ACCUMULATIVE INTERSECTION SPACE BASED CORNER DETECTION ALGORITHM

2008 ◽  
Vol 22 (08) ◽  
pp. 1559-1586 ◽  
Author(s):  
NA LU ◽  
ZUREN FENG
Keyword(s):  
Monte Carlo ◽  
Detection Method ◽  
Detection Algorithm ◽  
Search Method ◽  
The Other ◽  
Corner Detection ◽  
Detection Methods ◽  
Local Maxima ◽  
Edge Detectors ◽  
Theory And Experiments

There is no parametric formulation of corner, so the conventional Hough transform cannot be employed to detect corners directly. A random corner detection method is developed in this paper based on a new concept "accumulative intersection space" under Monte Carlo scheme. This method transforms the corner detection in the image space into local maxima localization in the accumulative intersection space where the intersections are accumulated by random computations. The proposed algorithm has been demonstrated by both theory and experiments. The proposed algorithm is isotropic, robust to image rotation, insensitive to noise and false corners on diagonal edges. Unlike the other existing contour based corner detection methods, our algorithm can effectively avoid the influence of the edge detectors, such as rounding corners or line interceptions. Extensive comparisons among our approach and the other detectors including Harris operator, Fei Shen and Han Wang detector, Han Wang and Brady detector, Foveated Visual Search method and SIFT feature, have shown the effectiveness of our method.

scholarly journals A multiple pattern complex event detection scheme based on decomposition and merge sharing for massive event streams

2020 ◽  
Vol 16 (10) ◽  
pp. 155014772096133
Author(s):  
Jianhua Wang ◽  
Bang Ji ◽  
Feng Lin ◽  
Shilei Lu ◽  
Yubin Lan ◽  
...  
Keyword(s):  
Event Detection ◽  
Detection Method ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Detection Scheme ◽  
Event Stream ◽  
Detection Model ◽  
Efficient Detection ◽  
High Efficient ◽  
Complex Events

Quickly detecting related primitive events for multiple complex events from massive event stream usually faces with a great challenge due to their single pattern characteristic of the existing complex event detection methods. Aiming to solve the problem, a multiple pattern complex event detection scheme based on decomposition and merge sharing is proposed in this article. The achievement of this article lies that we successfully use decomposition and merge sharing technology to realize the high-efficient detection for multiple complex events from massive event streams. Specially, in our scheme, we first use decomposition sharing technology to decompose pattern expressions into multiple subexpressions, which can provide many sharing opportunities for subexpressions. We then use merge sharing technology to construct a multiple pattern complex events by merging sharing all the same prefix, suffix, or subpattern into one based on the above decomposition results. As a result, our proposed detection method in this article can effectively solve the above problem. The experimental results show that the proposed detection method in this article outperforms some general detection methods in detection model and detection algorithm in multiple pattern complex event detection as a whole.

Improved FAST Corner Detection Based on Harris Algorithm for Chinese Characters

2013 ◽  
Vol 850-851 ◽  
pp. 767-770 ◽  
Author(s):  
Na Yao ◽  
Tie Cheng Bai ◽  
Jie Chen
Keyword(s):  
Feature Extraction ◽  
Fast Algorithm ◽  
Detection Rate ◽  
Detection Method ◽  
Corner Detection ◽  
Detection Methods ◽  
Chinese Characters ◽  
Line Segments ◽  
Running Time ◽  
Shape Feature Extraction

According to the characteristics of Chinese characters image, we propose an improved corner detection method based on FAST algorithm and Harris algorithm to improve detection rate and shorten the running time for next feature extraction in this paper. The image of Chinese characters is detected for corners using FAST algorithm Firstly. Second, computing corner response function (CRF) of Harris algorithm, false corners are removed. The corners founded lastly are the endpoints of line segments, providing the length of line segments for shape feature extraction. The proposed method is compared with several corner detection methods over a number of images. Experimental results show that the proposed method shows better performance in terms of detection rate and running time.

WAVELET-BASED EDGE DETECTION IN DIGITAL IMAGES

2008 ◽  
Vol 08 (04) ◽  
pp. 513-533 ◽  
Author(s):  
MUHAMMAD HUSSAIN ◽  
TURGHUNJAN ABDUKIRIM ◽  
YOSHIHIRO OKADA
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
Local Maxima ◽  
Wavelet Filters ◽  
Dyadic Wavelet ◽  
Edge Detectors ◽  
Frame Work ◽  
Dyadic Wavelet Transform ◽  
Edge Detection Method ◽  
Better Than

This paper proposes a wavelet based multilevel edge detection method that exploits spline dyadic wavelets and a frame work similar to that of Canny's edge detector.2 Using the recently proposed dyadic lifting schemes by Turghunjan et al.1 spline dyadic wavelet filters have been constructed, which are characterized by higher order of regularity and have the potential of better inherent noise filtering and detection results. Edges are determined as the local maxima in the subbands at different scales of the dyadic wavelet transform. Comparison reveals that our method performs better than Mallat's and Canny's edge detectors.

