Article classification method based on depth recovery information

A technology for depth restoration and object classification, applied in neural learning methods, image analysis, biological neural network models, etc., can solve problems such as lack of geometric information, and achieve improved performance and good performance
CN108520535AActive Publication Date: 2018-09-11TIANJIN UNIV
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Patent Information

Authority / Receiving Office
CN · China
Current Assignee / Owner
TIANJIN UNIV
Publication Date
2018-09-11

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Abstract

The invention relates to the technical field of article classification and monocular image depth estimation in the field of computer vision, and proposes a model which enhances the classification performance through introduction of depth information and only needs an RGB image instead of a real depth image acquired by a sensor as input. The invention provides an article classification method basedon depth recovery information. The method includes following steps: (1) preprocessing a data set; (2) establishing a depth recovery model in the model; (3) training two image classification models which respectively receive RGB and depth images as input; (4) establishing a final fusion model, and performing training and tests; (5) migrating a trained fusion network in step 4 to a classified dataset of a natural image; and (6) comparing effects of image classification of the model on two disclosed data sets and performing visualization. The method is mainly applied to occasions of article classification and monocular image estimation in the field of computer vision.
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Description

technical field

[0001] The invention relates to the technical field of object classification and monocular image depth estimation in the field of computer vision, in particular to a depth estimation method based on a generative confrontation network. Background technique

[0002] Image object classification is a fundamental problem in computer vision research, and it is also the basis for other high-level vision tasks such as image segmentation, object tracking, and behavior analysis. Since the color RGB image is a two-dimensional projection of the real three-dimensional world, a flat image may correspond to countless actual scenes in the real world. Therefore, the depth information is inevitably lost. Depth information can reflect geometric information that 2D images do not have, and is of great significance for 3D scene reconstruction, gesture recognition, human body pose estimation, etc. [1] . The 2D information represented by the RGB image and the depth information re...

Claims

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