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.

An Improved Harris Corner Detection Algorithm for Noised Images

2012 ◽  
Vol 433-440 ◽  
pp. 6151-6156 ◽  
Author(s):  
Xin Zhang ◽  
Xiu Hua Ji
Keyword(s):  
Image Registration ◽  
Detection Algorithm ◽  
Experimental Results ◽  
Corner Detection ◽  
Detection Accuracy ◽  
Image Mosaic ◽  
Harris Corner ◽  
Noise Impact ◽  
Corner Points ◽  
Harris Corner Detection

The Harris corner detection algorithm is widely applied in image mosaic, which is simple and stable. However, the algorithm has a disadvantage that it obtains a lot of false corners when there exist some noise in an image. An improved Harris corner detection algorithm is proposed in this paper. The new algorithm reduces the noise impact greatly. The experimental results show that the improved algorithm not only reduces false corner points greatly, but also retain the majority of true corners. As a result, it improves the detection accuracy and reduces the chance of error matching in image registration.

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 DETECTING FEATURES VIA MODIFIED HARRIS CORNERS AND MATCHING THEM THROUGH SIFT

2020 ◽  
Author(s):  
Jimut Bahan Pal
Keyword(s):  
Noise Intensity ◽  
Scale Invariant ◽  
Sift Algorithm ◽  
Invariant Feature ◽  
Harris Corner ◽  
Key Points ◽  
Daunting Task ◽  
Feature Transform ◽  
Scale Invariant Feature ◽  
Robust To Noise

Interpreting images spatially is a daunting task which is achieved by detecting corners and features.The most important task of detecting features is achieved by Harris Corner Algorithm. The algorithmis not robust to different scale of the same image. The algorithm may detect corner but when theimage is zoomed in, the corner may appear as ridges. We use the corners detected from HarrisCorner algorithm and treat these as key points to pass into Scale Invariant Feature Transform (SIFT)algorithm. The SIFT algorithm extracts descriptor vector of dimension 128 X 1 from these cornersand can be used to find similarity between different images. This process is quite robust to noise,intensity, scale and occlusion and is used for matching images from a database of descriptors. Wehave investigated both the algorithms in this paper and made a modified version of Harris Corneralgorithm by performing different kind of thresholding, both of them gave a little different result.

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