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 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 Image Mosaic Method Based on SIFT Features of Line Segment

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Jun Zhu ◽  
Mingwu Ren
Keyword(s):  
Line Segment ◽  
Corner Detection ◽  
Image Mosaic ◽  
Scale Invariant ◽  
Point Matching ◽  
Lighting Condition ◽  
Harris Corner ◽  
Key Points ◽  
Scale Invariant Feature ◽  
Harris Corner Detection

This paper proposes a novel image mosaic method based on SIFT (Scale Invariant Feature Transform) feature of line segment, aiming to resolve incident scaling, rotation, changes in lighting condition, and so on between two images in the panoramic image mosaic process. This method firstly uses Harris corner detection operator to detect key points. Secondly, it constructs directed line segments, describes them with SIFT feature, and matches those directed segments to acquire rough point matching. Finally, Ransac method is used to eliminate wrong pairs in order to accomplish image mosaic. The results from experiment based on four pairs of images show that our method has strong robustness for resolution, lighting, rotation, and scaling.

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 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 An improved corner detection algorithm used in video statistics

2018 ◽  
Vol 13 ◽  
pp. 174830181881348
Author(s):  
Sun Wanchun ◽  
Zhang Jianxun
Keyword(s):  
Linear Model ◽  
Detection Algorithm ◽  
Large Error ◽  
Corner Detection ◽  
Current Frame ◽  
Order Linear ◽  
Population Statistics ◽  
First Order ◽  
Time Operation ◽  
Harris Corner

In order to address the difficult problem to determine the number of populations, this paper improves the algorithm based on the Harris point detection algorithm, and the number of people is returned through the first-order linear regression model. First of all, according to the shortcomings of Harris corner algorithm in population statistics, an adaptive gray difference idea is proposed, and the concept of integral image is introduced to overcome its defects in noise immunity and real-time operation. Secondly, in view of the large error generated in the process of population statistics in the first-order static model, a dynamic linear model regression method is proposed. In this method, it is believed that there is certain proportionality coefficient between each frame of corner points and the number of people with the change of time, and this coefficient has certain correlation with the angle points in the previous frame and current frame. At the same time, in order to eliminate the number of redundant corners generated in the corner statistics process, the frame difference method is used to filter the stationary point. Finally, the number of people is returned through first-order linear model.

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 Detection of Facial Features in Scale-Space

10.14311/948 ◽  
2007 ◽  
Vol 47 (4-5) ◽  
Author(s):  
P. Hosten ◽  
M. Asbach
Keyword(s):  
Nearest Neighbor ◽  
Feature Space ◽  
Scale Space ◽  
Facial Features ◽  
Training Process ◽  
Interest Points ◽  
New Approach ◽  
Harris Corner ◽  
Harris Corner Detector ◽  
Karhunen Loeve Transform

This paper presents a new approach to the detection of facial features. A scale adapted Harris Corner detector is used to find interest points in scale-space. These points are described by the SIFT descriptor. Thus invariance with respect to image scale, rotation and illumination is obtained. Applying a Karhunen-Loeve transform reduces the dimensionality of the feature space. In the training process these features are clustered by the k-means algorithm, followed by a cluster analysis to find the most distinctive clusters, which represent facial features in feature space. Finally, a classifier based on the nearest neighbor approach is used to decide whether the features obtained from the interest points are facial features or not. 

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