Multiple-scale high-resolution image object detection method based on continuity

A target detection and target technology, applied in the field of image processing, can solve the problems of high false alarm rate and time-consuming, and achieve the effect of reducing false detection, improving detection efficiency, and improving detection rate.

Active Publication Date: 2012-10-10
XIDIAN UNIV
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Problems solved by technology

However, for large-scale, high-resolution images, the algorithm has a high false alarm rate, and for large-s

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  • Multiple-scale high-resolution image object detection method based on continuity
  • Multiple-scale high-resolution image object detection method based on continuity
  • Multiple-scale high-resolution image object detection method based on continuity

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Embodiment Construction

[0040] refer to figure 1 , the specific implementation process of the present invention is as follows:

[0041] Step 1: Obtain positive and negative training sample sets, extract sample features from the positive sample set to construct a dictionary, and cluster the dictionary.

[0042] (1.1) The present invention selects as Figure 4 550 images containing objects and 500 images not containing objects with a size of 100×40 are shown as positive and negative training sample sets;

[0043] (1.2) Take 50 positive samples in the training sample set, apply Forstner operator to detect feature points, extract 13×13 pixel blocks around the feature points as positive sample features, and get 400 positive sample features in total; Convert a 13×13 positive sample feature into a 1×169 feature vector, use all positive sample features to form a 400×169 two-dimensional feature vector, and use the two-dimensional feature vector as a positive sample feature dictionary;

[0044] (1.3) Use the ...

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Abstract

The invention provides an object detection method based on continuity. In a current object detection technology, a false alarm rate is high and time is consumed. By using the method of the invention, the above problems can be solved. The method has the following steps: extracting sample characteristics based on forstner operator from 50 positive case training samples; constructing a dictionary through combing a space relation between sample characteristics and carrying out cluster on the dictionary; using a sparse neural network to train a classifier; inputting an image to be detected, carrying out mean filtering and binarization and extracting a region of interest based on an abrupt change; carrying out multi-scale zoom on the region of interest, sliding a 100*40 window on each layer, applying the classifier in a window calculating activity value and acquiring an active value distribution map; using a neighborhood inhibition and repeated part elimination method to search an object activity peak point; calculating the object continuity and obtaining a final object detection result according to the continuity. The method of the invention has the following advantages that: detection accuracy is high; the false alarm rate is low; timeliness is high. The method can be used for object detection and positioning of the large-scale high-resolution image.

Description

technical field [0001] The invention belongs to the technical field of image processing, relates to image segmentation and classification, and can be used for target detection and positioning in various large-scale and high-resolution complex background scenes in natural images. Background technique [0002] With the development of computer technology and the wide application of computer vision principles, the use of computer image processing technology to track targets in real time is becoming more and more popular. Target detection and positioning are in intelligent transportation systems, intelligent monitoring systems, military target detection and medical navigation. It has a wide range of application value in the positioning of surgical instruments during surgery. [0003] Target detection is an image segmentation based on the geometric and statistical characteristics of the target. It combines the segmentation and recognition of the target. Its accuracy and real-time ...

Claims

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Application Information

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IPC IPC(8): G06K9/62
Inventor 焦李成王爽高婷白静霍丽娜刘芳
Owner XIDIAN UNIV
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