Object prediction area optimization method applicable to target identification

A technology for predicting areas and target recognition, applied in character and pattern recognition, instruments, computer parts, etc., can solve problems such as high algorithm time complexity and increased probability of false detection of object areas

Active Publication Date: 2016-04-06
NORTHWESTERN POLYTECHNICAL UNIV
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Problems solved by technology

However, the method based on sliding window search mainly faces two problems in practical application: 1. Since the entire image needs to be searched and the classifier is applied in all possible positions, the time complexity of the algorithm is relatively high; 2. How to eff

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  • Object prediction area optimization method applicable to target identification
  • Object prediction area optimization method applicable to target identification
  • Object prediction area optimization method applicable to target identification

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Embodiment Construction

[0052] The present invention will be described in detail below in conjunction with the accompanying drawings. It should be noted that the described embodiments are only intended to facilitate the understanding of the present invention, and do not limit it in any way.

[0053] see figure 1 , the present invention is an object prediction area optimization method suitable for target recognition, which is mainly composed of five parts: object prediction area expansion, image segmentation, superpixel bounding box calculation, superpixel saliency evaluation and superpixel-based sliding window search.

[0054] The method specifically includes steps as follows:

[0055] 1. Object prediction area expansion:

[0056] Since the standard for the correct prediction of the object area is that the overlap between the predicted area and the real area of ​​the object exceeds 50% of their union, there must be different degrees of deviation between the correctly predicted area and the real area....

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Abstract

The invention discloses an object prediction area optimization method applicable to target identification, comprising the following steps: expanding an object prediction area; performing image segmentation; performing super pixel bounding box calculation and super pixel significance evaluation; and finally, getting an optimized object prediction area based on sliding window search of super pixels. By controlling the size of super pixels, the scale of image segmentation is increased, the search scope of super pixels is reduced, and therefore, the time complexity of the algorithm is low and is only related to the number of super pixels in an image; as the pixels in the super pixels are consistent and segmentation of the local edge is better, a better locating effect can be produced by taking super pixels as the basic elements of sliding window search compared with single pixels; the search area is reduced effectively, and the traditional pixel-based sliding window search algorithm is accelerated; and in addition, by cascading the method, the target identification accuracy of the existing target identification algorithm can be improved.

Description

【Technical field】 [0001] The invention belongs to the field of image processing and computer vision, and in particular relates to an object prediction region optimization method suitable for target recognition. 【Background technique】 [0002] Vision is an important way for human beings to obtain external information, and images are an important carrier of information. With the development of image processing technology, the size and resolution of images are gradually increasing, and the information contained in them is also constantly enriched. Studies have shown that when humans observe an image, their eyes usually move between the objects contained in the image, and they are not interested in other areas such as the background. Most visual technologies, such as pedestrian detection, face recognition, target tracking and target recognition, etc. , which also acts on the above-mentioned region containing the object. Therefore, how to quickly and effectively locate the posi...

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Application Information

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IPC IPC(8): G06K9/32
CPCG06V10/25G06V2201/07
Inventor 黄攀峰陈路蔡佳孟中杰张彬刘正雄
Owner NORTHWESTERN POLYTECHNICAL UNIV
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