scholarly journals Robust and Efficient Corner Detector Using Non-Corners Exclusion

2020 ◽  
Vol 10 (2) ◽  
pp. 443 ◽  
Author(s):  
Tao Luo ◽  
Zaifeng Shi ◽  
Pumeng Wang
Keyword(s):  
Good Accuracy ◽  
Detection Efficiency ◽  
Corner Detection ◽  
Harris Corner ◽  
Harris Detector ◽  
Harris Corner Detector ◽  
Detection Quality ◽  
Point Detection ◽  
Better Than ◽  
Rule Out

Corner detection is a traditional type of feature point detection method. Among methods used, with its good accuracy and the properties of invariance for rotation, noise and illumination, the Harris corner detector is widely used in the fields of vision tasks and image processing. Although it possesses a good performance in detection quality, its application is limited due to its low detection efficiency. The efficiency is crucial in many applications because it determines whether the detector is suitable for real-time tasks. In this paper, a robust and efficient corner detector (RECD) improved from Harris corner detector is proposed. First, we borrowed the principle of the feature from accelerated segment test (FAST) algorithm for corner pre-detection, in order to rule out non-corners and retain many strong corners as real corners. Those uncertain corners are looked at as candidate corners. Second, the gradients are calculated in the same way as the original Harris detector for those candidate corners. Third, to reduce additional computation amount, only the corner response function (CRF) of the candidate corners is calculated. Finally, we replace the highly complex non-maximum suppression (NMS) by an improved NMS to obtain the resulting corners. Experiments demonstrate that RECD is more competitive than some popular corner detectors in detection quality and speed. The accuracy and robustness of our method is slightly better than the original Harris detector, and the detection time is only approximately 8.2% of its original value.

scholarly journals Evaluating the Performance of Corner Detection Approaches for Features Extraction from UAV Images

2019 ◽  
Author(s):  
Abdulla Al-Rawabdeh ◽  
Ali Almagbile ◽  
Ahmad khawaldeh ◽  
Omar Aldayafleh ◽  
Mohammad Zeitoun ◽  
...  
Keyword(s):  
Threshold Value ◽  
Corner Detection ◽  
Threshold Values ◽  
Harris Corner ◽  
Harris Detector ◽  
Camera Position ◽  
The Common ◽  
Uav Images ◽  
The Relationship ◽  
Better Than

Many corner detector techniques have already been used in extracting information from UAV images to perform various photogrammetric and mapping activities. Among these techniques is the Feature from Accelerated Segment Test (FAST) and the Harris corner detector. It is widely agreed that the evaluation of detectors is of great importance because it evaluates and enhances the accuracy of the detected features. This research evaluates the performance of FAST-9 and FAST-12 as well as the Harris detector in terms of the repeatability rate, completeness, and correctness under different threshold values. Each method is evaluated in terms of its ability for detection UAV objects (crowd and cars features). Then the common detected features between both FAST versions and the Harris detector are extracted. This is to determine which method performs best under different image conditions (e.g., illumination variations, camera position and orientation, and image noise). The results show that the size of the threshold plays a crucial role in determining the number of detected feature points. An increase in the threshold value leads to a decrease in the number of detected points and vice versa. Thus, the correctness decreases whereas the completeness increases as a function of the threshold values. Furthermore, the relationship between the FAST-9 and the Harris detector is slightly better than those between the FAST-12 and the Harris detector. This is because the number of common features between the FAST-9 and the Harris detector are relatively higher than those between the FAST-12 and the Harris detector.

scholarly journals An Improved Corner Detection Algorithm Based on Harris

2012 ◽  
Vol 6-7 ◽  
pp. 717-721 ◽  
Author(s):  
Zhao Yang Zeng ◽  
Zhi Qiang Jiang ◽  
Qiang Chen ◽  
Pan Feng He
Keyword(s):  
Computation Time ◽  
Detection Algorithm ◽  
Corner Detection ◽  
Test Results ◽  
Harris Corner ◽  
Texture Complexity ◽  
Harris Corner Detection ◽  
Better Than

In order to accurately extract corners from the image with high texture complexity, the paper analyzed the traditional corner detection algorithm based on gray value of image. Although Harris corner detection algorithm has higher accuracy, but there also exists the following problems: extracting false corners, the information of the corners is missing and computation time is a bit long. So an improved corner detection algorithm combined Harris with SUSAN corner detection algorithm is proposed, the new algorithm first use the Harris to detect corners of image, then use the SUSAN to eliminate the false corners. By comparing the test results show that the new algorithm to extract corners very effective, and better than the Harris algorithm in the performance of corner detection.

