connections without affecting the analysis results. In addition, edge computing can offset the use and maintenance costs of cloud computing for data sharing. For computer vision software, this means better responses can be made in real time, and only relevant insights can be moved to the cloud for further analysis. This feature is particularly useful for self-driving cars.
In order to operate safely, the vehicle will need to collect and analyze a large amount of data related to its surrounding environment, direction and weather conditions, all without delay. Relying on cloud solutions to analyze data can be dangerous because delays can lead to accidents.
Point cloud object recognition
Point cloud is a more commonly use technique in object recognition and object tracking recently. This technique is usually use within a space (such as a room or container). Where the position and shape of each object is represente by a list of coordinates (X, Y, and Z). The list of coordinates is know as a “point cloud”.
This technology can accurately represent the position of an object in space, and can accurately track any movement. The application of point cloud is endless. The following are just some industry examples and the benefits of these technologies:
1) Documents: asset monitoring, construction site tracking, malicious damage detection.
2) Classification: urban planning, review tools for easy analysis, necessary public utility work diagrams
3) Change detection: asset management, cargo tracking, natural disaster management.
4) Predictive maintenance: Continuous monitoring of assets and infrastructure to predict when repairs are need.
Fusion Reality: Enhanced VR and AR
Nowadays, any VR or AR system can create an immersive 3D environment, but it has almost nothing to do with the user’s real environment. Most AR devices can perform simple scanning of the environment (for example, Google ARCore can detect flat surfaces and changes in light conditions), and VR systems can detect the user’s movement through head tracking, controllers, etc., but their capabilities It doesn’t stop there.
Computer vision software is advancing VR and AR to the next stage of development called merged reality (MR).
With the help of external cameras and sensors that can map the environment, as well as eye tracking solutions and gyroscopes to locate users, VR and AR systems can:
Provide guidance and instructions in indoor environments, public places, underground and other places
Hardware store Lowe’s store is already using this product. Every shopper can borrow an AR device, list a shopping list on it and get the route of each item in the store. The AR device uses floor plans, inventory information, and environmental maps in real time to provide accurate directions.
Semantic instance segmentation
In order to understand what semantic instance segmentation is, we first divide this concept into two parts: semantic segmentation and instance segmentation.
Instance segmentation recognizes object contours at the pixel level, while semantic segmentation only groups pixels into specific object groups. Let us use balloon images to illustrate how these two technologies compare to others.
If put together, semantic and instance segmentation methods will become a powerful tool. This tool can not only detect all the pixels belonging to the object in the picture, but also determine which pixels belong to which object and the position of the object in the picture.
Land mapping through satellite imagery may be useful for government agencies to monitor deforestation (especially illegal deforestation), urbanization, and transportation.
Many architect firms also use such data for urban planning and architectural development. Some people even go a step further and use it in combination with AR devices to understand how its design looks in real life.
Computer vision technology Extended reading:
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