scholarly journals Automatic Choice of Denoising Parameter in Perona-Malik Model

2019 ◽  
Author(s):  
Андрей Насонов ◽  
Andrey Nasonov ◽  
Николай Мамаев ◽  
Nikolay Mamaev ◽  
Ольга Володина ◽  
...  
Keyword(s):  
Image Denoising ◽  
Reference Method ◽  
Information Value ◽  
Parameter Choice ◽  
Photographic Images ◽  
Difference Image ◽  
Image Diffusion ◽  
Reference Parameter ◽  
The Difference ◽  
Automatic Choice

In this work, we propose a no-reference method for automatic choice of the parameters of Perona-Malik image diffusion algorithm for the problem of image denoising. The idea of the approach it to analyze and quantify the presence of structures in the difference image between the noisy image and the processed image as the mutual information value. We apply the proposed method to photographic images and to retinal images with modeled Gaussian noise with different parameters and analyze the effects of no-reference parameter choice compared to the optimal results. The proposed algorithm shows the effectiveness of no-reference parameter choice for the problem of image denoising.

Neural network processing of radar scanning data array

2021 ◽  
Author(s):  
V.E. Dmitriyev ◽  
D.V. Popov ◽  
V.A. Shakhnov
Keyword(s):  
Spatial Perception ◽  
Digital Processing ◽  
Radar Data ◽  
Information Value ◽  
Data Array ◽  
Additional Information ◽  
Distribution Of Values ◽  
The Difference ◽  
Radar Data Processing ◽  
Made In

This article deals with the digital processing of a matrix radar image. The information received from the radar scanner needs to be transformed to enable visual perception. The article describes the main methods of digital processing of matrix data, presents the images transformed by them. The aim of the article was the development of a radar data processing algorithm that identifies the contours and edges of examined objects. The authors propose an algorithm for isolating the geometric structure of the scanned area. The difference between the processing method and the known analogues is based on the nature of the change in the values of the array being processed and consists in the double operation of extracting the gradient of the distribution of values. The software implementation of the algorithm is made in C++ using methods from an open library of computer vision. The efficiency of the algorithm was estimated based on comparison with the algorithms for determining edges based on linear filtering and neural networks. The results of the work can be used to create software for mobile short-range radar devices. Imaging from object boundaries and their edges provides spatial perception of the image by the operator, and free areas are available for rendering additional information. This solution allows you to combine scanning devices and thereby increase the information value of the result.

scholarly journals A Novel Active Contours Model for Environmental Change Detection from Multitemporal Synthetic Aperture Radar Images

2020 ◽  
Vol 12 (11) ◽  
pp. 1746
Author(s):  
Salman Ahmadi ◽  
Saeid Homayouni
Keyword(s):  
Synthetic Aperture Radar ◽  
Change Detection ◽  
Active Contours ◽  
Training Data ◽  
Synthetic Aperture ◽  
Data Sets ◽  
Difference Image ◽  
Proposed Model ◽  
The Difference ◽  
Aperture Radar

In this paper, we propose a novel approach based on the active contours model for change detection from synthetic aperture radar (SAR) images. In order to increase the accuracy of the proposed approach, a new operator was introduced to generate a difference image from the before and after change images. Then, a new model of active contours was developed for accurately detecting changed regions from the difference image. The proposed model extracts the changed areas as a target feature from the difference image based on training data from changed and unchanged regions. In this research, we used the Otsu histogram thresholding method to produce the training data automatically. In addition, the training data were updated in the process of minimizing the energy function of the model. To evaluate the accuracy of the model, we applied the proposed method to three benchmark SAR data sets. The proposed model obtains 84.65%, 87.07%, and 96.26% of the Kappa coefficient for Yellow River Estuary, Bern, and Ottawa sample data sets, respectively. These results demonstrated the effectiveness of the proposed approach compared to other methods. Another advantage of the proposed model is its high speed in comparison to the conventional methods.

The Design of Real-Time Face Detection System Based on FPGA

2013 ◽  
Vol 473 ◽  
pp. 231-234
Author(s):  
Su Hua Chen ◽  
Xu Fang ◽  
Yong Guang Liu ◽  
Jun Wang
Keyword(s):  
Processing Speed ◽  
Detection System ◽  
Main Body ◽  
Infrared Source ◽  
Difference Image ◽  
Small Influence ◽  
The Face ◽  
The Difference ◽  
Locating System ◽  
Image Difference

The design attempts for thefirst time to realize face locating system on the FPGA platform using themethod combined initiative infrared source with image difference. Through imagedifference process, the system obtains a difference image without backgroundinterference which takes the face as the main body. It can obtain the personface boundary by projecting the difference image in the horizontal and verticaldirection. The system processing speed amount s to the video source frequency25 frame per second, satisfying the timely request; the method of initiativeinfrared source makes the exterior have small influence on the image andguarantees the robustness of the system.

