scholarly journals Detecting Saliency in Infrared Images via Multiscale Local Sparse Representation and Local Contrast Measure

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Xin Wang ◽  
Chunyan Zhang ◽  
Chen Ning ◽  
Yuzhen Zhang ◽  
Guofang Lv
Keyword(s):  
Sparse Representation ◽  
Background Noise ◽  
Saliency Detection ◽  
Infrared Image ◽  
Saliency Map ◽  
Local Contrast ◽  
Infrared Images ◽  
Saliency Maps ◽  
Contrast Measure ◽  
Local Sparse Representation

For infrared images, it is a formidable challenge to highlight salient regions completely and suppress the background noise effectively at the same time. To handle this problem, a novel saliency detection method based on multiscale local sparse representation and local contrast measure is proposed in this paper. The saliency detection problem is implemented in three stages. First, a multiscale local sparse representation based approach is designed for detecting saliency in infrared images. Using it, multiple saliency maps with various scales are obtained for an infrared image. These maps are then fused to generate a combined saliency map, which can highlight the salient region fully. Second, we adopt a local contrast measure based technique to process the infrared image. It divides the image into a number of image blocks. Then these blocks are utilized to calculate the local contrast to generate a local contrast measure based saliency map. In this map, the background noise can be suppressed effectually. Last, to make full use of the advantages of the above two saliency maps, we propose combining them together using an adaptive fusion scheme. Experimental results show that our method achieves better performance than several state-of-the-art algorithms for saliency detection in infrared images.

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.

scholarly journals Saliency Detection by Multilevel Deep Pyramid Model

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Hai Wang ◽  
Lei Dai ◽  
Yingfeng Cai ◽  
Long Chen ◽  
Yong Zhang
Keyword(s):  
Background Noise ◽  
State Of The Art ◽  
Saliency Detection ◽  
Saliency Map ◽  
Multiple Features ◽  
Low Level ◽  
Pyramid Model ◽  
High Level ◽  
Different Levels ◽  
Better Than

Traditional salient object detection models are divided into several classes based on low-level features and contrast between pixels. In this paper, we propose a model based on a multilevel deep pyramid (MLDP), which involves fusing multiple features on different levels. Firstly, the MLDP uses the original image as the input for a VGG16 model to extract high-level features and form an initial saliency map. Next, the MLDP further extracts high-level features to form a saliency map based on a deep pyramid. Then, the MLDP obtains the salient map fused with superpixels by extracting low-level features. After that, the MLDP applies background noise filtering to the saliency map fused with superpixels in order to filter out the interference of background noise and form a saliency map based on the foreground. Lastly, the MLDP combines the saliency map fused with the superpixels with the saliency map based on the foreground, which results in the final saliency map. The MLDP is not limited to low-level features while it fuses multiple features and achieves good results when extracting salient targets. As can be seen in our experiment section, the MLDP is better than the other 7 state-of-the-art models across three different public saliency datasets. Therefore, the MLDP has superiority and wide applicability in extraction of salient targets.

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.

Saliency Optimization and Integration Via Iterative Bootstrap Learning

2018 ◽  
Vol 32 (09) ◽  
pp. 1859016
Author(s):  
Liming Li ◽  
Xiaodong Chai ◽  
Shuguang Zhao ◽  
Shubin Zheng ◽  
Shengchao Su
Keyword(s):  
Multiple Scales ◽  
Saliency Detection ◽  
Saliency Map ◽  
Input Image ◽  
Training Data ◽  
Data Sets ◽  
Saliency Maps ◽  
Training Samples ◽  
Public Data

This paper proposes an effective method to elevate the performance of saliency detection via iterative bootstrap learning, which consists of two tasks including saliency optimization and saliency integration. Specifically, first, multiscale segmentation and feature extraction are performed on the input image successively. Second, prior saliency maps are generated using existing saliency models, which are used to generate the initial saliency map. Third, prior maps are fed into the saliency regressor together, where training samples are collected from the prior maps at multiple scales and the random forest regressor is learned from such training data. An integration of the initial saliency map and the output of saliency regressor is deployed to generate the coarse saliency map. Finally, in order to improve the quality of saliency map further, both initial and coarse saliency maps are fed into the saliency regressor together, and then the output of the saliency regressor, the initial saliency map as well as the coarse saliency map are integrated into the final saliency map. Experimental results on three public data sets demonstrate that the proposed method consistently achieves the best performance and significant improvement can be obtained when applying our method to existing saliency models.

