scholarly journals Multifeature Detection of Microaneurysms Based on Improved SSA

Symmetry ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2147
Author(s):  
Liwei Deng ◽  
Xiaofei Wang ◽  
Jiazhong Xu
Keyword(s):  
Detection Method ◽  
Binary Image ◽  
Detection Methods ◽  
Connected Components ◽  
Detection Problem ◽  
Evaluation Standards ◽  
Binary Images ◽  
Fitness Value ◽  
Overall Performance ◽  
Better Than

The early diagnosis of retinopathy is crucial to the prevention and treatment of diabetic retinopathy. The low proportion of positive cases in the asymmetric microaneurysm detection problem causes preprocessing to treat microaneurysms as noise to be eliminated. To obtain a binary image containing microaneurysms, the object was segmented by a symmetry algorithm, which is a combination of the connected components and SSA methods. Next, a candidate microaneurysm set was extracted by multifeature clustering of binary images. Finally, the candidate microaneurysms were mapped to the Radon frequency domain to achieve microaneurysm detection. In order to verify the feasibility of the algorithm, a comparative experiment was conducted on the combination of the connected components and SSA methods. In addition, PSNR, FSIM, SSIM, fitness value, average CPU time and other indicators were used as evaluation standards. The results showed that the overall performance of the binary image obtained by the algorithm was the best. Last but not least, the accuracy of the detection method for microaneurysms in this paper reached up to 93.24%, which was better than that of several classic microaneurysm detection methods in the same period.

Fast Ellipse Detection Method Based on Coordinates Transformation

2014 ◽  
Vol 926-930 ◽  
pp. 3038-3041
Author(s):  
Cheng Wang
Keyword(s):  
Digital Image ◽  
Detection Method ◽  
Closed Curve ◽  
New Method ◽  
Detection Methods ◽  
Circle Detection ◽  
Detection Problem ◽  
Ellipse Detection ◽  
Coordinates Transformation ◽  
Detection Speed

In this paper, we introduce a new method for ellipse detection. For any object has closed curve in a digital image, it is easy to calculate the centroid of the object. We assume the object is an ellipse, and then by rotating, scaling this object, it can be transformed to a circle. So, ellipse detection problem becomes circle detection problem. Compared with other detection methods, our method only need process border points of the object, hence has higher detection speed.

Road crack detection and quantification based on segmentation network using architecture of matrix

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Gang Li ◽  
Yongqiang Chen ◽  
Jian Zhou ◽  
Xuan Zheng ◽  
Xue Li
Keyword(s):  
Crack Detection ◽  
Detection Method ◽  
Detection Methods ◽  
Content Type ◽  
The Road ◽  
Detection Algorithms ◽  
Model Training ◽  
Relative Errors ◽  
Detection Effect ◽  
Better Than

PurposePeriodic inspection and maintenance are essential for effective pavement preservation. Cracks not only affect the appearance of the road and reduce the levelness, but also shorten the life of road. However, traditional road crack detection methods based on manual investigations and image processing are costly, inefficiency and unreliable. The research aims to replace the traditional road crack detection method and further improve the detection effect.Design/methodology/approachIn this paper, a crack detection method based on matrix network fusing corner-based detection and segmentation network is proposed to effectively identify cracks. The method combines ResNet 152 with matrix network as the backbone network to achieve feature reuse of the crack. The crack region is identified by corners, and segmentation network is constructed to extract the crack. Finally, parameters such as the length and width of the cracks were calculated from the geometric characteristics of the cracks and the relative errors with the actual values were 4.23 and 6.98% respectively.FindingsTo improve the accuracy of crack detection, the model was optimized with the Adam algorithm and mixed with two publicly available datasets for model training and testing and compared with various methods. The results show that the detection performance of our method is better than many excellent algorithms, and the anti-interference ability is strong.Originality/valueThis paper proposed a new type of road crack detection method. The detection effect is better than a variety of detection algorithms and has strong anti-interference ability, which can completely replace traditional crack detection methods and meet engineering needs.

