OpenCV is a native cross-platform C library for Computer Vision, Machine Learning, and image processing. It is increasingly being adopted for development in Python. This course features some trending applications of vision and deep learning and will help you master these techniques. You will learn how to retrieve structure from motion (sfm) and you will also see how we can build an application to capture 2D images and join them dynamically to achieve street views by capturing camera projection angles and relative image positions. You will also learn how to track your head in 3D in real-time, and perform facial recognition against a goldenset.
The Image Recognition Technology Is, Usually, Associated with an Array of Security and Surveillance-Related Uses and the Rapidly Developing Autonomous Vehicle Niche. Can Image Recognition Apps Help Businesses in Other Verticals? With Reuters' predictions for the not-so-far-off year of 2022 being in the region of a hefty $43-57 billion, Image Recognition is one big lure for AI outfits, and, simultaneously, a lot of hope for businesses and organizations that depend upon it for their survival and success. These include entities as diverse, as manufacturers of autonomous cars and security systems, national nature parks, border security forces, and companies that produce drones. Be it monitoring the state of a much cherished rainforest or sending drones to remote oil rigs to check if all one's assets are in one piece, almost all of the widely known uses of Image Recognition seem to be related to security and surveillance.
The image recognition technology used in today's autonomous cars and aerial drones as well as tomorrow's cancer-seeking robotic medical devices, all depend on artificial intelligence. These "computers that see" teach themselves to recognize objects -- a dog, a pedestrian crossing the street, a stopped car or a cancer tumor. Now, researchers at Stanford University have devised a new type of camera system that can classify images faster and more energy efficiently, and that could one day be built small enough to be embedded in the devices themselves, something that is not possible today. "That autonomous car you just passed has a relatively huge, relatively slow, energy intensive computer in its trunk," says Gordon Wetzstein, an assistant professor of electrical engineering and (by courtesy) computer science at Stanford, who directed the research. Wetzstein and Julie Chang, a doctoral candidate in his lab and first author on the paper, have married two types of computers into one -- creating a hybrid optical-electrical computer designed specifically for image analysis.
Dozens of companies who fancy their technologies as a great fit for the automotive market are on the hunt for alliances. For example, Kyocera and FotoNation announced Tuesday (May 17) a partnership agreement to develop intelligent automotive camera technology -- deemed critical in the coming era of semi- and fully autonomous cars. Kyocera has already dabbled in the auto sector with its rearview camera modules. FotoNation, with the lion's share of computational imaging solutions for mobile phones, entered the driver monitoring system market last year. "The two companies' interests are aligned," Sumat Mehra, senior vice president of marketing and business development at FotoNation, told EE Times, "to expand each company's presence in the automotive market" -- well beyond what they offer today.
Or carmakers could utilise the technology to make autonomous cars safer and more reactive to their immediate environment. Cremers' trailblazing research into mathematical image pro-cessing and pattern recognition earned him the 2016 Gottfried Wilhelm Leibniz Prize – Germany's most esteemed award in the sciences. His question: How can we use a camera to capture and "recover" the 3D world and reconstruct it in real time? It might lie in something called "Direct Image Alignment," which is a core component of his current research into realising the 3D world in images – faster, with greater accuracy and with more robustness.