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Leveling is a surveying procedure used to determine elevation differences between distant points. Elevation refers to the vertical distance above or below a reference datum, typically mean sea level (MSL). In the United States, elevations are often referenced to the mean sea level station at Father Point Rimouski along the St. Lawrence Seaway. To make the datum accessible, permanent markers are established throughout the region. These markers, called benchmarks, have known elevations. If the...
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Consider a man with a mass of 70 kg seated in a chair connected to a pin support through a member BC. If the man maintains an upright position, the task is to determine the horizontal and vertical reactions of the chair on the man when the member makes a 45° angle with the horizontal. At this moment, the man has a speed of 5 m/s, increasing at a rate of 1 m/s².
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Differential leveling is a precise method in surveying used to determine the elevation difference between two points. Its primary goal is to establish accurate vertical measurements to create level surfaces or grade lines critical for designing and constructing infrastructures such as roads, bridges, and buildings.The procedure for differential leveling begins with setting up and leveling the instrument at a point where the benchmark can be seen. The level rod is held on the benchmark (BM), and...
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A novel adaptive level set segmentation method.

Yazhong Lin1, Qian Zheng2, Jiaqiang Chen1

  • 1The 175 Hospital, Southeast Hospital of Xiamen University, Zhangzhou, Fujian 363000, China.

Computational and Mathematical Methods in Medicine
|September 26, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a novel adaptive fusing level set method for image segmentation, enhancing both accuracy and speed by intelligently combining existing techniques. The new approach overcomes limitations of prior methods, offering improved overall performance for image analysis.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Medical Imaging

Background:

  • Image segmentation is crucial for medical image analysis.
  • Existing methods like ADPLS and LBF have limitations in speed, accuracy, or sensitivity to initial contours.
  • Intensity inhomogeneity in images poses a significant challenge for segmentation accuracy.

Purpose of the Study:

  • To develop a novel adaptive fusing level set method for image segmentation.
  • To combine the strengths of the adaptive distance preserving level set (ADPLS) and local binary fitting (LBF) models.
  • To improve segmentation accuracy, speed, and stability, particularly for images with intensity inhomogeneity.

Main Methods:

  • A new adaptive fusing level set method is proposed.
  • The method adaptively adjusts the weights of ADPLS and LBF based on image spatial information.
  • The fusion strategy aims to leverage the speed of ADPLS and the accuracy of LBF.

Main Results:

  • The proposed method significantly improves comprehensive performance indicators.
  • Experimental results demonstrate enhanced accuracy compared to existing methods.
  • Improved speed and stability were observed, making it more robust for image segmentation tasks.

Conclusions:

  • The novel adaptive fusing level set method offers superior performance for image segmentation.
  • This approach effectively addresses the limitations of previous ADPLS and LBF methods.
  • The method shows great potential for applications requiring accurate and efficient image segmentation.