This presentation by Michael Liou was part of the Open Systems Media AI Day, Deploying Vision Systems with AI Capabilities. Because of the proliferation of edge devices, computer vision is one of AI’s killer apps in the form of edge AI.
Detecting unauthorized personnel is a crucial task for any business that needs to protect the safety of their employees and clients, or that stores valuable assets or data on-premises. Human security guards certainly have their uses, but they aren’t without faults, either: they aren’t available around the clock, they can only be present in a single location, and they’re vulnerable to human error (just like the rest of us).
Edge detection is an extremely popular task in fields such as computer vision and image processing. It’s not hard to see why: as humans, we depend on edge detection for tasks such as depth perception and detecting objects in our field of view.
Far from making edge AI less critical, 5G can actually complement edge AI, with both technologies working in concert to digitally transform your business.
An application programming interface (API) is a set of functions and definitions that enables two different software applications or systems to communicate with each other. Without APIs, developers would need to write custom integrations and scripts in order for these systems to exchange information—a time-consuming and technically challenging process. Chooch’s computer vision API helps users get fast, highly accurate identifications of the objects and concepts in their visual content. Given an image or video, the Chooch computer vision API will return the requested output (e.g. the items or faces in an image), as well as the relevant pixel coordinates.
Görsel yapay zeka artık hayatımızda. Turkcell CEO’su Murat Erkan ve Chooch AI CEO’su Emrah Gültekin ile gerçekleşen sohbetin video kaydını izleyebilirsiniz. Hakan Erdemli (Chooch AI) moderatörlüğündeki bu söyleşide, yapay zeka ve bilgisayarlı görü ile elde edilecek hızlı ve anlık iş değerini keşfedin.
You might know you want to bring computer vision into your organization—but do you know exactly which computer vision use case would offer the most benefit? Image segmentation is a subfield of computer vision that seeks to divide an image into contiguous parts by associating each pixel with a certain category, such as the background or a foreground object.
Computer vision, and subfields of computer vision such as object detection and object recognition, can certainly be classified under machine learning, depending on how you build the computer vision model. IBM defines machine learning as“a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.”
Knowing how to assess the performance of a pattern recognition model is highly important for a wide variety of tasks in artificial intelligence, machine learning, and computer vision. Below, I’ll discuss some of the most widely used criteria for good pattern recognition systems.
The potential applications of computer vision are nearly limitless: from counting the number of customers in a store to diagnosing complex medical issues. Practically any use case that involves visual data (i.e. images and videos) is suitable for computer vision, with state-of-the-art models often matching or exceeding human performance.