scholarly journals Developing an Image Manipulation Detection Algorithm Based on Edge Detection and Faster R-CNN

Symmetry ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1223 ◽  
Author(s):  
Xiaoyan Wei ◽  
Yirong Wu ◽  
Fangmin Dong ◽  
Jun Zhang ◽  
Shuifa Sun
Keyword(s):  
Edge Detection ◽  
Feature Fusion ◽  
Interpolation Method ◽  
Region Of Interest ◽  
Detection Algorithm ◽  
Image Manipulation ◽  
Bilinear Interpolation ◽  
Detection Algorithms ◽  
Wide Availability ◽  
Manipulation Detection

Due to the wide availability of the tools used to produce manipulated images, a large number of digital images have been tampered with in various media, such as newspapers and social networks, which makes the detection of tampered images particularly important. Therefore, an image manipulation detection algorithm leveraged by the Faster Region-based Convolutional Neural Network (Faster R-CNN) model combined with edge detection was proposed in this paper. In our algorithm, first, original tampered images and their detected edges were sent into symmetrical ResNet101 networks to extract tampering features. Then, these features were put into the Region of Interest (RoI) pooling layer. Instead of the RoI max pooling approach, the bilinear interpolation method was adopted to obtain the RoI region. After the RoI features of original input images and edge feature images were sent into bilinear pooling layer for feature fusion, tampering classification was performed in fully connection layer. Finally, Region Proposal Network (RPN) was used to locate forgery regions. Experimental results on three different image manipulation datasets show that our proposed algorithm can detect tampered images more effectively than other existing image manipulation detection algorithms.

A WT-Based Edge Detection with Adaptive Threshold

2011 ◽  
Vol 204-210 ◽  
pp. 1386-1389
Author(s):  
Deng Yin Zhang ◽  
Li Xiao ◽  
Shun Rong Bo
Keyword(s):  
Wavelet Transform ◽  
Edge Detection ◽  
Threshold Value ◽  
Detection Algorithm ◽  
Adaptive Threshold ◽  
Value Iteration ◽  
Edge Detection Algorithm ◽  
Detection Algorithms ◽  
Image Edge ◽  
Threshold Setting

The existing edge detection algorithms with wavelet transform need to artificially set the threshold value and are lack of flexibility.To salve the limitations, in this paper, we propose a WT(wavelet transform)-based edge detection algorithm with adaptive threshold, which uses threshold value iteration method to achieve adaptive threshold setting. Comparison of experiment results for the CT image shows that the method which improve the clarity and continuity of the image edge can effectively distinguish edge and noise, and get more completely information of the edge. It has good application value in the fields of medical clinical diagnosis and image processing.

scholarly journals Hardware Implementation of Sobel Edge Detection Algorithm

2020 ◽  
Vol 32 ◽  
pp. 03051
Author(s):  
Ankita Pujare ◽  
Priyanka Sawant ◽  
Hema Sharma ◽  
Khushboo Pichhode
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Feature Detection ◽  
Hardware Implementation ◽  
Software Tool ◽  
Detection Algorithm ◽  
Edge Detection Algorithm ◽  
Detection Algorithms ◽  
Sobel Edge Detection ◽  
Image Edges

In the fields of image processing, feature detection, the edge detection is an important aspect. For detection of sharp changes in the properties of an image, edges are recognized as important factors which provides more information or data regarding the analysis of an image. In this work coding of various edge detection algorithms such as Sobel, Canny, etc. have been done on the MATLAB software, also this work is implemented on the FPGA Nexys 4 DDR board. The results are then displayed on a VGA screen. The implementation of this work using Verilog language of FPGA has been executed on Vivado 18.2 software tool.

scholarly journals An Improved Unauthorized Unmanned Aerial Vehicle Detection Algorithm Using Radiofrequency-Based Statistical Fingerprint Analysis

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 274 ◽  
Author(s):  
Shengying Yang ◽  
Huibin Qin ◽  
Xiaolin Liang ◽  
Thomas Gulliver
Keyword(s):  
Signal To Noise Ratio ◽  
Recognition Rate ◽  
Region Of Interest ◽  
Detection Algorithm ◽  
Estimation Accuracy ◽  
Fingerprint Analysis ◽  
Detection Algorithms ◽  
Rf Signals ◽  
Aerial Vehicle ◽  
Value Decomposition