scholarly journals SAR Image Change Detection Based on Multi-scale Feature Extraction

2021 ◽  
Vol 10 (4) ◽  
pp. 077-081
Author(s):  
Xiaoqian Yuan ◽  
Chao Chen ◽  
Shan Tian ◽  
Jiandan Zhong
Keyword(s):  
Feature Extraction ◽  
Change Detection ◽  
Speckle Noise ◽  
Kernel Matrix ◽  
Sar Image ◽  
Scale Feature ◽  
Difference Image ◽  
Multi Scale ◽  
Image Change Detection ◽  
The Difference

In order to improve the contrast of the difference image and reduce the interference of the speckle noise in the synthetic aperture radar (SAR) image, this paper proposes a SAR image change detection algorithm based on multi-scale feature extraction. In this paper, a kernel matrix with weights is used to extract features of two original images, and then the logarithmic ratio method is used to obtain the difference images of two images, and the change area of the images are extracted. Then, the different sizes of kernel matrix are used to extract the abstract features of different scales of the difference image. This operation can make the difference image have a higher contrast. Finally, the cumulative weighted average is obtained to obtain the final difference image, which can further suppress the speckle noise in the image.

scholarly journals A simple approach for monitoring vegetation change using time series remote sensing analysis: A case study from the Thathe Vondo Area in Limpopo Province, South Africa

2021 ◽  
Vol 117 (7/8) ◽  
Author(s):  
Nndanduleni Muavhi
Keyword(s):  
Time Series ◽  
Vegetation Cover ◽  
Vegetation Change ◽  
Vegetation Index ◽  
Simple Approach ◽  
Limpopo Province ◽  
Difference Image ◽  
Normalised Difference Vegetation Index ◽  
Gain And Loss ◽  
The Difference

This study presents a simple approach of spatiotemporal change detection of vegetation cover based on analysis of time series remotely sensed images. The study was carried out at Thathe Vondo Area, which is characterised by episodic variation of vegetation gain and loss. This variation is attributable to timber and tea plantations and their production cycles, which periodically result in either vegetation gain or loss. The approach presented here was implemented on two ASTER images acquired in 2007 and 2017. It involved the combined use of band combination, unsupervised image classification and Normalised Difference Vegetation Index (NDVI) techniques. True colour composite (TCC) images for 2007 and 2017 were created from combination of bands 1, 2 and 3 in red, blue and green, respectively. The difference image of the TCC images was then generated to show the inconsistencies of vegetation cover between 2007 and 2017. For analytical simplicity and interpretability, the difference image was subjected to ISODATA unsupervised classification, which clustered pixels in the difference image into eight classes. Two ISODATA derived classes were interpreted as vegetation gain and one as vegetation loss. These classes were confirmed as regions of vegetation gain and loss by NDVI values of 2007 and 2017. In addition, the polygons of vegetation gain and loss regions were created and superimposed over the TCC images to further demonstrate the spatiotemporal vegetation change in the area. The vegetation change statistics show vegetation gain and loss of 10.62% and 2.03%, respectively, implying a vegetation gain of 8.59% over the selected decade.

scholarly journals Performance Evaluation of Ground AR Anchor with WebXR Device API

2021 ◽  
Vol 11 (17) ◽  
pp. 7877
Author(s):  
Daehyeon Lee ◽  
Woosung Shim ◽  
Munyong Lee ◽  
Seunghyun Lee ◽  
Kye-Dong Jung ◽  
...  
Keyword(s):  
Performance Evaluation ◽  
Mixed Reality ◽  
Ground Truth ◽  
Application Programming Interface ◽  
Absolute Error ◽  
Frame Rate ◽  
Difference Image ◽  
Match Rate ◽  
The Difference ◽  
The Web

Recently, the development of 3D graphics technology has led to various technologies being combined with reality, where a new reality is defined or studied; they are typically named by combining the name of the technology with “reality”. Representative “reality” includes Augmented Reality, Virtual Reality, Mixed Reality, and eXtended Reality (XR). In particular, research on XR in the web environment is actively being conducted. The Web eXtended Reality Device Application Programming Interface (WebXR Device API), released in 2018, allows instant deployment of XR services to any XR platform requiring only an active web browser. However, the currently released tentative version has poor stability. Therefore, in this study, the performance evaluation of WebXR Device API is performed using three experiments. A camera trajectory experiment is analyzed using ground truth, we checked the standard deviation between the ground truth and WebXR for the X, Y, and Z axes. The difference image experiment is conducted for the front, left, and right directions, which resulted in a visible difference image for each image of ground truth and WebXR, small mean absolute error, and high match rate. In the experiment for measuring the 3D rendering speed, a frame rate similar to that of real-time is obtained.

scholarly journals Modified S2CVA Algorithm Using Cross-Sharpened Images for Unsupervised Change Detection