Yawning Detection Based on Mouth Feature Points Curve Fitting

2012 ◽  
Vol 605-607 ◽  
pp. 2227-2231
Author(s):  
Wu Yang Ding ◽  
Ling Zhang ◽  
Yun Hua Chen
Keyword(s):  
Mathematical Model ◽  
Real Time ◽  
Curve Fitting ◽  
Detection Method ◽  
Detection Algorithm ◽  
Corner Detection ◽  
Feature Points ◽  
Harris Corner ◽  
Fatigue Monitoring ◽  
Harris Corner Detection

A yawning detection method which can be used in drivers’ fatigue monitoring is proposed. To adapt to the variance in different mouth shapes and sizes, it based on mouth inner contour corner detection and curve fitting. First, the Harris corner detection algorithm was used to detect inner mouth feature points. Second, we established the open mouths’ mathematical model by curve fitting those points, calculated the degree of mouth openness using the mouth model, and generated the real-time M-curve. Third, the duration of big openness in successive images is divided into levels for further judgment. The validation results show that the method can obtain more precise mouth parameters and distinguish yawn from complex mouth activities. So the method achieves a higher level of accuracy.

A NEW EDGE DETECTION METHOD THROUGH TEMPLATE MATCHING

1994 ◽  
Vol 08 (04) ◽  
pp. 899-917 ◽  
Author(s):  
MAO-JIUN J. WANG ◽  
SHIAU-CHYI CHANG ◽  
CHIH-MING LIU ◽  
WEN-YEN WU
Keyword(s):  
Edge Detection ◽  
Template Matching ◽  
Pattern Analysis ◽  
Detection Method ◽  
Performance Criteria ◽  
Detection Methods ◽  
Edge Detector ◽  
Edge Detectors ◽  
Edge Detection Method ◽  
Single Pixel

This paper reviews some gradient edge detection methods and proposes a new detector — the template matching edge detector (TMED). This detector utilizes the concepts of pattern analysis and the template matching of 3×3 masks. A set of performance criteria was used to evaluate the gradient edge detectors as well as the template matching edge detector. The results indicate that the new method is superior to the other gradient edge detectors. In addition, the template matching edge detector has also demonstrated good performance on noisy images. It can obtain very precise edge detection of single pixel width.

scholarly journals Flame Detection Based on Deep Learning Using HSV Color Model and Corner Detection Algorithm

2021 ◽  
Vol 35 (2) ◽  
pp. 108-114
Author(s):  
Jin-Kyu Ryu ◽  
Dong-Kurl Kwak
Keyword(s):  
Deep Learning ◽  
Detection Method ◽  
Detection Algorithm ◽  
Corner Detection ◽  
Color Model ◽  
Actual Performance ◽  
Harris Corner ◽  
Hsv Color Model ◽  
Flame Detection ◽  
Harris Corner Detection

Recently, many image classification or object detection models that use deep learning techniques have been studied; however, in an actual performance evaluation, flame detection using these models may achieve low accuracy. Therefore, the flame detection method proposed in this study is image pre-processing with HSV color model conversion and the Harris corner detection algorithm. The application of the Harris corner detection method, which filters the output from the HSV color model, allows the corners to be detected around the flame owing to the rough texture characteristics of the flame image. These characteristics allow for the detection of a region of interest where multiple corners occur, and finally classify the flame status using deep learning-based convolutional neural network models. The flame detection of the proposed model in this study showed an accuracy of 97.5% and a precision of 97%.

scholarly journals A Framework to Test Resistency of Detection Algorithms for Stepping-Stone Intrusion on Time-Jittering Manipulation

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Lixin Wang ◽  
Jianhua Yang ◽  
Michael Workman ◽  
Peng-Jun Wan
Keyword(s):  
Data Stream ◽  
Detection Method ◽  
Detection Algorithm ◽  
Efficient Algorithms ◽  
Detection Methods ◽  
The Internet ◽  
Stepping Stones ◽  
Stepping Stone ◽  
Detection Algorithms ◽  
A Chain

