scholarly journals Saliency-Based Diver Target Detection and Localization Method

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Jianjiang Zhu ◽  
Siquan Yu ◽  
Lei Gao ◽  
Zhi Han ◽  
Yandong Tang
Keyword(s):  
Saliency Detection ◽  
Region Of Interest ◽  
Saliency Map ◽  
Shadow Region ◽  
Localization Accuracy ◽  
Acoustic Shadow ◽  
Sonar Image ◽  
Sonar Images ◽  
Detection And Localization

Diver target automatic detection is indispensable for underwater defense systems, such as the unmanned harbor surveillance system. It is a very challenging task due to various poses and intensity features of diver target. In addition, the background noise in sonar images is complex, which also makes the task more difficult. In this paper, we propose a diver detection method based on saliency detection for sonar images. On the basis of studying the characteristics of diver sonar images, we first decompose the original sonar image and perform median filtering on it, which can significantly improve the quality of the sonar image saliency map. We employ saliency detection technique based on frequency analysis to segment the acoustic highlight region from its surroundings. This segmentation region roughly locates the diver target and generates a region of interest (ROI). We then extract the acoustic shadow region in ROI, which contributes to furtherly improve the localization accuracy. Finally, we merge the segmented highlight region and the extracted acoustic shadow region and compute the minimum outer rectangle of the merged region. Experimental results validate that the proposed method can well detect and locate the diver target, and it can also satisfy the demands of real-time application, and there is almost no false alarm in this method.

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 Sonar image recognition of underwater target based on convolutional neural network

2021 ◽  
Vol 39 (2) ◽  
pp. 285-291
Author(s):  
Leilei Jin ◽  
Hong LIANG ◽  
Changsheng Yang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Recognition ◽  
Target Recognition ◽  
Saliency Detection ◽  
Image Features ◽  
Feature Maps ◽  
Sonar Image ◽  
Sonar Images ◽  
Underwater Target

Underwater target recognition is one core technology of underwater unmanned detection. To improve the accuracy of underwater automatic target recognition, a sonar image recognition method based on convolutional neural network was proposed and the underwater target recognition model was established according to the characteristics of sonar images. Firstly, the sonar image was segmented and clipped with a saliency detection method to reduce the dimension of input data, and to reduce the interference of image background to the feature extraction process. Secondly, by using stacked convolutional layers and pooling layers, the high-level semantic information of the target was automatically learned from the input sonar image, to avoid damaging the effective information caused by extracting image features manually. Finally, the spatial pyramid pooling method was used to extract the multi-scale information from the sonar feature maps, which was to make up for the lack of detailed information of sonar images and solve the problem caused by the inconsistent size of input images. On the collected sonar image dataset, the experimental results show that the target recognition accuracy of the present method can recognize underwater targets more accurately and efficiently than the conventional convolutional neural networks.

A Multi-View Sonar Image Fusion Method Based on Nonsubsampled Contourlet Transform and Morphological Modification

2014 ◽  
Vol 530-531 ◽  
pp. 567-570 ◽  
Author(s):  
Zhi Gang Zhang ◽  
Hong Yu Bian ◽  
Zi Qi Song ◽  
Hui Xu
Keyword(s):  
Image Fusion ◽  
Contourlet Transform ◽  
Nonsubsampled Contourlet Transform ◽  
Multi Scale ◽  
Sonar Image ◽  
Morphological Modification ◽  
Morphology Modification ◽  
Sonar Images ◽  
Set Up

In underwater detection, a single image from the image sonar doesnt have the capacity to fully describe the surface of the interested target. In practice, a target is usually detected from various views, and similar contours and textures in a series of multi-view images can be employed. This work offers a novel technique for the fusion of a series of sonar images in multi-view detection of the same target, to improve the quality of images and repair the regions of target. The method takes advantage of Nonsubsampled Contourlet Transform (NSCT) to acquire coefficient matrixes of different resolutions and directions. Besides, a framework for sonar image fusion is set up based on morphology modification. The coefficient matrixes in NSCT domain are fused in the multi-scale framework and revised in decision-making. Experimental results show that target regions in the fused sonar images are effectively repaired and image quality also get improved evidently.

