scholarly journals Improved FAST corner-detection method

2019 ◽  
Vol 2019 (19) ◽  
pp. 5493-5497 ◽  
Author(s):  
Wenyi Xiong ◽  
Weiming Tian ◽  
Zhijun Yang ◽  
Xiaowei Niu ◽  
Xiangfei Nie
Keyword(s):  
Detection Method ◽  
Corner Detection

scholarly journals Efficient 3D stereo vision stabilization for multi-camera viewpoints

2019 ◽  
Vol 8 (3) ◽  
pp. 882-889
Author(s):  
Sharif Shah Newaj Bhuiyan ◽  
Othman O. Khalifa
Keyword(s):  
Stereo Vision ◽  
Linear Order ◽  
Spanning Tree ◽  
Detection Method ◽  
Corner Detection ◽  
Geometric Transformation ◽  
Image Stabilization ◽  
Tree Algorithm ◽  
Stabilization Process ◽  
Key Points

In this paper, an algorithm is developed in 3D Stereo vision to improve image stabilization process for multi-camera viewpoints. Finding accurate unique matching key-points using Harris Laplace corner detection method for different photometric changes and geometric transformation in images. Then improved the connectivity of correct matching pairs by minimizing the global error using spanning tree algorithm. Tree algorithm helps to stabilize randomly positioned camera viewpoints in linear order. The unique matching key-points will be calculated only once with our method. Then calculated planar transformation will be applied for real time video rendering. The proposed algorithm can process more than 200 camera viewpoints within two seconds.

Multi-feature SUSAN corner detection method

2005 ◽  
Author(s):  
Xun Wang ◽  
Jianqiu Jin ◽  
Yun Ling ◽  
Zhaoyi Jiang
Keyword(s):  
Detection Method ◽  
Corner Detection

Improved FAST Corner Detection Based on Harris Algorithm for Chinese Characters

2013 ◽  
Vol 850-851 ◽  
pp. 767-770 ◽  
Author(s):  
Na Yao ◽  
Tie Cheng Bai ◽  
Jie Chen
Keyword(s):  
Feature Extraction ◽  
Fast Algorithm ◽  
Detection Rate ◽  
Detection Method ◽  
Corner Detection ◽  
Detection Methods ◽  
Chinese Characters ◽  
Line Segments ◽  
Running Time ◽  
Shape Feature Extraction

According to the characteristics of Chinese characters image, we propose an improved corner detection method based on FAST algorithm and Harris algorithm to improve detection rate and shorten the running time for next feature extraction in this paper. The image of Chinese characters is detected for corners using FAST algorithm Firstly. Second, computing corner response function (CRF) of Harris algorithm, false corners are removed. The corners founded lastly are the endpoints of line segments, providing the length of line segments for shape feature extraction. The proposed method is compared with several corner detection methods over a number of images. Experimental results show that the proposed method shows better performance in terms of detection rate and running time.

Corner detection method based on wavelet transform

2001 ◽  
Author(s):  
Xiaoming Peng ◽  
Chengping Zhou ◽  
Mingyue Ding
Keyword(s):  
Wavelet Transform ◽  
Detection Method ◽  
Corner Detection

scholarly journals A NOVEL AND FAST CORNER DETECTION METHOD FOR SAR IMAGERY

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 605-608 ◽  
Author(s):  
N. Jiao ◽  
W. Kang ◽  
Y. Xiang ◽  
H. You
Keyword(s):  
Detection Method ◽  
Single Point ◽  
Corner Detection ◽  
Selection Strategy ◽  
Sar Images ◽  
Multiplicative Property ◽  
Processing Window ◽  
Center Point ◽  
Sar Imagery ◽  
Candidate Point

Corners play an important role on image processing, while it is difficult to detect reliable and repeatable corners in SAR images due to the complex property of SAR sensors. In this paper, we propose a fast and novel corner detection method for SAR imagery. First, a local processing window is constructed for each point. We use the local mean of a 3 x 3 mask to represent a single point, which is weighted by a Gaussian template. Then the candidate point is compared with 16 surrounding points in the processing window. Considering the multiplicative property of speckle noise, the similarity measure between the center point and the surrounding points is calculated by the ratio of their local means. If there exist more than M continuous points are different from the center point, then the candidate point is labelled as a corner point. Finally, a selection strategy is implemented by ranking the corner score and employing the non-maxima suppression method. Extreme situations such as isolated bright points are also removed. Experimental results on both simulated and real-world SAR images show that the proposed detector has a high repeatability and a low localization error, compared with other state-of-the-art detectors.