Unmanned aerial vehicles (UAVs) are now readily available worldwide and users can easily fly them remotely using smart controllers. This has created the problem of keeping unauthorized UAVs away from private or sensitive areas where they can be a personal or public threat. This paper proposes an improved radio frequency (RF)-based method to detect UAVs. The clutter (interference) is eliminated using a background filtering method. Then singular value decomposition (SVD) and average filtering are used to reduce the noise and improve the signal to noise ratio (SNR). Spectrum accumulation (SA) and statistical fingerprint analysis (SFA) are employed to provide two frequency estimates. These estimates are used to determine if a UAV is present in the detection environment. The data size is reduced using a region of interest (ROI), and this improves the system efficiency and improves azimuth estimation accuracy. Detection results are obtained using real UAV RF signals obtained experimentally which show that the proposed method is more effective than other well-known detection algorithms. The recognition rate with this method is close to 100% within a distance of 2.4 km and greater than 90% within a distance of 3 km. Further, multiple UAVs can be detected accurately using the proposed method.

scholarly journals Atmospheric River Tracking Method Intercomparison Project (ARTMIP): project goals and experimental design

2018 ◽  
Vol 11 (6) ◽  
pp. 2455-2474 ◽  
Author(s):  
Christine A. Shields ◽  
Jonathan J. Rutz ◽  
Lai-Yung Leung ◽  
F. Martin Ralph ◽  
Michael Wehner ◽  
...  
Keyword(s):  
Experimental Design ◽  
Region Of Interest ◽  
Detection Algorithm ◽  
Tracking Method ◽  
Detection Algorithms ◽  
Atmospheric River ◽  
Wide Range ◽  
Tier 1 ◽  
Intercomparison Project ◽  
Two Phases

Abstract. The Atmospheric River Tracking Method Intercomparison Project (ARTMIP) is an international collaborative effort to understand and quantify the uncertainties in atmospheric river (AR) science based on detection algorithm alone. Currently, there are many AR identification and tracking algorithms in the literature with a wide range of techniques and conclusions. ARTMIP strives to provide the community with information on different methodologies and provide guidance on the most appropriate algorithm for a given science question or region of interest. All ARTMIP participants will implement their detection algorithms on a specified common dataset for a defined period of time. The project is divided into two phases: Tier 1 will utilize the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) reanalysis from January 1980 to June 2017 and will be used as a baseline for all subsequent comparisons. Participation in Tier 1 is required. Tier 2 will be optional and include sensitivity studies designed around specific science questions, such as reanalysis uncertainty and climate change. High-resolution reanalysis and/or model output will be used wherever possible. Proposed metrics include AR frequency, duration, intensity, and precipitation attributable to ARs. Here, we present the ARTMIP experimental design, timeline, project requirements, and a brief description of the variety of methodologies in the current literature. We also present results from our 1-month “proof-of-concept” trial run designed to illustrate the utility and feasibility of the ARTMIP project.

An Effective Method for Defects Detection in Radiographic Images of Welds Based on Edge Detection and Morphology

2013 ◽  
Vol 290 ◽  
pp. 71-77
Author(s):  
Wen Ming Guo ◽  
Yan Qin Chen
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
Detection Algorithm ◽  
Morphological Processing ◽  
Radiographic Images ◽  
X Ray ◽  
Weld Defects ◽  
Detection Algorithms ◽  
Detection Quality

In the current industrial production, as steel weld X-ray images are low contrasted and noisy, the efficiency and precision can’t be both ensured. This paper has studied three different edge detection algorithms and found the most suitable one to detect weld defects. Combined with this edge detection algorithm, we proposed a new weld defects detection method. This method uses defect features to find the defects in edge images with morphological processing. Compared to the traditional methods, the method has ensured detection quality of weld defects detection.

scholarly journals A Low Redundancy Wavelet Entropy Edge Detection Algorithm

2021 ◽  
Vol 7 (9) ◽  
pp. 188
Author(s):  
Yiting Tao ◽  
Thomas Scully ◽  
Asanka G. Perera ◽  
Andrew Lambert ◽  
Javaan Chahl
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Shannon Entropy ◽  
Detection Algorithm ◽  
Edge Detection Algorithm ◽  
Detection Techniques ◽  
Detection Algorithms ◽  
Real Time Image ◽  
Time Image ◽  
Vision Application

