Detection algorithm for IR ship target in complex background of sea and sky

It is an important and challenging problem to detect small targets in clutter scene and low SNR (Signal Noise Ratio) in infrared (IR) images. In order to solve this problem, a method based on feature salience is proposed for automatic detection of targets in complex background. Firstly, in this paper, the method utilizes the average absolute difference maximum (AADM) as the dissimilarity measurement between targets and background region to enhance targets. Secondly, minimum probability of error was used to build the model of feature salience. Finally, by computing the realistic degree of features, this method solves the problem of multi-feather fusion. Experimental results show that the algorithm proposed shows better performance with respect to the probability of detection. It is an effective and valuable small target detection algorithm under a complex background.

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Research on Fast Face Detection Algorithm in Complex Background

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1030-1032.1779 ◽

2014 ◽

Vol 1030-1032 ◽

pp. 1779-1782

Author(s):

Xin Wang ◽

He Pan

Keyword(s):

Prior Knowledge ◽

Face Detection ◽

Fast Algorithm ◽

Vertical Direction ◽

Detection Algorithm ◽

Color Information ◽

Human Eye ◽

Face Region ◽

Complex Background ◽

The Face

This paper presents a fast algorithm for face detection in complex background, in which image color information is used first, project upper part of the partition of face in the gray image to horizontal and vertical direction. Determine the eyes positions by the minimum value, and scope the human eye in the face of prior knowledge to judge and adjust the face region.

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Based on the Adaboost Algorithm of Face Detection Research

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.635-637.985 ◽

2014 ◽

Vol 635-637 ◽

pp. 985-988

Author(s):

Wei Bo Yu ◽

Lin Zhao ◽

Wei Ming He

Keyword(s):

Face Detection ◽

Recognition System ◽

Detection Algorithm ◽

Detection Accuracy ◽

Adaboost Algorithm ◽

Complex Background ◽

Complex Image ◽

Face Recognition System ◽

Lower Accuracy ◽

The Face

Because of the influence of complex image background, illumination changes, facial rotation and some other factors, makes face detection in complex background is much more difficult, lower accuracy and slower speed. Adaboost algorithm was used for face detection, and implemented the test process in OpenCV. Face detection experiments were performed on images with facial rotation and complex background, the detection accuracy rate was 85% and 99% respectively, the average detection time of each picture was 16.67ms and 76ms.Experimental results show that the face detection algorithm can accurately and quickly realize face detection in complex background, and can satisfy the requirements of real-time face recognition system.

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