OSTEOPOROSIS AND RISK FACTORS IN THE PATIENTS ON CHRONIC HEMODIALYSIS

2015 ◽  
pp. 50-58
Author(s):  
Thi Dung Nguyen ◽  
Tam Vo
Keyword(s):  
Quality Of Life ◽  
Risk Factors ◽  
Bone Density ◽  
Region Of Interest ◽  
Cross Sectional Study ◽  
Chronic Hemodialysis ◽  
Cross Sectional ◽  
X Ray ◽  
Density Reduction

Background: The patients on hemodialysis have a significantly decreased quality of life. One of many problems which reduce the quality of life and increase the mortality in these patients is osteoporosis and osteoporosis associated fractures. Objectives: To assess the bone density of those on hemodialysis by dual energy X ray absorptiometry and to examine the risk factors of bone density reduction in these patients. Patients and Method: This is a cross-sectional study, including 93 patients on chronic hemodialysis at the department of Hemodialysis at Cho Ray Hospital. Results: Mean bone densities at the region of interest (ROI) neck, trochanter, Ward triangle, intertrochanter and total neck are 0.603 ± 0.105; 0.583 ± 0.121; 0.811 ± 0.166; 0.489 ± 0.146; 0.723 ± 0.138 g/cm2 respectively. The prevalences of osteoporosis at those ROI are 39.8%, 15.1%; 28%; 38.7%; and 26.9% respectively. The prevalences of osteopenia at those ROI are 54.8%; 46.3%; 60.2%; 45.2% and 62.7% respectively. The prevalence of osteopososis in at least one ROI is 52.7% and the prevalence of osteopenia in at least one ROI is 47.3%. There are relations between the bone density at the neck and the gender of the patient and the albuminemia. Bone density at the trochanter is influenced by gender, albuminemia, calcemia and phosphoremia. Bone density at the intertrochanter is affected by the gender. Bone density at the Ward triangle is influenced by age and albuminemia. Total neck bone density is influenced by gender, albuminemia and phosphoremia. Conclusion: Osteoporosis in patients on chronic hemodialysis is an issue that requires our attention. There are many interventionable risk factors of bone density decrease in these patients. Key words: Osteoporosis, DEXA, chronic renal failure, chronic hemodialysis

scholarly journals On Combining DeepSnake and Global Saliency for Detection of Orchard Apples

2021 ◽  
Vol 11 (14) ◽  
pp. 6269
Author(s):  
Wang Jing ◽  
Wang Leqi ◽  
Han Yanling ◽  
Zhang Yun ◽  
Zhou Ruyan
Keyword(s):  
Saliency Detection ◽  
Color Difference ◽  
Saliency Map ◽  
Apple Fruit ◽  
Initial Contour ◽  
Detection Accuracy ◽  
Natural Environments ◽  
Fast Detection ◽  
Single Target ◽  
Training Samples

For the fast detection and recognition of apple fruit targets, based on the real-time DeepSnake deep learning instance segmentation model, this paper provided an algorithm basis for the practical application and promotion of apple picking robots. Since the initial detection results have an important impact on the subsequent edge prediction, this paper proposed an automatic detection method for apple fruit targets in natural environments based on saliency detection and traditional color difference methods. Combined with the original image, the histogram backprojection algorithm was used to further optimize the salient image results. A dynamic adaptive overlapping target separation algorithm was proposed to locate the single target fruit and further to determine the initial contour for DeepSnake, in view of the possible overlapping fruit regions in the saliency map. Finally, the target fruit was labeled based on the segmentation results of the examples. In the experiment, 300 training datasets were used to train the DeepSnake model, and the self-built dataset containing 1036 pictures of apples in various situations under natural environment was tested. The detection accuracy of target fruits under non-overlapping shaded fruits, overlapping fruits, shaded branches and leaves, and poor illumination conditions were 99.12%, 94.78%, 90.71%, and 94.46% respectively. The comprehensive detection accuracy was 95.66%, and the average processing time was 0.42 s in 1036 test images, which showed that the proposed algorithm can effectively separate the overlapping fruits through a not-very-large training samples and realize the rapid and accurate detection of apple targets.

Processing of Sonar Image Based on Compressive Sensing

2011 ◽  
Vol 301-303 ◽  
pp. 719-723 ◽  
Author(s):  
Zhi Jing Xu ◽  
Huan Lei Dai ◽  
Pei Pei Cao
Keyword(s):  
Wavelet Transform ◽  
Matching Pursuit ◽  
Experimental Result ◽  
Underwater Acoustic Channel ◽  
Sonar Image ◽  
Underwater Image ◽  
Reconstruction Image ◽  
Observation Matrix ◽  
Efficient Transmission

The particularity of the underwater acoustic channel has put forward a higher request for collection and efficient transmission of the underwater image. In this paper, based on the characteristics of sonar image, wavelet transform is used to sparse decompose the image, and selecting Gaussian random matrix as the observation matrix and using the orthogonal matching pursuit (OMP) algorithm to reconstruct the image. The experimental result shows that the quality of the reconstruction image and PSNR have gained great ascension compared to the traditional compression and processing of image based on the wavelet transform while they have the same measurement numbers in the coding portion. It provides a convenient for the sonar image’s underwater transmission.

scholarly journals Enhancement of Region of Interest CT Reconstructions through Multimodal Data

2018 ◽  
Vol 4 (1) ◽  
pp. 331-335
Author(s):  
David Schote ◽  
Tim Pfeiffer ◽  
Georg Rose
Keyword(s):  
Substantial Reduction ◽  
Region Of Interest ◽  
Proof Of Concept ◽  
Multimodal Data ◽  
New Approach ◽  
Full Field ◽  
Lack Of Information ◽  
Ct Data ◽  
Flight Measurements

AbstractComputed tomography (CT) scans are frequently used intraoperatively, for example to control the positioning of implants during intervention. Often, to provide the required information, a full field of view is unnecessary. I nstead, the region-of-interest (ROI) imaging can be performed, allowing for substantial reduction in the applied X-ray dose. However, ROI imaging leads to data inconsistencies, caused by the truncation of the projections. This lack of information severely impairs the quality of the reconstructed images. This study presents a proof-of-concept for a new approach that combines the incomplete CT data with ultrasound data and time of flight measurements in order to restore some of the lacking information. The routine is evaluated in a simulation study using the original Shepp-Logan phantom in ROI cases with different degrees of truncation. Image quality is assessed by means of normalized root mean square error. The proposed method significantly reduces truncation artifacts in the reconstructions and achieves considerable radiation exposure reductions.

scholarly journals Ship Detection in Panchromatic Optical Remote Sensing Images Based on Visual Saliency and Multi-Dimensional Feature Description

2020 ◽  
Vol 12 (1) ◽  
pp. 152 ◽  
Author(s):  
Ting Nie ◽  
Xiyu Han ◽  
Bin He ◽  
Xiansheng Li ◽  
Hongxing Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Fourier Transform ◽  
Detection Method ◽  
Saliency Detection ◽  
Visual Saliency ◽  
Saliency Map ◽  
False Alarms ◽  
Optical Remote Sensing ◽  
Remote Sensing Images ◽  
Ship Detection

Ship detection in panchromatic optical remote sensing images is faced with two major challenges, locating candidate regions from complex backgrounds quickly and describing ships effectively to reduce false alarms. Here, a practical method was proposed to solve these issues. Firstly, we constructed a novel visual saliency detection method based on a hyper-complex Fourier transform of a quaternion to locate regions of interest (ROIs), which can improve the accuracy of the subsequent discrimination process for panchromatic images, compared with the phase spectrum quaternary Fourier transform (PQFT) method. In addition, the Gaussian filtering of different scales was performed on the transformed result to synthesize the best saliency map. An adaptive method based on GrabCut was then used for binary segmentation to extract candidate positions. With respect to the discrimination stage, a rotation-invariant modified local binary pattern (LBP) description was achieved by combining shape, texture, and moment invariant features to describe the ship targets more powerfully. Finally, the false alarms were eliminated through SVM training. The experimental results on panchromatic optical remote sensing images demonstrated that the presented saliency model under various indicators is superior, and the proposed ship detection method is accurate and fast with high robustness, based on detailed comparisons to existing efforts.

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 SALIENCY-GUIDED CHANGE DETECTION OF REMOTELY SENSED IMAGES USING RANDOM FOREST

2018 ◽  
Vol XLII-3 ◽  
pp. 341-348
Author(s):  
W. Feng ◽  
H. Sui ◽  
X. Chen
Keyword(s):  
Random Forest ◽  
Change Detection ◽  
Learning Algorithm ◽  
Saliency Detection ◽  
Principal Component ◽  
Visual Saliency ◽  
Saliency Map ◽  
Difference Image ◽  
Fcm Clustering ◽  
Optimal Segmentation

Studies based on object-based image analysis (OBIA) representing the paradigm shift in change detection (CD) have achieved remarkable progress in the last decade. Their aim has been developing more intelligent interpretation analysis methods in the future. The prediction effect and performance stability of random forest (RF), as a new kind of machine learning algorithm, are better than many single predictors and integrated forecasting method. In this paper, we present a novel CD approach for high-resolution remote sensing images, which incorporates visual saliency and RF. First, highly homogeneous and compact image super-pixels are generated using super-pixel segmentation, and the optimal segmentation result is obtained through image superimposition and principal component analysis (PCA). Second, saliency detection is used to guide the search of interest regions in the initial difference image obtained via the improved robust change vector analysis (RCVA) algorithm. The salient regions within the difference image that correspond to the binarized saliency map are extracted, and the regions are subject to the fuzzy <i>c</i>-means (FCM) clustering to obtain the pixel-level pre-classification result, which can be used as a prerequisite for superpixel-based analysis. Third, on the basis of the optimal segmentation and pixel-level pre-classification results, different super-pixel change possibilities are calculated. Furthermore, the changed and unchanged super-pixels that serve as the training samples are automatically selected. The spectral features and Gabor features of each super-pixel are extracted. Finally, superpixel-based CD is implemented by applying RF based on these samples. Experimental results on Ziyuan 3 (ZY3) multi-spectral images show that the proposed method outperforms the compared methods in the accuracy of CD, and also confirm the feasibility and effectiveness of the proposed approach.

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