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Improved visual attention model-based method of natural scene object detection

A technology of visual attention and target detection, which is applied in the field of target recognition and can solve the problems of low target detection accuracy

Inactive Publication Date: 2011-02-23
XIDIAN UNIV
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Problems solved by technology

However, these methods have the disadvantage of low target detection accuracy for different types of natural scene images.

Method used

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  • Improved visual attention model-based method of natural scene object detection
  • Improved visual attention model-based method of natural scene object detection
  • Improved visual attention model-based method of natural scene object detection

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Embodiment Construction

[0040] Reference figure 1 , The present invention is based on an improved visual attention model target detection method, including the following steps:

[0041] Step 1: Extract the feature saliency map of the original image through Itti's visual attention model.

[0042] 1.1) Using Itti's visual attention model to extract feature maps of the components of color C, brightness I, and direction O from the original image, and use the pyramid model to decompose each feature map at multiple scales to obtain decomposed images of different scales;

[0043] 1.2) For the decomposed images of different scales, merge the images between the scales to obtain the contrast feature map:

[0044] 1.3) Normalize the contrast feature map to obtain the saliency map of the brightness feature Saliency map of color features And directional feature saliency map

[0045] The second step is to extract the saliency map of the original image.

[0046] 2.1) Find the Fourier transform F[I] of the original image. I...

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Abstract

The invention discloses an improved visual attention model-based method of a natural scene object detection, which mainly solves the problems of low detection accuracy rate and high false detection rate in the conventional visual attention model-based object detection. The method comprises the following steps of: (1) inputting an image to be detected, and extracting feature saliency images of brightness, color and direction by using a visual attention model of Itti; (2) extracting a feature saliency image of a spectrum of an original image; (3) performing data sampling and marking on the feature saliency images of the brightness, the color, the direction and the spectrum and an attention image of an experimenter to form a final rough set information table; (4) constructing attribute significance according to the rough set information table, and obtaining the optimal weight value of the feature images by clustering ; and (5) weighing feature sub-images to obtain a saliency image of the original image, wherein a saliency area corresponding to the saliency image is a target position area. The method can more effectively detect a visual attention area in a natural scene and position objects in the visual attention area.

Description

Technical field [0001] The invention belongs to the technical field of image processing, relates to target recognition, and can be used for road sign detection, video monitoring, natural scene recognition and classification. Background technique [0002] Target detection is one of the most critical technologies in computer vision and pattern recognition systems. The effect of target detection directly affects the reliability and effectiveness of the entire system, which has been a hot research topic in recent years. With the development of technology, people increasingly find that the existing simple methods based on image processing and machine learning are not fully applicable to most images. Therefore, researchers have begun to pay attention to the human visual attention mechanism, studying how the human eye searches, finds and detects objects in natural scenes. [0003] The visual attention mechanism is an intrinsic property of the visual system of primates. It is a mechanism...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06K9/62
Inventor 高新波韩冰李洁邓成路文田春娜王秀梅王颖
Owner XIDIAN UNIV
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