Target recognition method based on compressed sensing

Abstract

The invention discloses a target recognition method based on compressed sensing. The method includes the steps of acquiring standard sample maps of at least two types of targets; obtaining characteristic atoms of the types of targets using a characteristic atomic extraction method; arranging each characteristic atom of each type of target diagonally into a dictionary [psi]p of each type of target, and arranging the dictionaries of the types of targets in parallel into a comprehensive dictionary [psi]; performing compressed sampling on an original image x to be recognized by the by using a measurement matrix [phi] to obtain a compressed sampled signal y; combining the comprehensive dictionary [psi], the measurement matrix [phi] and the sampled signal y, and calculating a sparse coefficient [theta] of the original image to be recognized through reconstruction; and processing the sparse coefficient [theta] to obtain a coefficient map, and performing classification and counting according to the number of rows and the size of the connected domain in the coefficient map, to achieve the recognition of the types of the targets in the original image. The target recognition method proposed in the invention can improve the speed of target recognition in the image and realize multi-target recognition.

Description

technical field [0001] The invention relates to the technical field of image recognition, in particular to a target recognition method based on compressed sensing. Background technique [0002] Target recognition in images is the process of using various program algorithms to distinguish specific targets in the image, and using the identified targets as the basis for the next step of processing. In today's information and network, it can be widely applied to many field. The human eye is often slow in recognizing a specific target, and long-term recognition and division of similar targets will also cause aesthetic fatigue and gradually produce a large number of false recognitions. Machine recognition is used instead of human eye recognition, and computer calculations are used to replace it. The amount of brain used by the human eye can increase speed and reduce energy consumption, which is very beneficial to the field of image recognition. For example, to recognize a thousan...

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Problems solved by technology

[0003] However, the current commonly used image target recognition technology generally has the disadvantages of long time consumption and slow speed. The reason is that the traditional image target recognition technology requires the following processes: image preprocessing, image segmentation, feature extraction, and feature recognition or matching; that is, sampling The obtained image information needs to be processed multiple times to identify the required target, and the existing image recognition separates the image acquisition process from the target recognition process, which is not conducive to the improvement of speed

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