Image multispectral analysis method used for video monitoring or machine vision field
A machine vision and video surveillance technology, applied in the fields of image processing, intelligent transportation, and video surveillance, can solve the problems of limited dynamic range, low target recognition, low contrast, etc., to achieve high detail visibility, low computational complexity, saving The effect of data bandwidth
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example 1
[0055] Example 1, backend display analysis application
[0056] According to a certain rule, select a scene with a certain dynamic range spectrum to perform spectrum expansion; and then perform spectrum expansion analysis one by one by moving the dynamic range window; so that the contrast of each spectrum image information is increased, and more details can be reproduced .
[0057] Image 6 It is a typical traffic imaging image. Because the actual lighting is very strong, the backlight is serious, and the contrast between light and dark in multi-target scenes is huge, it is impossible for ordinary sensors and display devices to reproduce a clear image of each target in intelligent traffic. The license plate, driver, vehicle and road surface in the picture are not well represented as a whole.
[0058] Figure 7 It is the image after multi-spectral analysis with the license plate as the target. Obviously, the license plate can be clearly reproduced, and other non-interesting ...
example 2
[0060] Example 2, sensor-side application in video acquisition
[0061] Traditional sensor image acquisition compresses the entire dynamic range into low-bit-number narrow dynamic range image data, which results in a narrow distribution of image brightness from dark to bright scenes, and there are useless spectral bands, which are finally reflected in image contrast Very weak, multi-object local details cannot be guaranteed. The video image multi-spectral analysis method provided by the present invention is used in video recording, and the scenes in the limited dynamic range are selectively captured during video recording, such as only dark areas or bright areas are captured, and they are expanded when recorded into data signals to the entire spectrum. The contrast reflected in this effect is very strong, which solves the common problem of missing or unclear details in different areas.
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