What Happens When a Computer Can Perceive the World as Human’s do? Enter Computer Vision
iPhone X’s Face ID was greeted with a lot of hype when it was announced. The facial recognition technology has the capabilities to differentiate the owner of the device from others just by scanning the face. In the same year, Google Pixel had an inbuilt app called Google Lens, which could scan images and return […]READ MORE >>
iPhone X’s Face ID was greeted with a lot of hype when it was announced. The facial recognition technology has the capabilities to differentiate the owner of the device from others just by scanning the face. In the same year, Google Pixel had an inbuilt app called Google Lens, which could scan images and return relevant and accurate results. So how exactly do computers know what it’s looking at? Much of this can be attributed to computer vision, which utilizes neural networks, machine learning, and image recognition to make accurate human-like decisions regarding images. However, knowing what the picture is can be impressive, but what significance does it offer in changing the technological landscape?
Applications of computer vision and machine learning algorithms
It may be possible to direct the self-driving cars to drive in a particular lane or apply brakes when an obstacle appears in their proximity. But one major challenge for autonomous vehicles is its ability to identify road signs, lane markings, traffic signals, and hand signals. Advanced computer vision and machine learning algorithms can quickly detect whether the obstacle nearby is a boy crossing the road or just some object which can be circumvented. Additionally, it can also understand fellow drivers’ hand signals and make necessary adjustments.
Computer vision enables users to understand the content of the image by using powerful artificial intelligence tools, which can quickly categorize images across thousands of categories. These image searches are so powerful that it can list out all the objects within a single picture and also identify objects shot through multiple angles. Image search can help companies identify explicit or offensive content, so they can block them before they create a nuisance. Google Cloud Vision API and Microsoft Azure Computer Vision API are two of the most significant players in this area.
Today, the recommended products section is primarily driven by simple algorithms, which classify product type and display other goods that are from the same category. However, with advancements in computer vision and machine learning algorithms, retail companies can now show products that have a similar design, color, or style. Visual product discovery can recommend a large number of products and find similar goods in a separate category. For instance, a customer may like a sneaker which might have been placed in the sports shoe category. This way, artificial intelligence tools can enhance customer experience.
People may be impressed by how accurately the new iPhone X can detect faces. However, that is just the tip of the iceberg in terms of computer vision. Computers are far more capable than just recognizing faces. They can identify and decipher facial expressions and ascertain whether the person is feeling happy, sad, or perplexed. Such technologies are mostly used in content testing, where advertisers capture the reaction of the audience to their ads.
Understanding non-verbal cues, body language, and gestures is a crucial aspect of human behavior, and that is what sets it apart from machines. However, the line between humans and machine regarding this aspect is blurring. Advancements in computer vision and artificial intelligence means that computers can understand human gestures. Such technology is embraced by companies such as PointGrab, which offers home appliance with smart gesture recognition technology, allowing users to control the appliances by using hand gestures.
To know more about applications of computer vision and machine learning algorithms, artificial intelligence, and how such technology can enhance customer experience: