RGB-D object identification method

An object recognition and object technology, applied in the fields of stereo vision and deep learning, can solve the problems of imperfect feature description and low recognition accuracy, and achieve the effect of solving the poor recognition effect.
CN107944459AInactive Publication Date: 2018-04-20TIANJIN UNIV
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Patent Information

Authority / Receiving Office
CN Β· China
Current Assignee / Owner
TIANJIN UNIV
Publication Date
2018-04-20
Estimated Expiration
Not applicable Β· inactive patent

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Abstract

The invention discloses an RGB-D object identification method which comprises the following steps of acquiring a gray scale image which is generated by a color image, and a surface normal vector thatis generated by a depth image, using the color image, the gray scale image, the depth image and the surface normal vector as multi-data mode information; respectively extracting high-layer characteristics in the color image, the gray scale image and the surface normal vector through a convolutional-recurrent neural network; extracting the high-layer characteristic of the depth image by means of aconvolutional-Fisher vector-recurrent neural network; and performing characteristic fusion on the plurality of high-layer characteristics, obtaining a total characteristic of the object, and inputtingthe total characteristic of the object into a characteristic classifier for realizing an object identification task. According to the RGB-D object identification method, a plurality of data modes arecombined; more accurate RGB-D object characteristics are extracted; and furthermore object identification accuracy is improved.
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Description

technical field

[0001] The invention relates to the technical fields of deep learning and stereo vision, in particular to an RGB-D object recognition method. Background technique

[0002] Object recognition is one of the key technical issues in the field of computer vision, which has important research value and broad application prospects. With the further development and application of sensing technology, the Kinect camera, which can simultaneously acquire color images and depth images, has gradually become a new generation of mainstream imaging devices. Usually, the color image can provide information such as the texture and color of the target, and the depth image can provide effective information such as depth and shape. The two kinds of information complement each other and further enhance the performance of various visual tasks. How to fully mine the depth information in RGB-D data, explore the relationship between depth and color data, and further improve the target...

Claims

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