2018 ◽  
Vol 10 (9) ◽  
pp. 3301 ◽  
Author(s):  
Honglyun Park ◽  
Jaewan Choi ◽  
Wanyong Park ◽  
Hyunchun Park
Keyword(s):  
Change Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Seasonal Effects ◽  
Detection Accuracy ◽  
Difference Image ◽  
Before And After ◽  
Change Detection Algorithms ◽  
The Difference ◽  
Direction Information

This study aims to reduce the false alarm rate due to relief displacement and seasonal effects of high-spatial-resolution multitemporal satellite images in change detection algorithms. Cross-sharpened images were used to increase the accuracy of unsupervised change detection results. A cross-sharpened image is defined as a combination of synthetically pan-sharpened images obtained from the pan-sharpening of multitemporal images (two panchromatic and two multispectral images) acquired before and after the change. A total of four cross-sharpened images were generated and used in combination for change detection. Sequential spectral change vector analysis (S2CVA), which comprises the magnitude and direction information of the difference image of the multitemporal images, was applied to minimize the false alarm rate using cross-sharpened images. Specifically, the direction information of S2CVA was used to minimize the false alarm rate when applying S2CVA algorithms to cross-sharpened images. We improved the change detection accuracy by integrating the magnitude and direction information obtained using S2CVA for the cross-sharpened images. In the experiment using KOMPSAT-2 satellite imagery, the false alarm rate of the change detection results decreased with the use of cross-sharpened images compared to that with the use of only the magnitude information from the original S2CVA.

scholarly journals A Novel Change Detection Method Based on Statistical Distribution Characteristics Using Multi-Temporal PolSAR Data

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1508 ◽  
Author(s):  
Jinqi Zhao ◽  
Yonglei Chang ◽  
Jie Yang ◽  
Yufen Niu ◽  
Zhong Lu ◽  
...  
Keyword(s):  
Change Detection ◽  
Optimal Threshold ◽  
Distribution Model ◽  
Test Statistic ◽  
Omnibus Test ◽  
Polarimetric Synthetic Aperture Radar ◽  
Difference Image ◽  
East Lake ◽  
Multi Temporal ◽  
The Difference

Unsupervised change detection approaches, which are relatively straightforward and easy to implement and interpret, and which require no human intervention, are widely used in change detection. Polarimetric synthetic aperture radar (PolSAR), which has an all-weather response capability with increased polarimetric information, is a key tool for change detection. However, for PolSAR data, inadequate evaluation of the difference image (DI) map makes the threshold-based algorithms incompatible with the true distribution model, which causes the change detection results to be ineffective and inaccurate. In this paper, to solve these problems, we focus on the generation of the DI map and the selection of the optimal threshold. An omnibus test statistic is used to generate the DI map from multi-temporal PolSAR images, and an improved Kittler and Illingworth algorithm based on either Weibull or gamma distribution is used to obtain the optimal threshold for generating the change detection map. Multi-temporal PolSAR data obtained by the Radarsat-2 sensor over Wuhan in China are used to verify the efficiency of the proposed method. The experimental results using our approach obtained the best performance in East Lake and Yanxi Lake regions with false alarm rates of 1.59% and 1.80%, total errors of 2.73% and 4.33%, overall accuracy of 97.27% and 95.67%, and Kappa coefficients of 0.6486 and 0.6275, respectively. Our results demonstrated that the proposed method is more suitable than the other compared methods for multi-temporal PolSAR data, and it can obtain both effective and accurate results.

Comparison of Serum Calcium Measurements Obtained with the SMA 12/60 and by Atomic Absorption Spectrophotometry

1971 ◽  
Vol 17 (10) ◽  
pp. 1047-1049 ◽  
Author(s):  
S Raymond Gambino ◽  
Ivelice Fonseca
Keyword(s):  
Atomic Absorption ◽  
Serum Calcium ◽  
Large Scale ◽  
Colorimetric Assay ◽  
Internal Standard ◽  
Reference Method ◽  
Atomic Absorption Spectrophotometry ◽  
Laboratory Model ◽  
Absorption Spectrophotometry ◽  
The Difference

Abstract Comparable results are obtained when serum calcium is measured by a colorimetric cresolpthalein method on the Technicon SMA 12/60 and by atomic absorption spectrophotometry (Instrumentation Laboratory Model 153 instrument), with strontium as an internal standard. Sera from 250 patients with abnormal calcium concentrations, ranging from 4.9 mg/100 ml to 9.0 mg/100 ml, were measured by each method; the difference between the means for each method was only 0.04 mg/100 ml. The standard deviation of the difference between paired results was ±0.16 mg/100 ml. We were unable to discover any effect of drug or disease on the colorimetric assay, which therefore is suitable for use in large-scale surveys. On the other hand, the more precise atomic absorption spectrophotometry is suitable for use as a reference method. In addition, with an internal standard it is fast and simple, making it suitable for emergency assays of calcium 24 h a day.

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