An automatic boundary tracking algorithm for Rural Buildings Based on Row and Column Searching Method

Remote sensing image vectorization; edge enhancement; boundary tracking; Abstract. This paper researched and implemented remote sensing image vectorization process using semi-automated way. Remote sensing image can be vectored as the following five steps:vertical and horizontal edge enhancement, open operation, close operation, binarization, and finally the boundary tracking. The paper presented an improved tracking algorithm to avoid the shortcomings of traditional edge tracking algorithm. In our tracking algorithm, for image maps, we first use open operation and then use close operation to remove noise.This vectorization method changed the traditional data collection mode and improved data collection efficiency.

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Achieving the Measurement of Tool Diameter by the Boundary Tracing Algorithm Based on Model

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.411.469 ◽

2011 ◽

Vol 411 ◽

pp. 469-473

Author(s):

Yue Shen Lai ◽

Meng Shi ◽

Jun Wei Tian ◽

Gang Cheng

Keyword(s):

Image Recognition ◽

Least Square Method ◽

Tracking Algorithm ◽

Least Square ◽

Image Measurement ◽

Boundary Tracking ◽

Tracing Algorithm ◽

Boundary Tracing ◽

Tool Diameter ◽

Tracing Method

Boundary tracing method is an important preprocessing instrument for image recognition and image measurement, but the traditional boundary tracing method exits a conflict between the veracity and the speed. In response to these problems, we propose the boundary tracking algorithm based on the model, and then obtain the tool diameter. Experiments show that the boundary tracking algorithm can achieve a good boundary track. Compared two algorithms of diameter measurements, we obtain that the least square method runs shorter, and more efficient.

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A Moving-Boundary Tracking Algorithm for Inviscid Compressible Flow

Hyperbolic Problems: Theory, Numerics, Applications ◽

10.1007/978-3-540-75712-2_104 ◽

2008 ◽

pp. 989-996 ◽

Cited By ~ 6

Author(s):

K. M. Shyue

Keyword(s):

Compressible Flow ◽

Moving Boundary ◽

Tracking Algorithm ◽

Boundary Tracking ◽

Inviscid Compressible Flow

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Robust long-term object tracking with adaptive scale and rotation estimation

International Journal of Advanced Robotic Systems ◽

10.1177/1729881420909736 ◽

2020 ◽

Vol 17 (2) ◽

pp. 172988142090973

Author(s):

Huimin Lu ◽

Dan Xiong ◽

Junhao Xiao ◽

Zhiqiang Zheng

Keyword(s):

Object Tracking ◽

Estimation Method ◽

Tracking Algorithm ◽

Correlation Filter ◽

Estimation Accuracy ◽

Continuous Space ◽

Searching Method ◽

Object Searching ◽

Rotation Estimation

In this article, a robust long-term object tracking algorithm is proposed. It can tackle the challenges of scale and rotation changes during the long-term object tracking for security robots. Firstly, a robust scale and rotation estimation method is proposed to deal with scale changes and rotation motion of the object. It is based on the Fourier–Mellin transform and the kernelized correlation filter. The object’s scale and rotation can be estimated in the continuous space, and the kernelized correlation filter is used to improve the estimation accuracy and robustness. Then a weighted object searching method based on the histogram and the variance is introduced to handle the problem that trackers may fail in the long-term object tracking (due to semi-occlusion or full occlusion). When the tracked object is lost, the object can be relocated in the whole image using the searching method, so the tracker can be recovered from failures. Moreover, two other kernelized correlation filters are learned to estimate the object’s translation and the confidence of tracking results, respectively. The estimated confidence is more accurate and robust using the dedicatedly designed kernelized correlation filter, which is employed to activate the weighted object searching module, and helps to determine whether the searching windows contain objects. We compare the proposed algorithm with state-of-the-art tracking algorithms on the online object tracking benchmark. The experimental results validate the effectiveness and superiority of our tracking algorithm.

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Metallographical Image Segmentation and Compression

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.152-154.276 ◽

2012 ◽

Vol 152-154 ◽

pp. 276-280 ◽

Cited By ~ 1

Author(s):

Qiu Dong Sun ◽

Shun Fu Gao ◽

Jiang Wei Huang ◽

Wei Chen

Keyword(s):

Image Processing ◽

Image Segmentation ◽

Grain Boundary ◽

Processing Method ◽

Tracking Algorithm ◽

Experimental Result ◽

Compression Method ◽

Boundary Tracking ◽

Compression Effect ◽

Double Threshold

The double-threshold binarization and morphological transform were applied to process the metallographical image. They could classify the grain and the grain boundary from gray metallographical image. Also, the eight-direction tracking techniques about Freeman chain encoding for metal metallographical compression had been discussed, and a grain boundary tracking algorithm was given. The experimental result shows that the proposed image processing method can segment grains and their boundaries efficiently. Freeman chain encoding can compress the stored data of metallographical image greatly. Compared with another compression method RLE, it has much higher compression effect.

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Magnetic Resonance Imaging Segmentation on the Basis of Boundary Tracking Algorithm in Lung Cancer Surgery

Contrast Media & Molecular Imaging ◽

10.1155/2021/1368687 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Chengmin Liu ◽

Fulin Ye ◽

Yikai Hu ◽

Shengxin Gao ◽

Yu Lu ◽

...

Keyword(s):

Magnetic Resonance Imaging ◽

Lung Cancer ◽

Treatment Group ◽

Lung Cancer Surgery ◽

Tracking Algorithm ◽

Control Group ◽

Resonance Imaging ◽

Target Image ◽

Boundary Tracking ◽

Experimental Group

This work was to study the guiding value of magnetic resonance imaging (MRI) based on the target region boundary tracking algorithm in lung cancer surgery. In this study, the traditional boundary tracking algorithm was optimized, and the target neighborhood point boundary tracking method was proposed. The iterative method was used to binarize the lung MRI image, which was applied to the MRI images of 50 lung cancer patients in hospital. The patients were divided into two groups as the progression-free survival (PFS) and overall survival (OS) of surgical treatment group (experimental group, n = 25) and nonsurgical treatment group (control group, n = 25). The experimental group received surgical resection, while the control group received systemic chemotherapy. The results showed that the traditional boundary tracking algorithm needed to manually rejudge whether the concave and convex parts of the image were missing. The target boundary tracking algorithm can effectively avoid the leakage of concave and convex parts and accurately locate the target image contour, fast operation, without manual intervention. The PFS time of the experimental group (325 days) was significantly higher than that of the control group (186 days) P < 0.05 . The OS time of the experimental group (697 days) was significantly higher than that of the control group (428 days) P < 0.05 . Fisher exact probability method was used to test the total survival time of patients in the two groups, and the tumor classification and treatment group had significant influence on the OS time P < 0.05 . The target boundary tracking algorithm in this study can effectively locate the contour of the target image, and the operation speed was fast. Surgical resection of lung cancer can improve the PFS and OS of patients.

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