Image Acquisition for Pressed Protuberant Characters on Metal Label

2011 ◽  
Vol 219-220 ◽  
pp. 170-173
Author(s):  
Guo Ping Li ◽  
Hua Guan Liu ◽  
Chang Sheng Ai
Keyword(s):  
Image Acquisition ◽  
Ccd Camera ◽  
Input Image ◽  
Image Contrast ◽  
Binary Image ◽  
Image Preprocessing ◽  
Binary Images ◽  
Novel Method ◽  
The Difference ◽  
Better Than

The pressed protuberant characters on metal label are the difference of reflectance. It is very difficult to obtain the character of full binary image directly. It has presented a novel method of image acquisition on metal label pressed protuberant character based on moiré contour. At first, the principles of moiré contour were analyzed; Secondly, the experiment parameters were designed by using of shadowing moiré equipment and pressed characters’ height. The moiré image of metal label is captured through experiment by using of CCD camera. The binary image of pressed characters was output using moiré fringe image modulated by the characters’ height as input image contrast enhancing, complement, middle filter, automatic threshold etc. The binary image of pressed character was obtained using image preprocessing. At last, the binary images of moiré contour obtained and direct obtained were compared. The experiments show that the binary image of moiré contour obtained is better than direct obtained image.

LOW-LATENCY CONNECTED COMPONENT LABELING USING AN FPGA

2010 ◽  
Vol 21 (03) ◽  
pp. 405-425 ◽  
Author(s):  
YASUAKI ITO ◽  
KOJI NAKANO
Keyword(s):  
Binary Image ◽  
Connected Components ◽  
Low Latency ◽  
Connected Component ◽  
Binary Images ◽  
Connected Component Labeling ◽  
Labeling Algorithm ◽  
Internal Storage

Connected component labeling is a process that assigns unique labels to the connected components of a binary image. The main contribution of this paper is to present a low-latency hardware connected component labeling algorithm for k-concave binary images designed and implemented in FPGA. Pixels of a binary image are given to the FPGA in raster order, and the resulting labels are also output in the same order. The advantage of our labeling algorithm is low latency and to use a small internal storage of the FPGA. We have implemented our hardware labeling algorithm in an Altera Stratix Family FPGA, and evaluated the performance. The implementation result shows that for a 10-concave binary image of 2048 × 2048, our connected component labeling algorithm runs in approximately 70ms and its latency is approximately 750µs.

scholarly journals Unsupervised Change Detection Driven by Floating References: A Pattern Analysis Approach

2021 ◽  
Author(s):  
RG Negri ◽  
Alejandro Frery
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Pattern Analysis ◽  
Detection Method ◽  
Simulated Data ◽  
Detection Methods ◽  
Practical Significance ◽  
Significance Level ◽  
Natural Factors ◽  
Better Than

© 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature. The Earth’s environment is continually changing due to both human and natural factors. Timely identification of the location and kind of change is of paramount importance in several areas of application. Because of that, remote sensing change detection is a topic of great interest. The development of precise change detection methods is a constant challenge. This study introduces a novel unsupervised change detection method based on data clustering and optimization. The proposal is less dependent on radiometric normalization than classical approaches. We carried experiments with remote sensing images and simulated datasets to compare the proposed method with other unsupervised well-known techniques. At its best, the proposal improves by 50% the accuracy concerning the second best technique. Such improvement is most noticeable with uncalibrated data. Experiments with simulated data reveal that the proposal is better than all other compared methods at any practical significance level. The results show the potential of the proposed method.

scholarly journals Unsupervised Change Detection Driven by Floating References: A Pattern Analysis Approach

2021 ◽  
Author(s):  
RG Negri ◽  
Alejandro Frery
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Pattern Analysis ◽  
Detection Method ◽  
Simulated Data ◽  
Detection Methods ◽  
Practical Significance ◽  
Significance Level ◽  
Natural Factors ◽  
Better Than

© 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature. The Earth’s environment is continually changing due to both human and natural factors. Timely identification of the location and kind of change is of paramount importance in several areas of application. Because of that, remote sensing change detection is a topic of great interest. The development of precise change detection methods is a constant challenge. This study introduces a novel unsupervised change detection method based on data clustering and optimization. The proposal is less dependent on radiometric normalization than classical approaches. We carried experiments with remote sensing images and simulated datasets to compare the proposed method with other unsupervised well-known techniques. At its best, the proposal improves by 50% the accuracy concerning the second best technique. Such improvement is most noticeable with uncalibrated data. Experiments with simulated data reveal that the proposal is better than all other compared methods at any practical significance level. The results show the potential of the proposed method.