scholarly journals Grape Berry Detection and Size Measurement Based on Edge Image Processing and Geometric Morphology

Machines ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 233
Author(s):  
Lufeng Luo ◽  
Wentao Liu ◽  
Qinghua Lu ◽  
Jinhai Wang ◽  
Weichang Wen ◽  
...  
Keyword(s):  
Image Processing ◽  
Detection Method ◽  
Grape Berry ◽  
Corner Detection ◽  
Edge Image ◽  
Grape Berries ◽  
Berry Size ◽  
Average Accuracy ◽  
Different Types ◽  
Geometric Morphology

Counting grape berries and measuring their size can provide accurate data for robot picking behavior decision-making, yield estimation, and quality evaluation. When grapes are picked, there is a strong uncertainty in the external environment and the shape of the grapes. Counting grape berries and measuring berry size are challenging tasks. Computer vision has made a huge breakthrough in this field. Although the detection method of grape berries based on 3D point cloud information relies on scanning equipment to estimate the number and yield of grape berries, the detection method is difficult to generalize. Grape berry detection based on 2D images is an effective method to solve this problem. However, it is difficult for traditional algorithms to accurately measure the berry size and other parameters, and there is still the problem of the low robustness of berry counting. In response to the above problems, we propose a grape berry detection method based on edge image processing and geometric morphology. The edge contour search and the corner detection algorithm are introduced to detect the concave point position of the berry edge contour extracted by the Canny algorithm to obtain the best contour segment. To correctly obtain the edge contour information of each berry and reduce the error grouping of contour segments, this paper proposes an algorithm for combining contour segments based on clustering search strategy and rotation direction determination, which realizes the correct reorganization of the segmented contour segments, to achieve an accurate calculation of the number of berries and an accurate measurement of their size. The experimental results prove that our proposed method has an average accuracy of 87.76% for the detection of the concave points of the edge contours of different types of grapes, which can achieve a good edge contour segmentation. The average accuracy of the detection of the number of grapes berries in this paper is 91.42%, which is 4.75% higher than that of the Hough transform. The average error between the measured berry size and the actual berry size is 2.30 mm, and the maximum error is 5.62 mm, which is within a reasonable range. The results prove that the method proposed in this paper is robust enough to detect different types of grape berries.

scholarly journals A fast X-corner detection method based on block-search strategy

2019 ◽  
Vol 11 (3) ◽  
pp. 168781401983414
Author(s):  
Cai Meng ◽  
Qi Wang ◽  
Lingjie Wu ◽  
Shaoya Guan ◽  
Yao Wu ◽  
...  
Keyword(s):  
Search Strategy ◽  
Detection Method ◽  
Corner Detection ◽  
Image Size ◽  
Image Block ◽  
Fast Detection ◽  
Sampling Window ◽  
Sampling Windows ◽  
Searching Method ◽  
Detection Speed

In order to detect X-corner (or X-point) features more accurately and apace, this article presents a novel and fast detection method based on block-by-block search strategy. Unlike general pixel-by-pixel searching method, the sampling window is first moved along the image block-by-block to find the X-corner candidates rapidly keeping in view the four-step and min-step-distance constraints. During the motion, some overlap is kept between the adjacent sampling windows in order to ensure that all X-corners could have a chance to reside inside, avoiding the possibility of that some X-corners may locate on the edge. Moreover, labeling technology is adopted to prevent duplicate candidates. After the collection of X-corner candidates, the neighborhood variance and centrosymmetry constraints are used to exclude outliers, and the intersection lines is calculated as the sub-pixel position of true X-corner. The experimental results using synthetic and real images show that the presented method approximately takes just about 13 ms to detect 52 X-corners in an image size of 1024 × 768 on a computer having Intel Core i3 CPU at 3.6 GHz and 4GB RAM. The proposed method has faster detection speed compared with the latest methods such as ChESS, SC, and Micron Tracker system while possessing the same or higher detection precision.

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