Hackers on the Internet usually send attacking packets using compromised hosts, called stepping-stones, in order to avoid being detected and caught. With stepping-stone attacks, an intruder remotely logins these stepping-stones using programs like SSH or telnet, uses a chain of Internet hosts as relay machines, and then sends the attacking packets. A great number of detection approaches have been developed for stepping-stone intrusion (SSI) in the literature. Many of these existing detection methods worked effectively only when session manipulation by intruders is not present. When the session is manipulated by attackers, there are few known effective detection methods for SSI. It is important to know whether a detection algorithm for SSI is resistant on session manipulation by attackers. For session manipulation with chaff perturbation, software tools such as Scapy can be used to inject meaningless packets into a data stream. However, to the best of our knowledge, there are no existing effective tools or efficient algorithms to produce time-jittered network traffic that can be used to test whether an SSI detection method is resistant on intruders’ time-jittering manipulation. In this paper, we propose a framework to test resistency of detection algorithms for SSI on time-jittering manipulation. Our proposed framework can be used to test whether an existing or new SSI detection method is resistant on session manipulation by intruders with time-jittering.

Spam detection using semantic web in mail services

2018 ◽  
Vol 7 (2.7) ◽  
pp. 44
Author(s):  
S Bhavika ◽  
B Prema Sindhuri ◽  
G Bhavana
Keyword(s):  
Semantic Web ◽  
Detection Method ◽  
The Other ◽  
Detection Methods ◽  
Spam Detection ◽  
Daily Lives ◽  
Do So

Electronic mails have become a part of our daily lives to exchange different type of information and messages. They provide a great medium to communicate with large number of people in a single stretch. This made so many marketing groups to think that email is a great platform for publicizing their goods or products. Not only are these marketers there so many other types of users who wants to make use of these emails for their own needs. As the time prolongs, this had become a problem for the other users because of the continuous undesired electronic messages sent by different marketing and some other unauthorized users. These messages are termed as spam messages. These spam mails have become a serious issue and there is a need to clear away all these junk mails. To do so different spam detection methodologies are developed and employed for providing an effective mailing service to the users. In this paper, we present various spam detection methods that are existing and also finding the accurate, effective and reliable spam detection method.

A Novel High Accuracy Sub-Pixel Corner Detection Algorithm for Camera Calibration

2012 ◽  
Vol 239-240 ◽  
pp. 713-716 ◽  
Author(s):  
Fang Jie Yu ◽  
Xin Luan ◽  
Da Lei Song ◽  
Xiu Fang Li ◽  
Hong Hong Zhou
Keyword(s):  
Response Function ◽  
Camera Calibration ◽  
Detection Algorithm ◽  
High Accuracy ◽  
Center Of Gravity ◽  
Corner Detection ◽  
Detection Methods ◽  
Robust Performance ◽  
Harris Detector ◽  
Candidate Regions

This paper presents a novel sub-pixel corner detection algorithm for camera calibration. In order to achieve high accuracy and robust performance, the pixel level candidate regions are firstly identified by Harris detector. Within these regions, the center of gravity (COG) method is used to gain sub-pixel corner detection. Instead of using the intensity value of the regions, we propose to use corner response function (CRF) as the distribution of the weights of COG. The results of camera calibration experiments show that the proposed algorithm is more accurate and robust than traditional COG sub-pixel corner detection methods.

scholarly journals Solder Joint Defect Detection in the Connectors Using Improved Faster-RCNN Algorithm

2021 ◽  
Vol 11 (2) ◽  
pp. 576
Author(s):  
Kaihua Zhang ◽  
Haikuo Shen
Keyword(s):  
Solder Joint ◽  
Defect Detection ◽  
Data Augmentation ◽  
Detection Method ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Data Set ◽  
Electronic Products ◽  
Original Algorithm

The miniaturization and high integration of electronic products have higher and higher requirements for welding of internal components of electronic products. A welding quality detection method has always been one of the important research contents in the industry, among which, the research on solder joint defect detection of a connector has gradually attracted people’s attention with the development of image detection algorithm. The traditional solder joint detection method of connector adopts manual detection or automatic detection methods, which is inefficient and not safe enough. With the development of deep learning, the application of a deep convolutional neural network to target detection has become a research hotspot. In this paper, a data set of connector solder joint samples was made and the number of image samples was expanded to more than 3 times of the original by using data augmentation. Clustering generates anchor boxes and transfer learning with ResNet-101 were fused, so an improved faster region-based convolutional neural networks (Faster RCNN) algorithm was proposed. The experiment verified that the improved algorithm proposed in this paper had a great improvement in all aspects compared with the original algorithm. The average detection accuracy of this method can reach 94%, and the detection rate of some defects can even reach 100%, which can completely meet the industrial requirements.

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