Ring Fusion of Fisheye Images Based on Corner Detection Algorithm for Around View Monitoring System of Intelligent Driving

In order to improve the visual effect of the around view monitor (AVM), we propose a novel ring fusion method to reduce the brightness difference among fisheye images and achieve a smooth transition around stitching seam. Firstly, an integrated corner detection is proposed to automatically detect corner points for image registration. Then, we use equalization processing to reduce the brightness among images. And we match the color of images according to the ring fusion method. Finally, we use distance weight to blend images around stitching seam. Through this algorithm, we have made a Matlab toolbox for image blending. 100% of the required corner is accurately and fully automatically detected. The transition around the stitching seam is very smooth, with no obvious stitching trace.

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An Improved Harris Corner Detection Algorithm for Noised Images

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.433-440.6151 ◽

2012 ◽

Vol 433-440 ◽

pp. 6151-6156 ◽

Cited By ~ 1

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.

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Implementation of Harris Corner Detection Algorithm for Volumetric Images

Journal of Intelligent Systems with Applications ◽

10.54856/jiswa.201805008 ◽

2018 ◽

pp. 18-22

Author(s):

Ceyda Nur Ozturk ◽

Songul Albayrak

Keyword(s):

Three Dimensional ◽

Scale Space ◽

Detection Algorithm ◽

Corner Detection ◽

Sample Object ◽

Harris Corner ◽

Corner Points ◽

Volumetric Images ◽

Third Dimension ◽

Harris Corner Detection

More effective detection of corner points in three dimensional (3-D) volumetric images can be possible through expansion of Harris corner detection algorithm, which run in two dimensional (2-D) images, into third dimension. In this study, the standard algorithm of Harris that detected corner points in 2-D slices and its 3-D version were implemented in the scale-space to determine the corner points of volumetric object images. The results obtained in sample object images with 2-D and 3-D methods that used different approaches for scale-space construction were qualitatively assessed.

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Vision Measurement for the Pose of Cannon Barrel

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.423-426.2530 ◽

2013 ◽

Vol 423-426 ◽

pp. 2530-2535

Author(s):

Jian Dong Su ◽

Xiu Sheng Duan ◽

Jing Xiao

Keyword(s):

Measuring Method ◽

Detection Algorithm ◽

Azimuth Angle ◽

Corner Detection ◽

Fire Control ◽

Vertical Angle ◽

Vision Measurement ◽

Extrinsic Parameter ◽

Harris Corner ◽

Corner Points

The pose measurement of cannon barrel (vertical angle and azimuth angle) is a difficulty and emphasis in the precision analysis of SPAAG fire control system. To solve this problem, this paper presents a contactless measuring method based on computer vision. Before measurement, fix a checked planar faceplate on the cannon barrel as a marker. Firstly, get the coordinate of X corner points using Harris Corner Detection Algorithm to calibrate the camera, and chalk up the cameras intrinsic parameters based on Extended Kalman Filter. Secondly, get the corner points from the image of test position, then calculate the extrinsic parameter matrix of the corresponding position combining with LSE algorithm. Finally, according to the motion model of cannon barrel and the position relative to the marker, derive the constraint equations between extrinsic parameter and vertical angle and azimuth angle, then figure out the two angles. Experiment validate the maneuverability and veracity of this method, and the results indicate that the measuring precision of this method is less than 1mil.

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Hybrid corner detection algorithm for brain magnetic resonance image registration

2011 4th International Conference on Biomedical Engineering and Informatics (BMEI) ◽

10.1109/bmei.2011.6098339 ◽

2011 ◽

Cited By ~ 3

Author(s):

Di Zhou ◽

Yiwen Gao ◽

Liuyi Lu ◽

Honghui Wang ◽

Yongming Li ◽

...

Keyword(s):

Magnetic Resonance ◽

Image Registration ◽

Magnetic Resonance Image ◽

Detection Algorithm ◽

Corner Detection ◽

Brain Magnetic Resonance Image

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A Novel Corner Detection Based on Improved SUSAN Model

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.128-129.469 ◽

2011 ◽

Vol 128-129 ◽

pp. 469-472

Author(s):

Zhi Hui Yang ◽

Xiao Lan Han ◽

Fang Fang Guo

Keyword(s):

Coordinate System ◽

High Efficiency ◽

Detection Algorithm ◽

Adaptive Threshold ◽

The Self ◽

The Other ◽

Corner Detection ◽

Rotate Coordinate System ◽

Corner Points ◽

Modified Algorithm

The paper proposes a novel corner detection algorithm based on classical Susan model. The improvement is given from two aspects. One is the choice of the self-adaptive threshold. The other is that we present a rotate coordinate system to find all possible corner points. Experiment result shows that the modified algorithm is robust and of high efficiency.

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