Image processing method, system and device, storage medium and blackboard device

Abstract

The invention discloses an image processing method, system and device, a storage medium and a blackboard device. The method comprises the following steps of: threshold segmentation processing of pixelpoints is carried out on the first blackboard image; and performing threshold segmentation processing of at least one image block on the first blackboard image, then judging that the number of timesthat pixel points of the first blackboard image are divided into first type pixel points is within a first threshold range, taking the pixel points as target pixel points, and extracting a target image from the first blackboard image by utilizing the target pixel points. The blackboard device comprises a blackboard, a camera used for shooting the blackboard and a processor used for loading a program to execute the image processing method. By using the image processing scheme provided by the embodiment of the invention, the problem of image reflection can be effectively solved, and the accuracyof extracting the target image from the blackboard image is greatly improved. The image processing method, system and device, the storage medium and the blackboard device can be widely applied to thefield of image processing.

Description

technical field [0001] The invention relates to image processing technology, in particular to an image processing method, system, device, storage medium and blackboard device. Background technique [0002] Image threshold segmentation algorithm is a commonly used image segmentation method. Because of its simple implementation, small amount of calculation and stable performance, it has become the most basic and widely used segmentation technology in image segmentation. It is widely used in the extraction of target objects from images. program. At present, commonly used threshold segmentation algorithms mainly include two ideas, one is the global threshold segmentation algorithm, and the other is the local threshold segmentation algorithm. Wherein, for the global threshold segmentation algorithm, it mainly uses different metrics to find the optimal global threshold, thereby using the optimal global threshold to segment the image, but for the target scene where there are multi...

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Problems solved by technology

Wherein, for the global threshold segmentation algorithm, it mainly uses different metrics to find the optimal global threshold, so as to use the optimal global threshold to segment the image, but for the target scene where there are multiple peaks and troughs It is basically unable to adapt, that is to say, if there are many peaks and valleys in the image, the global threshold segmentation algorithm cannot segment the foreground and background well, so it can be seen that for images with reflections, using The global threshold segmentation algorithm cannot extract the target image from the image very well, and the accuracy of target image extraction is low

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