scholarly journals PSDSD-A Superpixel Generating Method Based on Pixel Saliency Difference and Spatial Distance for SAR Images

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 304 ◽  
Author(s):  
Tao Xie ◽  
Jingjian Huang ◽  
Qingzhan Shi ◽  
Qingping Wang ◽  
Naichang Yuan
Keyword(s):  
Distance Measure ◽  
Speckle Noise ◽  
Saliency Map ◽  
Spatial Distance ◽  
Gaussian Kernel ◽  
Local Contrast ◽  
Sar Images ◽  
Generating Method ◽  
Contrast Measure ◽  
Almost All

Superpixel methods are widely used in the processing of synthetic aperture radar (SAR) images. In recent years, a number of superpixel algorithms for SAR images have been proposed, and have achieved acceptable results despite the inherent speckle noise of SAR images. However, it is still difficult for existing algorithms to obtain satisfactory results in the inhomogeneous edge and texture areas. To overcome those problems, we propose a superpixel generating method based on pixel saliency difference and spatial distance for SAR images in this article. Firstly, a saliency map is calculated based on the Gaussian kernel function weighted local contrast measure, which can not only effectively suppress the speckle noise, but also enhance the fuzzy edges and regions with intensity inhomogeneity. Secondly, superpixels are generated by the local k-means clustering method based on the proposed distance measure, which can efficiently sort pixels to different clusters. In this step, the distance measure is calculated by combining the saliency difference and spatial distance with a proposed adaptive local compactness parameter. Thirdly, post-processing is utilized to clean up small segments. The evaluation experiments on the simulated SAR image demonstrate that our proposed method dramatically outperforms four state-of-the-art methods in terms of boundary recall, under-segmentation error, and achievable segmentation accuracy under almost all of the experimental parameters at a moderate segment speed. The experiments on real-world SAR images of different sceneries validate the superiority of our method. The superpixel results of the proposed method adhere well to the contour of targets, and correctly reflect the boundaries of texture details for the inhomogeneous regions.

scholarly journals A novel attention model for salient structure detection in seismic volumes

2021 ◽  
Vol 1 (1) ◽  
pp. 31-45
Author(s):  
Muhammad Amir Shafiq ◽  
◽  
Zhiling Long ◽  
Haibin Di ◽  
Ghassan AlRegib ◽  
...  
Keyword(s):  
Seismic Data ◽  
System Modeling ◽  
Saliency Detection ◽  
Spectral Projection ◽  
Detection Algorithm ◽  
Saliency Map ◽  
Seismic Interpretation ◽  
Imaging Data ◽  
Attention Model ◽  
Saliency Maps

<abstract><p>A new approach to seismic interpretation is proposed to leverage visual perception and human visual system modeling. Specifically, a saliency detection algorithm based on a novel attention model is proposed for identifying subsurface structures within seismic data volumes. The algorithm employs 3D-FFT and a multi-dimensional spectral projection, which decomposes local spectra into three distinct components, each depicting variations along different dimensions of the data. Subsequently, a novel directional center-surround attention model is proposed to incorporate directional comparisons around each voxel for saliency detection within each projected dimension. Next, the resulting saliency maps along each dimension are combined adaptively to yield a consolidated saliency map, which highlights various structures characterized by subtle variations and relative motion with respect to their neighboring sections. A priori information about the seismic data can be either embedded into the proposed attention model in the directional comparisons, or incorporated into the algorithm by specifying a template when combining saliency maps adaptively. Experimental results on two real seismic datasets from the North Sea, Netherlands and Great South Basin, New Zealand demonstrate the effectiveness of the proposed algorithm for detecting salient seismic structures of different natures and appearances in one shot, which differs significantly from traditional seismic interpretation algorithms. The results further demonstrate that the proposed method outperforms comparable state-of-the-art saliency detection algorithms for natural images and videos, which are inadequate for seismic imaging data.</p></abstract>

