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"Microscopic water discrimination" may be the goal of machine vision for the future
The author:SIPO  Time:2018-04-02  Read: 151

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In the 520 cups, there are no more than 450ML of clear water in each cup. One of the guests is selected by the guests. After being observed by the challenger Wang Xi, they are put back in their place. The picture shows the observation and recording of the target cup shape by Wang Hao at the identification stand. In Jiangsu Satellite TV’s “Most Powerful Brain”, the audience witnessed an astonishing scene: There was a “ghost” Wang Hao, who was able to use the naked eye in the same batch of 520 cups of the same amount. In the clear water, after observing without touching, one of the guests randomly selected one of the cups of water. Surprisingly, the ghost used a scanner-like eye to quickly swipe a glass of water without any difference. At the fastest, he scanned 4-5 glasses of water.

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After “scanning observations” of some glasses (not all 520 glasses of water), Wang Xi successfully locked the target cup and identified “Water Brothers.” Wang Xi had exposed the magical principles of his secret technique on other TV programs. : Contains all the information including air bubbles, dust, and residual (albeit minor) fingerprints in the cups, bringing them together into the brain and generating images in the brain, such as a parrot or a child's face. In this way he is equivalent to holding a photo to find a human face. In his head, the "long" gap between two glasses of water is even greater than the difference between two faces.

The working "mechanism" of this magical technique is very similar to the machine vision system. The machine vision inspection system uses CCD and CMOS industrial cameras to convert the detected targets into image signals and send them to a dedicated image processing system. The signals are converted into digital signals based on pixel distribution, brightness, and color information. The image processing system processes these signals. Various operations are performed to extract the characteristics of the target, such as area, number, position, and length, and then output the result according to the preset allowable degree and other conditions, including size, angle, number, pass/fail, yes/no, etc. Implement automatic identification.

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Recognition, Detection, Positioning, and Recording—Automated image processing is now in many areas of industrial production, and machine vision is also well known. In fact, machine vision is not only an industrial camera, but also the overall vision solution including network components, triggering devices and image processing software, namely, image collection, image analysis, and image transmission.

Of course, the good work of the machines in the industrial field lies in testing, that is, comparing each product target with a given standard, and eliminating target objects that are not in compliance with the standards. Since the machine vision system at the present stage can only be photographed one by one and transmitted one by one to the analysis equipment, it cannot directly lock, and it is impossible to have what humans have, and reconstruct the abstract messy information into the imagination of the screen. If the rules of "microscopic water discrimination" come to the battle of man-machine wars, Wang Hao will win no doubt (it should be able to report "a revenge for a stone" for Li Shishi).

Today, the most bovine computers have been able to do a variety of things in terms of visual identity, from identifying cats on the web to identifying specific faces in many photographs. However, while basic image classification has become easier, when it comes to extracting meaning and information from abstract scenes, robots still face a series of new problems, and this type of technology clearly has a long way to go go.

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