scholarly journals 5 Detection and Measurement of Displacement and Velocity of Single Moving Object in a Stationary Background

2018 ◽  
Vol 7 (1) ◽  
pp. 6
Author(s):  
Amr Reda. R. Almaddah ◽  
Tauseef Ahmad ◽  
Abdullah Dubai
Keyword(s):  
Vertical Direction ◽  
Moving Object ◽  
Corner Detection ◽  
Interest Points ◽  
Scale Invariant ◽  
Current Frame ◽  
Lighting Condition ◽  
Harris Corner ◽  
Independent Variable ◽  
Harris Corner Detector

The traditional Harris detector are sensitive to noise and resolution because without the property of scale invariant.  In this research, The Harris corner detector algorithm is improved, to work with multi resolution images, the technique has also been working with poor lighting condition by using histogram equalization technique. The work we have done addresses the issue of robustly detection of feature points, detected multiple of local features are characterized by the intensity changes in both horizontal and vertical direction which is called corner features.  The goal of this work is to detect the corner of an object through the Harris corner detector with multiple scale of the same image. The scale invariant property applied to the Harris algorithm for improving the corner detection performance in different resolution of the same image with the same interest point. The detected points represented by two independent variables (x, y) in a matrix (x, y) and the dependent variable f are called intensity of interest points. Through these independent variable, we get the displacement and velocity of object by subtracting independent variable f(x,y) at current frame from the previous location f ̀((x,) ̀(y,) ̀) of another frame. For further work, multiple of moving object environment have been taken consideration for developing algorithms.

scholarly journals Evaluation of accurate eye corner detection methods for gaze estimation

2014 ◽  
Vol 7 (3) ◽  
Author(s):  
Jose Javier Bengoechea ◽  
Juan J. Cerrolaza ◽  
Arantxa Villanueva ◽  
Rafael Cabeza
Keyword(s):  
Neural Network ◽  
High Performance ◽  
Low Cost ◽  
Corner Detection ◽  
Detection Methods ◽  
Gaze Estimation ◽  
Harris Corner ◽  
Feature Based ◽  
Harris Corner Detector ◽  
Iris Center

Accurate detection of iris center and eye corners appears to be a promising approach for low cost gaze estimation. In this paper we propose novel eye inner corner detection methods. Appearance and feature based segmentation approaches are suggested. All these methods are exhaustively tested on a realistic dataset containing images of subjects gazing at different points on a screen. We have demonstrated that a method based on a neural network presents the best performance even in light changing scenarios. In addition to this method, algorithms based on AAM and Harris corner detector present better accuracies than recent high performance face points tracking methods such as Intraface.

scholarly journals 5 Detection and Measurement of Displacement and Velocity of Single Moving Object in a Stationary Background

2018 ◽  
Vol 1 (1) ◽  
pp. 6
Author(s):  
Amr Reda. R. Almaddah ◽  
Tauseef Ahmad ◽  
Abdullah Dubai
Keyword(s):  
Vertical Direction ◽  
Moving Object ◽  
Corner Detection ◽  
Interest Points ◽  
Scale Invariant ◽  
Current Frame ◽  
Lighting Condition ◽  
Harris Corner ◽  
Independent Variable ◽  
Harris Corner Detector

The traditional Harris detector are sensitive to noise and resolution because without the property of scale invariant.  In this research, The Harris corner detector algorithm is improved, to work with multi resolution images, the technique has also been working with poor lighting condition by using histogram equalization technique. The work we have done addresses the issue of robustly detection of feature points, detected multiple of local features are characterized by the intensity changes in both horizontal and vertical direction which is called corner features.  The goal of this work is to detect the corner of an object through the Harris corner detector with multiple scale of the same image. The scale invariant property applied to the Harris algorithm for improving the corner detection performance in different resolution of the same image with the same interest point. The detected points represented by two independent variables (x, y) in a matrix (x, y) and the dependent variable f are called intensity of interest points. Through these independent variable, we get the displacement and velocity of object by subtracting independent variable f(x,y) at current frame from the previous location f ̀((x,) ̀(y,) ̀) of another frame. For further work, multiple of moving object environment have been taken consideration for developing algorithms.