Image Classification Based on Sparse Representation and ROI

2014 ◽  
Vol 556-562 ◽  
pp. 4906-4910
Author(s):  
Hui Hui Zhao ◽  
Jun Ding Sun
Keyword(s):  
Sparse Representation ◽  
Image Classification ◽  
Extraction Methods ◽  
Saliency Map ◽  
Regions Of Interest ◽  
Experimental Results ◽  
Classification Method ◽  
Saliency Maps ◽  
Roi Extraction

A new image classification method based on regions of interest (ROI) and sparse representation is introduced in the paper. Firstly, the saliency map of each image is extracted by different methods. Then, we choose sparse representation to represent and classify the saliency maps. Four different ROI extraction methods are chosen as examples to evaluate the performance of the proposed method. Experimental results show that it is more effective for image classification based on ROI.

scholarly journals Beyond Universal Saliency: Personalized Saliency Prediction with Multi-task CNN

2017 ◽  
Author(s):  
Yanyu Xu ◽  
Nianyi Li ◽  
Junru Wu ◽  
Jingyi Yu ◽  
Shenghua Gao
Keyword(s):  
Computer Vision ◽  
Saliency Detection ◽  
Saliency Map ◽  
Saliency Maps ◽  
Saliency Model ◽  
Gaze Patterns ◽  
Prediction Scheme ◽  
Saliency Prediction ◽  
Standing Problem

Saliency detection is a long standing problem in computer vision. Tremendous efforts have been focused on exploring a universal saliency model across users despite their differences in gender, race, age, etc. Yet recent psychology studies suggest that saliency is highly specific than universal: individuals exhibit heterogeneous gaze patterns when viewing an identical scene containing multiple salient objects. In this paper, we first show that such heterogeneity is common and critical for reliable saliency prediction. Our study also produces the first database of personalized saliency maps (PSMs). We model PSM based on universal saliency map (USM) shared by different participants and adopt a multi-task CNN framework to estimate the discrepancy between PSM and USM. Comprehensive experiments demonstrate that our new PSM model and prediction scheme are effective and reliable.

scholarly journals A Generative Adversarial Network with Dual Discriminators for Infrared and Visible Image Fusion Based on Saliency Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Dazhi Zhang ◽  
Jilei Hou ◽  
Wei Wu ◽  
Tao Lu ◽  
Huabing Zhou
Keyword(s):  
Image Fusion ◽  
Loss Function ◽  
Network Architecture ◽  
Saliency Detection ◽  
Infrared Image ◽  
Saliency Map ◽  
Generative Adversarial Network ◽  
Visible Image ◽  
Adversarial Network ◽  
Salient Regions

Infrared and visible image fusion needs to preserve both the salient target of the infrared image and the texture details of the visible image. Therefore, an infrared and visible image fusion method based on saliency detection is proposed. Firstly, the saliency map of the infrared image is obtained by saliency detection. Then, the specific loss function and network architecture are designed based on the saliency map to improve the performance of the fusion algorithm. Specifically, the saliency map is normalized to [0, 1], used as a weight map to constrain the loss function. At the same time, the saliency map is binarized to extract salient regions and nonsalient regions. And, a generative adversarial network with dual discriminators is obtained. The two discriminators are used to distinguish the salient regions and the nonsalient regions, respectively, to promote the generator to generate better fusion results. The experimental results show that the fusion results of our method are better than those of the existing methods in both subjective and objective aspects.

Saliency Detection through Nonlinear Metric Fusion

2014 ◽  
Vol 926-930 ◽  
pp. 3692-3695
Author(s):  
Zhi Yang
Keyword(s):  
Visual System ◽  
Human Visual System ◽  
Saliency Detection ◽  
Saliency Map ◽  
Difficult Problem ◽  
Fusion Strategy ◽  
Cross Diffusion ◽  
Saliency Maps ◽  
Feature Information ◽  
Nature Scene

In order to detect the saliency part in the picture of nature scene, usually, bottom-up saliency models use several feature information such as color and orientation, each feature is taken to compute the saliency map respectively, then these saliency maps are linearly combined to a final saliency map. However, how to fuse these features are still a difficult problem, linearly combined is not appropriate to the human visual system, so in this paper we propose a nonlinear metric fusion strategy with different features namely cross diffusion, it fuse the metric from diverse features which can be used to detect the saliency more appropriate.

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