A 2❝ sketch representation where surfaces, information about depth and discontinuities on an image are pieced together.A Primal Sketch of an image, where edges, bars, boundaries etc., are represented (this is clearly inspired by Hubel and Wiesel’s research).He introduced a framework for vision where low-level algorithms that detect edges, curves, corners, etc., are used as stepping stones towards a high-level understanding of visual data.ĭavid Marr’s representational framework for vision includes: The vision system’s main function, he argued, is to create 3D representations of the environment so we can interact with it. In 1982, David Marr, a British neuroscientist, published another influential paper - “ Vision: A computational investigation into the human representation and processing of visual information ”.īuilding on the ideas of Hubel and Wiesel (who discovered that vision processing doesn’t start with holistic objects), David gave us the next important insight: He established that vision is hierarchical.
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However, that project was, according to many, the official birth of CV as a scientific field. Fifty years later, we’re still nowhere near solving computer vision. The students, coordinated by Seymour himself and Gerald Sussman, were to engineer a platform that could perform, automatically, background/foreground segmentation and extract non-overlapping objects from real-world images. He was of the opinion that a small group of MIT students had it in them to develop a significant part of a visual system in one summer. This was the period when Seymour Papert, a professor at MIT’s AI lab, decided to launch the Summer Vision Project and solve, in a few months, the machine vision problem. The 1960s was when AI became an academic discipline and some of the researchers, extremely optimistic about the field’s future, believed it would take no longer than 25 years to create a computer as intelligent as a human being. Instead, he went on to join DARPA and is now known as one of the inventors of the Internet. It should be noted that Lawrence didn’t stay in Computer Vision for long. Larry wrote that the processes of 2D to 3D construction, followed by 3D to 2D display, were a good starting point for future research of computer-aided 3D systems. The goal of the program he developed and described in the paper was to process 2D photographs into line drawings, then build up 3D representations from those lines and, finally, display 3D structures of objects with all the hidden lines removed. This was one lucky accident! After some initial confusion, Hubel and Wiesel realized that what got the neuron excited was the movement of the line created by the shadow of the sharp edge of the glass slide. However, a few months into the research, they noticed, rather accidentally, that one neuron fired as they were slipping a new slide into the projector. Their first efforts were fruitless they couldn’t get the nerve cells to respond to anything. They placed electrodes into the primary visual cortex area of an anesthetized cat’s brain and observed, or at least tried to, the neuronal activity in that region while showing the animal various images.
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The duo ran some pretty elaborate experiments. Their publication, entitled “ Receptive fields of single neurons in the cat’s striate cortex ”, described core response properties of visual cortical neurons as well how a cat’s visual experience shapes its cortical architecture. One of the most influential papers in Computer Vision was published by two neurophysiologists - David Hubel and Torsten Wiesel - in 1959.
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I’ll start with a work that came out in the late 1950s and has nothing to do with software engineering or software testing. In this article, I’ll try to shed some light on how modern CV systems, powered primarily by convolutional neural networks, came to be. Although Computer Vision (CV) has only exploded recently (the breakthrough moment happened in 2012 when AlexNet won ImageNet), it certainly isn’t a new scientific field.Ĭomputer scientists around the world have been trying to find ways to make machines extract meaning from visual data for about 60 years now, and the history of Computer Vision, which most people don’t know much about, is deeply fascinating.