scholarly journals A Modified Harris Corner Detection for Breast IR Image

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Chia-Yen Lee ◽  
Hao-Jen Wang ◽  
Chung-Ming Chen ◽  
Ching-Cheng Chuang ◽  
Yeun-Chung Chang ◽  
...  
Keyword(s):  
Histogram Equalization ◽  
Corner Detection ◽  
Feature Points ◽  
Low Contrast ◽  
Clustering Problem ◽  
Harris Corner ◽  
Image Contrast Enhancement ◽  
Harris Corner Detector ◽  
Intensity Resolution ◽  
Harris Corner Detection

Harris corner detectors, which depend on strong invariance and a local autocorrelation function, display poor detection performance for infrared (IR) images with low contrast and nonobvious edges. In addition, feature points detected by Harris corner detectors are clustered due to the numerous nonlocal maxima. This paper proposes a modified Harris corner detector that includes two unique steps for processing IR images in order to overcome the aforementioned problems. Image contrast enhancement based on a generalized form of histogram equalization (HE) combined with adjusting the intensity resolution causes false contours on IR images to acquire obvious edges. Adaptive nonmaximal suppression based on eliminating neighboring pixels avoids the clustered features. Preliminary results show that the proposed method can solve the clustering problem and successfully identify the representative feature points of IR breast images.

Study on a Real-Time Corner Detection Algorithm

2010 ◽  
Vol 159 ◽  
pp. 192-197
Author(s):  
Yong Fang Guo ◽  
Ming Yu ◽  
Yi Cai Sun
Keyword(s):  
Real Time ◽  
Computation Time ◽  
Detection Algorithm ◽  
Corner Detection ◽  
Test Results ◽  
Multigrid Algorithm ◽  
Autocorrelation Matrix ◽  
Harris Corner ◽  
Harris Corner Detector ◽  
Real Time Applications

Conventional Harris corner detector is a desirable detector but it requires significantly more computation time. For MIC detector proposed by Trajkovic, the minimal computational demands of its operator make it well-suited for real-time applications, however the Trajkovic’s operator responses too readily to certain diagonal edges. For this reason, the paper proposed a new corner detection algorithm. The new corner detection algorithm adopted multigrid algorithm and preprocessed the lower resolution revision of the original image to obtain the potential corners, subsequently used autocorrelation matrix to get the corner response function for the corresponding points of the potential corner. The test results indicate the new corner detection algorithm can decrease edge responses and the number of textural corners effectively. Furthermore, it can satisfy the demands of real-time applications.

scholarly journals 2D kinematics of massive stars near the Galactic Centre

2020 ◽  
Vol 500 (3) ◽  
pp. 3213-3239
Author(s):  
Mattia Libralato ◽  
Daniel J Lennon ◽  
Andrea Bellini ◽  
Roeland van der Marel ◽  
Simon J Clark ◽  
...  
Keyword(s):  
Moving Objects ◽  
Hubble Space Telescope ◽  
Massive Stars ◽  
Line Of Sight ◽  
Galactic Centre ◽  
Harsh Environment ◽  
Proper Motions ◽  
Sgr A ◽  
Better Than ◽  
Rule Out

ABSTRACT The presence of massive stars (MSs) in the region close to the Galactic Centre (GC) poses several questions about their origin. The harsh environment of the GC favours specific formation scenarios, each of which should imprint characteristic kinematic features on the MSs. We present a 2D kinematic analysis of MSs in a GC region surrounding Sgr A* based on high-precision proper motions obtained with the Hubble Space Telescope. Thanks to a careful data reduction, well-measured bright stars in our proper-motion catalogues have errors better than 0.5 mas yr−1. We discuss the absolute motion of the MSs in the field and their motion relative to Sgr A*, the Arches, and the Quintuplet. For the majority of the MSs, we rule out any distance further than 3–4 kpc from Sgr A* using only kinematic arguments. If their membership to the GC is confirmed, most of the isolated MSs are likely not associated with either the Arches or Quintuplet clusters or Sgr A*. Only a few MSs have proper motions, suggesting that they are likely members of the Arches cluster, in agreement with previous spectroscopic results. Line-of-sight radial velocities and distances are required to shed further light on the origin of most of these massive objects. We also present an analysis of other fast-moving objects in the GC region, finding no clear excess of high-velocity escaping stars. We make our astro-photometric catalogues publicly available.

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