Intelligent segmentation method integrating target detection and image segmentation

Abstract

The invention discloses an intelligent segmentation method integrating target detection and image segmentation. According to the method, a target is extracted by utilizing a high-dimension feature extracted by a neutral network; and a deep learning-based target detection network Faster-RCNN has high efficiency and can quickly and accurately identify a target region, so that a target region of interest can be extracted firstly by utilizing the characteristic and then the region is subjected to target segmentation pointedly. The method can automatically detect and extract the region of interest, reduce occupation of calculation resources of a GPU, and quickly and accurately perform segmentation to obtain the target; and under the condition of a huge image quantity scale, the method has efficient implementation performance, so that the manual interaction process can be reduced.

Description

technical field

[0001] The invention relates to computer graphics and image processing technology, especially for the intelligent segmentation of target object sequence images in three-dimensional reconstruction, specifically an intelligent segmentation method integrating target detection and image segmentation. Background technique

[0002] In 3D reconstruction based on image sequences, the accuracy and speed of object segmentation play a decisive role in the quality and efficiency of reconstruction. Image segmentation is widely used in military, remote sensing, meteorology, medicine and other fields, and it is a difficult point in image processing. Due to the large number of images, the extremely complex and changeable surrounding environment, and the variety of object materials, it is impossible to use a single method for batch automatic segmentation. Currently, Photoshop software is used for target segmentation based on image sequences, and the process of manual particip...

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