scholarly journals Identification of Spikes in Time Series

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Dana E. Goin ◽  
Jennifer Ahern
Keyword(s):  
Time Series ◽  
Sensitivity And Specificity ◽  
Kalman Filtering ◽  
Detection Method ◽  
Detection Methods ◽  
Spike Detection ◽  
Overall Performance ◽  
The Mean ◽  
Arima Modeling ◽  
Smoothing Procedure

Abstract Researchers interested in the effects of exposure spikes on an outcome need tools to identify unexpectedly high values in a time series. However, the best method to identify spikes in time series is not known. This paper aims to fill this gap by testing the performance of several spike detection methods in a simulation setting. We created simulations parameterized by monthly violence rates in nine California cities that represented different series features, and randomly inserted spikes into the series. We then compared the ability to detect spikes of the following methods: ARIMA modeling, Kalman filtering and smoothing, wavelet modeling with soft thresholding, and an iterative outlier detection method. We varied the magnitude of spikes from 10 to 50 % of the mean rate over the study period and varied the number of spikes inserted from 1 to 10. We assessed performance of each method using sensitivity and specificity. The Kalman filtering and smoothing procedure had the best overall performance. We applied each method to the monthly violence rates in nine California cities and identified spikes in the rate over the 2005–2012 period.

scholarly journals STATISTICAL ANOMALY DETECTION FOR MONITORING OF HUMAN DYNAMICS

2015 ◽  
Vol XL-4/W5 ◽  
pp. 93-98
Author(s):  
K. Kamiya ◽  
T. Fuse
Keyword(s):  
Anomaly Detection ◽  
Traffic Control ◽  
Time Series Data ◽  
Detection Method ◽  
Population Data ◽  
Detection Methods ◽  
Series Data ◽  
Location Information ◽  
Detection Problem ◽  
Human Dynamics

Understanding of human dynamics has drawn attention to various areas. Due to the wide spread of positioning technologies that use GPS or public Wi-Fi, location information can be obtained with high spatial-temporal resolution as well as at low cost. By collecting set of individual location information in real time, monitoring of human dynamics is recently considered possible and is expected to lead to dynamic traffic control in the future. Although this monitoring focuses on detecting anomalous states of human dynamics, anomaly detection methods are developed ad hoc and not fully systematized. This research aims to define an anomaly detection problem of the human dynamics monitoring with gridded population data and develop an anomaly detection method based on the definition. According to the result of a review we have comprehensively conducted, we discussed the characteristics of the anomaly detection of human dynamics monitoring and categorized our problem to a semi-supervised anomaly detection problem that detects contextual anomalies behind time-series data. We developed an anomaly detection method based on a sticky HDP-HMM, which is able to estimate the number of hidden states according to input data. Results of the experiment with synthetic data showed that our proposed method has good fundamental performance with respect to the detection rate. Through the experiment with real gridded population data, an anomaly was detected when and where an actual social event had occurred.

scholarly journals Development of an Autonomous Electric Robot Implement for Intra-Row Weeding in Vineyards

Agriculture ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 18 ◽  
Author(s):  
David Reiser ◽  
El-Sayed Sehsah ◽  
Oliver Bumann ◽  
Jörg Morhard ◽  
Hans Griepentrog
Keyword(s):  
Power Consumption ◽  
Autonomous Navigation ◽  
Laser Scanner ◽  
Detection Methods ◽  
Hydraulic Motor ◽  
Mechanical Parts ◽  
Sonar Sensor ◽  
Overall Performance ◽  
Conventional Tool ◽  
Better Than

Intra-row weeding is a time consuming and challenging task. Therefore, a rotary weeder implement for an autonomous electrical robot was developed. It can be used to remove the weeds of the intra-row area of orchards and vineyards. The hydraulic motor of the conventional tool was replaced by an electric motor and some mechanical parts were refabricated to reduce the overall weight. The side shift, the height and the tilt adjustment were performed by linear electric motors. For detecting the trunk positions, two different methods were evaluated: A conventional electromechanical sensor (feeler) and a sonar sensor. The robot performed autonomous row following based on two dimensional laser scanner data. The robot prototype was evaluated at a forward speed of 0.16 ms−1 and a working depth of 40 mm. The overall performance of the two different trunk detection methods was tested and evaluated for quality and power consumption. The results indicated that an automated intra-row weeding robot could be an alternative solution to actual machinery. The overall performance of the sonar was better than the adjusted feeler in the performed tests. The combination of autonomous navigation and weeding could increase the weeding quality and decrease power consumption in future.

Sign in / Sign up

Export Citation Format

Share Document