A method for the semantic segmentation of an image

A semantic segmentation, image technology, applied in image analysis, image enhancement, image communication and other directions, can solve problems such as the inapplicability of convolutional networks

Inactive Publication Date: 2018-11-02
DELPHI TECH INC
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Specifically, deep and complex convolutional networks are not suitable for embedded devices in autonomous vehicles

Method used

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  • A method for the semantic segmentation of an image
  • A method for the semantic segmentation of an image
  • A method for the semantic segmentation of an image

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Embodiment Construction

[0038] exist figure 1 In , a raw image 20 captured by a digital camera mounted to a motor vehicle is shown. Image 20 included in figure 1 A two-dimensional arrangement of individual pixels that are not visible in . In the original image 20 , various objects of interest such as a road 10 , a vehicle 11 , and a traffic sign 13 can be recognized. For autonomous driving applications and advanced driver assistance systems, computer-based understanding of the captured scene is required. One means for achieving this automatic scene understanding is semantic segmentation of images, where each pixel is labeled according to a semantic category such as "road", "non-road", "pedestrian", "traffic sign", etc. exist figure 2 , as the original image 20 ( figure 1 ) results of semantic segmentation, which exemplarily shows the processed image 21. Semantic segments 15 of the processed image 21 correspond to different categories and are displayed in different colors or gray scales.

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Abstract

The present invention provides a method for the semantic segmentation of an image. The method for the semantic segmentation of an image having a two-dimensional arrangement of pixels comprises the steps of: segmenting at least a part of the image into superpixels, determining image descriptors for the superpixels, wherein each image descriptor comprises a plurality of image features, feeding the image descriptors of the superpixels to a convolutional network and labeling the pixels of the image according to semantic categories by means of the convolutional network, wherein the superpixels areassigned to corresponding positions of a regular grid structure extending across the image and the image descriptors are fed to the convolutional network based on the assignment.

Description

technical field [0001] The invention relates to a method for semantically segmenting an image with a two-dimensional pixel arrangement. Background technique [0002] Automated scene understanding is an important goal in the modern field of computer vision. One way to achieve automatic scene understanding is semantic segmentation of images, where individual pixels of an image are labeled according to their semantic categories. This semantic segmentation of images is especially useful in the context of object detection in advanced driver assistance systems (ADAS). For example, semantic segmentation of an image may involve dividing pixels into regions that belong to roads and regions that do not. In this case, the semantic categories are "road" and "non-road". According to the present application, there may be more than two semantic categories, such as "pedestrian", "car", "traffic sign", etc. Correctly labeling pixels is a challenging task because the appearance of predefi...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/10G06K9/46G06K9/00G06N3/04G06V10/50G06V10/764
CPCG06T7/10G06V20/588G06V10/44G06N3/045G06T7/11G06T7/187G06T2207/20084G06V20/58G06V10/467G06V10/50G06V10/267G06V10/82G06V10/764G06T2207/20081G06V20/41G06V20/584G06F18/241H04N23/80
Inventor F·佐霍里安B·安蒂克J·西格蒙德M·莫伊特
Owner DELPHI TECH INC
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