Method of automatic exposure

Abstract

The invention discloses a method of automatic exposure, designing an image adjusting method. The invention is invented for solving the problem of poor exposure effect of the prior art due to inaccurate object cutting. The invention comprises the following technical schemes: the method of automatic exposure is suitable for processing images, wherein, at least a second image data is generated before a first image data. The method comprises the following steps: inputting the first image data; performing an object detecting program so as to obtain at least one foreground object and a target background; judging whether the area of the foreground object is more than a critical value or not; and determining exposure amount according to the foreground object, the target background and the critical value.

Description

technical field [0001] The invention relates to an image adjustment method, in particular to a method for automatic exposure by detecting foreground objects. Background technique [0002] In the technology of image adjustment, the purpose of exposure control is that when the brightness of the shooting environment changes, the system controls the aperture or shutter to maintain the appropriate exposure of the captured image, so as not to be too bright or too dark. Automatic exposure control means that the system detects changes in screen brightness at any time to control exposure. Such as figure 1 As shown, the existing automatic exposure technology performs brightness analysis on the entire frame after capturing the frame. Then, according to the result of the aforementioned brightness analysis, the existing automatic exposure technology changes the exposure time to adjust the exposure amount. [0003] However, existing auto-exposure techniques suffer from a number of seri...

AI Technical Summary

Problems solved by technology

[0010] As a result, the accuracy of existing object segmentation algorithms has not yet reached a satisfactory level, so there are many limitations in application, such as:

[0011] 1. When the object is quite close to the background color feature, the existing object cutting algorithm is not easy to cut accurately

[0012] 2. The existing object cutting algorithm is prone to the phenomenon that the object is disconnected due to inadvertent cutting (such as: a part of the body is similar to the background color), and then a single object is judged as two objects

[0013] 3. When there are light reflections and shadow changes in the picture, the existing object cutting algorithm is not easy to cut accurately, but it is easy to cut out the light and shadow changes as new foreground objects, which increases the number of false alarms

When the object learning rate is slow, if the background changes, the background model cannot be updated in real time

These effects will cause the failure of the object segmentation algorithm

[0015] In summary, the existing object segmentation algorithm not only has many limitations, but also the existing object segmentation algorithm has many serious shortcomings, which makes the image processing process produce many flaws

Most of these shortcomings are caused by the fact that the existing object segmentation algorithms all use pixels as the starting point. The object information such as the sudden appearance of the object is resolved

Therefore, existing object segmentation algorithms need to be improved

Images