Superpixel segmentation and depth feature locating-based salient target detection method

A technology of superpixel segmentation and depth features, which is applied in the field of salient target detection based on superpixel segmentation and depth feature positioning, can solve the problem of unsatisfactory target shape extraction, and achieve complete background removal, strong robustness, and logic Effect

Active Publication Date: 2017-09-15
XIDIAN UNIV
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  • Abstract
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  • Application Information

AI Technical Summary

Problems solved by technology

Although these methods have achieved certain effectiveness in detecting salient targets, the detection effect is not satisfactory in terms of edge segmentation, background removal, an

Method used

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  • Superpixel segmentation and depth feature locating-based salient target detection method
  • Superpixel segmentation and depth feature locating-based salient target detection method
  • Superpixel segmentation and depth feature locating-based salient target detection method

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Embodiment 1

[0024] Some of the existing salient target detection methods only consider the characteristics of the image itself to find the difference between the target area and the background area of ​​the image, so as to distinguish the target position and the background area. In addition, the Markov chain is used to process the saliency map, and the mutual influence relationship between the central saliency area and the surrounding background area is found. There is also a method of using the convolution of the magnitude spectrum and the filter to realize the redundant information and finally find the salient target area. Although these methods are effective in detecting salient targets, the detection effect is not satisfactory in terms of edge segmentation, background removal, and target shape extraction, and has certain limitations.

[0025] Aiming at these defects of the prior art, after discussion and innovation, the present invention proposes a salient target detection method base...

Embodiment 2

[0036] The salient target detection method based on superpixel segmentation and depth feature location is the same as in embodiment 1, and the superpixel segmentation of the target image to be detected in step 1 of the present invention includes the following steps:

[0037] 1.1 First assume the target image, that is, the original Figure 1 There are N pixels in total, and the total area expected to be segmented is K. Obviously, there are N / K pixels in each divided area, and the distance between different areas is about It may happen that the set center point just appears on the edge. In order to avoid this situation, find the position with the minimum local gradient around the set center, and move the center position to the minimum local gradient. And set a label number in the same area as a mark.

[0038] 1.2 Calculate the Euclidean distance value of the five-dimensional feature vector from each pixel point to the determined center point of the surrounding neighborhood, an...

Embodiment 3

[0048] The salient target detection method based on superpixel segmentation and depth feature location is the same as that of Embodiment 1-2. In step 3 of the present invention, the three types of feature information, namely, nearest neighbor region information, global region information and corner background region information, are collected to fully consider Pay more attention to the center and ignore the surrounding background, the feature similarity of the target area, and the prior knowledge compared to the uniqueness of the global feature, including the following steps:

[0049] 3.1 Considering that the center is more likely to be salience than the surrounding background, saliency targets must be concentrated in a certain area. For each segmented area, the information in the nearest adjacent area is collected, that is, the nearest neighbor area information.

[0050] 3.2 Consider the degree of influence of the processing area on the entire image, by removing the informatio...

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Abstract

The invention provides a superpixel segmentation and depth feature locating-based salient target detection method and solves the problem of non-ideal target segmentation effect of a conventional salient target detection method. The method is implemented by comprising the steps that a processing unit of an image is upgraded to a collective similar region from an independent pixel point by utilizing superpixel segmentation based on linear iteration of color similarity; image features such as color features, direction features, depth features and the like are fully considered; in combination with a characteristic that human eyes pay more attention to a center and ignore a peripheral background, feature similarity of a region where a salient image is located and priori knowledge of uniqueness in comparison with global features, a locating salient map and a depth salient map of the input image are generated; and the salient maps are subjected to fusion and boundary processing. According to the method, the edge of the detected image is clearer in effect; background elimination is completer; and target morphological segmentation is completer. The method is used in the fields of human face identification, vehicle detection, moving target detection tracking, military guided missile detection, hospital pathological detection and the like.

Description

technical field [0001] The invention belongs to the technical field of image detection, and mainly relates to a salient target detection method, in particular to a salient target detection method based on superpixel segmentation and depth feature positioning. It is used in face recognition, vehicle detection, moving target detection and tracking, military missile detection, hospital pathological detection and other fields. Background technique [0002] As the amount of data continues to grow, the amount of data accumulated per unit of time increases exponentially. The huge amount of data requires better computer technology and algorithm theory to process and refine data information. With the emergence of high-resolution images, it brings great visual enjoyment. People's understanding of complex images has reached a very high level. Traditional image processing separates pixels, or completely analyzes the meaning of information transmitted by images. Faced with a huge amoun...

Claims

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Application Information

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IPC IPC(8): G06K9/32G06K9/46G06K9/48G06K9/62G06T7/11G06T7/90G06T7/529
CPCG06T7/40G06T2207/20021G06V10/24G06V10/44G06V10/478G06V10/473G06V10/751G06V2201/07
Inventor 肖嵩熊晓彤刘雨晴李磊王欣远杜建超
Owner XIDIAN UNIV
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