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Understanding the differences between biological and computer vision

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Since the early years of artificial intelligence, scientists have dreamed of creating computers that can "see" the world. As vision plays a key role in many things we do every day, cracking the code of computer vision seemed to be one of the major steps toward developing artificial general intelligence. But like many other goals in AI, computer vision has proven to be easier said than done. In 1966, scientists at MIT launched "The Summer Vision Project," a two-month effort to create a computer system that could identify objects and background areas in images. But it took much more than a summer break to achieve those goals. In fact, it wasn't until the early 2010s that image classifiers and object detectors were flexible and reliable enough to be used in mainstream applications.


Understanding the differences between biological and computer vision

#artificialintelligence

Welcome to AI book reviews, a series of posts that explore the latest literature on artificial intelligence. Since the early years of artificial intelligence, scientists have dreamed of creating computers that can "see" the world. As vision plays a key role in many things we do every day, cracking the code of computer vision seemed to be one of the major steps toward developing artificial general intelligence. But like many other goals in AI, computer vision has proven to be easier said than done. In 1966, scientists at MIT launched "The Summer Vision Project," a two-month effort to create a computer system that could identify objects and background areas in images.


Top 3 Use Cases for Deep Learning in Industrial Computer Vision

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Machine vision is commonly defined as the use of computer vision in the context of an industrial application, and the first use of machine vision for industrial purposes is often attributed to Electric Sorting Machine Company in the 1930s. They used a type of vacuum tube called a photomultiplier or PMT to sort food. Using this technology, machines could sort red apples from green and later recyclable glass bottles from ones with cracks. Much of the history of machine vision in the industrial sector has involved sorting one thing from another, the good from the bad. As camera technologies have improved, machine vision has been deployed for ever more precise quality control use cases, especially ones that involve parts that would be too small or hazardous for human inspectors.


An Introduction to Computer Vision

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Computer vision is a branch of AI that focuses on extracting useful information from pictures and videos. If AI allows computers to think, computer vision allows them to watch, learn and comprehend. Computer vision is architected in a similar way to human vision. Humans learn by seeing things repeatedly and identifying patterns. Our brains are comprised of an incredibly large network of interconnected neurons that mysteriously store and process information.


Introducing Caer -- Modern Computer Vision on the Fly

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By offering powerful image and video processing algorithms, Caer provides both casual and advanced users with an elegant interface for Machine vision operations. It leverages the power of libraries like OpenCV and Pillow to speed up your Computer Vision workflow -- making it ideal if you want to quickly test out something. This design philosophy makes Caer ideal for students, researchers, hobbyists and even experts in the fields of Deep Learning and Computer Vision to quickly prototype deep learning models or research ideas. Caer is an alternate Computer Vision library in Python that's designed to help speed up your Computer Vision workflow. It's ideal for rapid prototyping so you can focus more on the experimenting rather than the building. I use this package every single day when working on image and video processing workflows and it saves me tons of time!