Fast edge detection of images can be useful for many real-world applications. Edge detection is not an end application but often the first step of a computer vision application. Therefore, fast and simple edge detection techniques are important for efficient image processing. In this work, we propose a new edge detection algorithm using a combination of the wavelet transform, Shannon Entropy and thresholding. The new algorithm is based on the concept that each Wavelet decomposition level has an assumed level of structure that enables the use of Shannon entropy as a measure of global image structure. The proposed algorithm is developed mathematically and compared to five popular edge detection algorithms. The results show that our solution is low redundancy, noise resilient, and well suited to real-time image processing applications.

scholarly journals An algorithm for detecting the Chesapeake Bay breeze from mesoscale NWP model output

2021 ◽  
Keyword(s):  
Chesapeake Bay ◽  
Prediction Models ◽  
Weather Prediction ◽  
Wrf Model ◽  
Region Of Interest ◽  
Detection Algorithm ◽  
Model Output ◽  
Detection Algorithms ◽  
Bay Breeze ◽  
Nwp Model

Abstract A novel algorithm is developed for detecting and classifying the Chesapeake Bay breeze and similar water-body breezes in output from mesoscale numerical weather prediction models. To assess the generality of the new model-based detection algorithm (MBDA), it is tested on simulations from the Weather Research and Forecasting (WRF) model and on analyses and forecasts from the High Resolution Rapid Refresh (HRRR) model. The MBDA outperforms three observation-based detection algorithms (OBDAs) when applied to the same model output. Additionally, by defining the onshore wind directions based on model land-use data, not on the actual geography of the region of interest, performance of the OBDAs with model output can be improved. Although simulations by the WRF model were used to develop the new MBDA, it performed best when applied to HRRR analyses. The generality of the MBDA is promising, and additional tuning of its parameters might improve it further.

scholarly journals Dynamic Object Detection Algorithm Based on Lightweight Shared Feature Pyramid

2021 ◽  
Vol 13 (22) ◽  
pp. 4610
Author(s):  
Li Zhu ◽  
Zihao Xie ◽  
Jing Luo ◽  
Yuhang Qi ◽  
Liman Liu ◽  
...  
Keyword(s):  
Object Detection ◽  
Feature Fusion ◽  
Computational Cost ◽  
Detection Algorithm ◽  
Dynamic Object ◽  
Shared Feature ◽  
Detection Model ◽  
Detection Algorithms ◽  
Adaptive Inference ◽  
Feature Pyramid

Current object detection algorithms perform inference on all samples at a fixed computational cost in the inference stage, which wastes computing resources and is not flexible. To solve this problem, a dynamic object detection algorithm based on a lightweight shared feature pyramid is proposed, which performs adaptive inference according to computing resources and the difficulty of samples, greatly improving the efficiency of inference. Specifically, a lightweight shared feature pyramid network and lightweight detection head is proposed to reduce the amount of computation and parameters in the feature fusion part and detection head of the dynamic object detection model. On the PASCAL VOC dataset, under the two conditions of “anytime prediction” and “budgeted batch object detection”, the performance, computation amount and parameter amount are better than the dynamic object detection models constructed by networks such as ResNet, DenseNet and MSDNet.

scholarly journals The Application of Improved YOLO V3 in Multi-Scale Target Detection

2019 ◽  
Vol 9 (18) ◽  
pp. 3775 ◽  
Author(s):  
Ju ◽  
Luo ◽  
Wang ◽  
Hui ◽  
Chang
Keyword(s):  
Target Detection ◽  
Feature Fusion ◽  
Detection Algorithm ◽  
Detection Performance ◽  
Research Directions ◽  
Detection Algorithms ◽  
Multi Scale ◽  
Derivation Method ◽  
Small Targets ◽  
Mathematical Derivation

Target detection is one of the most important research directions in computer vision. Recently, a variety of target detection algorithms have been proposed. Since the targets have varying sizes in a scene, it is essential to be able to detect the targets at different scales. To improve the detection performance of targets with different sizes, a multi-scale target detection algorithm was proposed involving improved YOLO (You Only Look Once) V3. The main contributions of our work include: (1) a mathematical derivation method based on Intersection over Union (IOU) was proposed to select the number and the aspect ratio dimensions of the candidate anchor boxes for each scale of the improved YOLO V3; (2) To further improve the detection performance of the network, the detection scales of YOLO V3 have been extended from 3 to 4 and the feature fusion target detection layer downsampled by 4× is established to detect the small targets; (3) To avoid gradient fading and enhance the reuse of the features, the six convolutional layers in front of the output detection layer are transformed into two residual units. The experimental results upon PASCAL VOC dataset and KITTI dataset show that the proposed method has obtained better performance than other state-of-the-art target detection algorithms.

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