Have you ever wondered how an iPhone instantly identifies your face and unlocks the device? Or better, how do the uber-cool Tik-Tok filters work? Today, there are numerous instances where smart devices seem to have farfetched applications, but in most cases, these applications are powered by a common tech, computer vision.
Advanced computing capabilities and recent developments in computer vision have propelled the domain far enough. Such is the advancements in the field that several agencies providing Computer vision as a Service have popped up worldwide.
Machine vision as a service refers to outsourcing third-party computer vision algorithms instead of developing and constantly upgrading in-house.
With that basic idea about computer vision as a service, here is a detailed approach to the entire scheme. This will also aid you better before rounding off to a service provider.
Computer vision is part of the umbrella term Artificial Intelligence and is neatly implemented using programming tools like Python and OpenCV. Here are the following steps in a computer application that works uninterruptedly in the making of computer-vision-enabled software.
The first step in functioning a computer vision algorithm is determining the acquisition strategy of the image datasets. Do you want your users to upload pictures, or is the camera self-serving? Is there a possibility of having both of these features simultaneously? The options are endless; hence, the software architecture must be designed to accommodate the best possibilities.
When it comes to a machine learning algorithm, there are numerous data points to be fed to the system. The labels instruct the machine about the job. Further, labeling the dataset makes it more search-friendly, with attributes like color, intensity, and size used for the same.
Now that you have labeled data, it is time to undergo a meticulous quality check by testing it against a training dataset. Here, the images undergo a series of automated processes that enhance the images. This involves adding or removing pixels, sorting misclassified data, or removing noise.
The images are further modified with various operations such as cropping, compressing, flipping horizontally or vertically, and blurring, among others. The following exercise trains the system for better image recognition capabilities. This model serves as the base of other processes, including but not limited to, testing and optimization, training models, and others.
Your model is now prepared to work autonomously with various visuals in form of images or videos. The system continues to improve when used regularly.
Most advanced computer vision service providers are well equipped with various solutions. The algorithms for each service are personalized for every client, which helps attain goals in record time. On the other hand, the hassles involved in the R&D of the algorithms are done by agency experts for better results.
Some of the common services include:
While vision analytics is still in its infancy, numerous companies worldwide have rolled out remarkable solutions. Computer vision and other advanced biosecurity solutions are the next waves in technology.
Here are some definite reasons why computer vision as a service should not be ignored at any cost.
When it comes to in-house setups, there are a ton of factors that prohibit companies from doing so. These are as follows:
While investing in an internal setup sounds good, an in-house setup can prove costly. Here are the two sides of the coin.
When working in-house, companies face a major problem: the lack of training data sets. In such scenarios, the company must generate a huge amount of data. However, the biggest concern is not deploying a team on the streets or forming partnerships. It all boils down to privacy and the use of the data.
While there are numerous instances where computer vision plays a vital role, here are a few examples that highlight the effect.
Imagine a situation where you walk in, pull out all the things you need, and walk out without bothering the cashier! No, this is not part of science fiction. Amazon is set out to build thousands of such structures powered by machine learning and machine vision. The store runs with minimal user interference, and with computer vision, it keeps track of every individual’s stock, maintenance, and billing.
The industry leader in innovation, Google is known for its audacious dreams. However, one such project that failed badly was Google Glass. Aimed to bring the power of computer vision to everyone, the device was way ahead of its time and hence succumbed to death. However, the company gracefully introduced most of its features in an app called Google Lens. Google Lens can solve complex mathematical problems, identify plants, and even translate languages in real-time, making it one of the most productive apps on the internet.
The leading electric car manufacturer Tesla is known for its self-driving feature. The car collects information about the roads and uses it meticulously to self-drive even in the worst working conditions and hence could not be ignored at all costs.
While there is a range of opportunities that could be done with computer vision, it is best to outsource solutions unless you work on a large scale. Companies with decades-old experts in the industry understand the requirement and help both the production and enhancement of these A.I.-powered solutions.