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. 

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.

A fast approximate Harris corner detector

2009 ◽  
Author(s):  
Bing Han ◽  
Jiyin Sun ◽  
Jing Liu
Keyword(s):  
Harris Corner ◽  
Harris Corner Detector

An Approach of Wavefront Identification for Traveling Wave Fault Location Based on Harris Corner Detector

2014 ◽  
Vol 960-961 ◽  
pp. 1100-1103
Author(s):  
Guang Bin Zhang ◽  
Hong Chun Shu ◽  
Ji Lai Yu
Keyword(s):  
Traveling Waves ◽  
Transmission Lines ◽  
Traveling Wave ◽  
Fault Location ◽  
Corner Point ◽  
Harris Corner ◽  
Corner Points ◽  
Simulated Test ◽  
Harris Corner Detector ◽  
Calculated Distance

Wavefront identification is important for traveling based fault location. In order to improve its reliability, a novel wavefront identification method based on Harris corner detector has been proposed in this paper. The principle of single-ended traveling wave fault location was briefly introduced at first, and the features of wavefronts generated by faults on transmission lines were analyzed. The arrival of traveling waves' wavefronts is considered as corner points in digital image of waveshape. The corner points can be extracted precisely by Harris corner detector, and both false corner points and non-fault caused disturbance can be eliminated according to the calculated distance between two neighbour corner points and the angle of the corner point. The proposed method is proved feasible and effective by digital simulated test.

A new approach to scale-space image

1994 ◽  
Vol 15 (4) ◽  
pp. 365-372 ◽  
Author(s):  
Bimal Kumar Ray ◽  
Kumar S. Ray
Keyword(s):  
Scale Space ◽  
New Approach ◽  
Space Image

scholarly journals Localized probability of improvement for kriging based multi-objective optimization

Open Physics ◽  
2017 ◽  
Vol 15 (1) ◽  
pp. 954-958
Author(s):  
Yinjiang Li ◽  
Song Xiao ◽  
Paolo Di Barba ◽  
Mihai Rotaru ◽  
Jan K. Sykulski
Keyword(s):  
Uniform Distribution ◽  
Nearest Neighbor ◽  
Higher Dimensions ◽  
Multi Objective Optimization ◽  
New Approach ◽  
Multi Objective ◽  
Local Probability ◽  
Pareto Fronts ◽  
Probability Of Improvement ◽  
Infill Sampling

AbstractThe paper introduces a new approach to kriging based multi-objective optimization by utilizing a local probability of improvement as the infill sampling criterion and the nearest neighbor check to ensure diversification and uniform distribution of Pareto fronts. The proposed method is computationally fast and linearly scalable to higher dimensions.

Web Graph Clustering for Displays and Navigation of Cyberspace

Web Mining ◽  
2011 ◽  
pp. 253-275
Author(s):  
Xiaodi Huang ◽  
Wei Lai
Keyword(s):  
Data Mining ◽  
Nearest Neighbor ◽  
Structural Information ◽  
Graph Clustering ◽  
Data Sets ◽  
K Nearest Neighbor ◽  
New Approach ◽  
Web Graph

This chapter presents a new approach to clustering graphs, and applies it to Web graph display and navigation. The proposed approach takes advantage of the linkage patterns of graphs, and utilizes an affinity function in conjunction with the k-nearest neighbor. This chapter uses Web graph clustering as an illustrative example, and offers a potentially more applicable method to mine structural information from data sets, with the hope of informing readers of another aspect of data mining and its applications.

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