Saliency Detection Algorithm under Complex Background

2015 ◽  
Vol 12 (2) ◽  
pp. 423-429
Author(s):  
Xin Pang
Keyword(s):  
Saliency Detection ◽  
Detection Algorithm ◽  
Complex Background

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

2009 ◽  
Author(s):  
Xiaoping Wang ◽  
Tianxu Zhang ◽  
Dengwei Wang ◽  
Wenjun Shi
Keyword(s):  
Detection Algorithm ◽  
Complex Background

A Novel Pavement Crack Detection Algorithm Using Interlaced Low-Rank Group Convolution Hybrid Deep Network Under a Complex Background

2020 ◽  
Vol 57 (14) ◽  
pp. 141031
Author(s):  
李刚 Li Gang ◽  
刘强伟 Liu Qiangwei ◽  
万健 Wan Jian ◽  
马彪 Ma Biao ◽  
李莹 Li Ying
Keyword(s):  
Crack Detection ◽  
Detection Algorithm ◽  
Low Rank ◽  
Deep Network ◽  
Complex Background ◽  
Pavement Crack Detection ◽  
Rank Group

scholarly journals Salient Region Detection by Fusing Foreground and Background Cues Extracted from Single Image

2016 ◽  
Vol 2016 ◽  
pp. 1-18 ◽  
Author(s):  
Qiangqiang Zhou ◽  
Weidong Zhao ◽  
Lin Zhang ◽  
Zhicheng Wang
Keyword(s):  
Saliency Detection ◽  
Computational Cost ◽  
Detection Algorithm ◽  
Saliency Map ◽  
Local Contrast ◽  
Feature Maps ◽  
Color Distribution ◽  
Salient Region Detection ◽  
Region Detection ◽  
Application Fields

Saliency detection is an important preprocessing step in many application fields such as computer vision, robotics, and graphics to reduce computational cost by focusing on significant positions and neglecting the nonsignificant in the scene. Different from most previous methods which mainly utilize the contrast of low-level features, various feature maps are fused in a simple linear weighting form. In this paper, we propose a novel salient object detection algorithm which takes both background and foreground cues into consideration and integrate a bottom-up coarse salient regions extraction and a top-down background measure via boundary labels propagation into a unified optimization framework to acquire a refined saliency detection result. Wherein the coarse saliency map is also fused by three components, the first is local contrast map which is in more accordance with the psychological law, the second is global frequency prior map, and the third is global color distribution map. During the formation of background map, first we construct an affinity matrix and select some nodes which lie on border as labels to represent the background and then carry out a propagation to generate the regional background map. The evaluation of the proposed model has been implemented on four datasets. As demonstrated in the experiments, our proposed method outperforms most existing saliency detection models with a robust performance.

scholarly journals Research on an Infrared Multi-Target Saliency Detection Algorithm under Sky Background Conditions

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 459
Author(s):  
Shaosheng Dai ◽  
Dongyang Li
Keyword(s):  
Saliency Detection ◽  
Infrared Image ◽  
Detection Algorithm ◽  
Saliency Map ◽  
Phase Spectrum ◽  
Background Suppression ◽  
Suppression Effect ◽  
Multi Scale ◽  
Saliency Maps ◽  
Image Saliency

Aiming at solving the problem of incomplete saliency detection and unclear boundaries in infrared multi-target images with different target sizes and low signal-to-noise ratio under sky background conditions, this paper proposes a saliency detection method for multiple targets based on multi-saliency detection. The multiple target areas of the infrared image are mainly bright and the background areas are dark. Combining with the multi-scale top hat (Top-hat) transformation, the image is firstly corroded and expanded to extract the subtraction of light and shade parts and reconstruct the image to reduce the interference of sky blurred background noise. Then the image obtained by a multi-scale Top-hat transformation is transformed from the time domain to the frequency domain, and the spectral residuals and phase spectrum are extracted directly to obtain two kinds of image saliency maps by multi-scale Gauss filtering reconstruction, respectively. On the other hand, the quaternion features are extracted directly to transform the phase spectrum, and then the phase spectrum is reconstructed to obtain one kind of image saliency map by the Gauss filtering. Finally, the above three saliency maps are fused to complete the saliency detection of infrared images. The test results show that after the experimental analysis of infrared video photographs and the comparative analysis of Receiver Operating Characteristic (ROC) curve and Area Under the Curve (AUC) index, the infrared image saliency map generated by this method has clear target details and good background suppression effect, and the AUC index performance is good, reaching over 99%. It effectively improves the multi-target saliency detection effect of the infrared image under the sky background and is beneficial to subsequent detection and tracking of image targets.

INFRARED SMALL TARGET DETECTION ALGORITHM BASED ON FEATURE SALIENCE AND MULTI-FEATURES FUSION

2011 ◽  
Vol 25 (02) ◽  
pp. 299-308 ◽  
Author(s):  
ZHEN-XUE CHEN ◽  
CHENG-YUN LIU ◽  
FA-LIANG CHANG
Keyword(s):  
Target Detection ◽  
Detection Algorithm ◽  
Probability Of Detection ◽  
Absolute Difference ◽  
Small Target ◽  
Probability Of Error ◽  
Small Target Detection ◽  
Low Snr ◽  
Complex Background ◽  
Small Targets

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.

Research on Fast Face Detection Algorithm in Complex Background

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.

Based on the Adaboost Algorithm of Face Detection Research

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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