Depth order reasoning method based on occlusion feature learning

A technology of depth order and reasoning method, which is applied in the direction of image data processing, instrumentation, calculation, etc., can solve the problem that the effect of depth order reasoning is not very good, and achieve the effect of improving efficiency and reducing search space

Inactive Publication Date: 2016-11-23
WUXI BUPT SENSING TECH & IND ACADEMY
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Depth order inference of some existing monocular images, because of the lack ...

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  • Depth order reasoning method based on occlusion feature learning
  • Depth order reasoning method based on occlusion feature learning
  • Depth order reasoning method based on occlusion feature learning

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[0026] The present invention will be further described below in conjunction with the accompanying drawings.

[0027] as attached figure 1 Shown: A deep sequential inference method based on occlusion feature learning, including the following steps:

[0028] (1) Obtain the over-segmented image: Firstly, the soft boundary map of the original image is obtained through the Berkeley algorithm, and then a small threshold is used to binarize the soft boundary map to obtain the over-segmented image.

[0029] (2) Extract the over-segmented edge: first calculate all the connection points of three forks, every two connection points form an edge, and a region is formed between multiple edges; then, record all connection points and the distance formed by each pair of connection points Edges, details of the regions on both sides of each edge, and finally, the over-segmented image is divided into three components of connection points, edges and regions, and they are connected to each other. ...

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Abstract

The present invention provides a deep order reasoning method based on occlusion feature learning, comprising the following steps: obtaining an over-segmented image through the Berkeley algorithm, using an occlusion edge classifier to extract over-segmented edges from common edges; after color model processing, Extract occlusion edge features; select sparse-based classifiers, train and determine occlusion edges; remove false edges by fusing same-layer regions, and avoid misjudgment problems in the process of occlusion edge classifiers; adopt a new triple description operator to realize semi-local depth order reasoning; finally, through modeling, describe the local depth order as a partial order relationship, and use the relevant knowledge of graph theory to transform the global depth order reasoning into a kind of The problem of effective path, so as to complete the depth order reasoning of the whole image.

Description

technical field [0001] The invention relates to the research and realization of a deep sequence reasoning method based on occlusion feature learning. Background technique [0002] The depth order reasoning of monocular images was originally proposed by N.Mark and D.Mumford in 1990. The depth order reasoning of monocular images includes two processes: the process of segmenting the image into non-overlapping regions and the process of sorting the segmented regions according to the occlusion relationship. Regions are the parts between edges. Therefore, the process of dividing an image into non-overlapping regions can be transformed into a process of detecting edges, especially occluded edges. P.Arbelaez and J.Malik proposed the method of edge detection. However, the method they use is only applicable to hierarchical probability margin maps, and noisy margins can seriously affect the results of depth order inference. In contrast, the method based on occlusion relationship ed...

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Application Information

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IPC IPC(8): G06T7/00
Inventor 明安龙宋桂岭
Owner WUXI BUPT SENSING TECH & IND ACADEMY
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